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Campanharo CV, Dos Santos Silveira LV, Meira DD, Casotti MC, Altoé LSC, Louro ID, Gonçalves AFM, Machado AM, Paiva BS, de Souza Inocencio E, Rocha FVV, Pesente F, de Castro GDSC, da Paixão JPDS, Bourguignon JHB, Carneiro JS, de Oliveira JR, de Souza Freire P, Zamprogno SB, Dos Santos Uchiya T, de Paula Rezende T, de Pádua Sanders Medeiros V. Pan-cancer and multiomics: advanced strategies for diagnosis, prognosis, and therapy in the complex genetic and molecular universe of cancer. Clin Transl Oncol 2024:10.1007/s12094-024-03819-4. [PMID: 39725831 DOI: 10.1007/s12094-024-03819-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 11/23/2024] [Indexed: 12/28/2024]
Abstract
The pan-cancer and multi-omics approach is motivated by the genetic and molecular complexity inherent in the varied types of cancer. This method presents itself as a crucial resource for advancing early diagnosis, defining prognoses and identifying treatments that share common bases between different forms of tumors. The aim of this article is to explore pan-cancer analysis in conjunction with multi-omics strategies, evaluating laboratory, computational, clinical procedures and their consequences, as well as examining the tumor microenvironment, epigenetics and future directions of these technologies in patient management. To this end, a literature review was conducted using PUBMED, resulting in the selection of 260 articles, of which 81 were carefully chosen to support this analysis. The pan-cancer methodology is applied to the study of this microenvironment with the aim of investigating its common characteristics through multiomics data. The development of new therapies depends on understanding the oncogenic pathways associated with different cancers. Thus, the integration of multi-omics and pan-cancer analyzes offers an innovative perspective in the search for new control points, metabolic pathways and markers, in addition to facilitating the identification of patterns common to multiple cancer types, allowing the development of targeted treatments. In this way, the convergence of multiomics and clinical approaches promotes a broad view of cancer biology, leading to more effective and personalized therapies.
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Affiliation(s)
- Camilly Victória Campanharo
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Lívia Valle Dos Santos Silveira
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Débora Dummer Meira
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil.
| | - Matheus Correia Casotti
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Lorena Souza Castro Altoé
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Iúri Drumond Louro
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - André Felipe Monteiro Gonçalves
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - André Manhães Machado
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Breno Sousa Paiva
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Ester de Souza Inocencio
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Fabio Victor Vieira Rocha
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Fellipe Pesente
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Giulia de Souza Cupertino de Castro
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - João Pedro Dos Santos da Paixão
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - José Henrique Borges Bourguignon
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Júlia Salarini Carneiro
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Juliana Ribeiro de Oliveira
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Pâmela de Souza Freire
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Sophia Bridi Zamprogno
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Taissa Dos Santos Uchiya
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Thais de Paula Rezende
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
| | - Vinícius de Pádua Sanders Medeiros
- Núcleo de Genética Humana e Molecular, Universidade Federal do Espírito Santo (UFES), Av. Fernando Ferrari, N. 514, Prédio Ciências Biológicas, Bloco A, Sala 106, Vitória, Espírito Santo, Brasil
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He QE, Zhu JX, Wang LY, Ding EC, Song K. DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method. Front Genet 2022; 13:940214. [PMID: 36338981 PMCID: PMC9626520 DOI: 10.3389/fgene.2022.940214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022] Open
Abstract
Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.
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Affiliation(s)
- Qi-en He
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Jun-xuan Zhu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Li-yan Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - En-ci Ding
- Tianjin First Central Hospital, Tianjin, China
| | - Kai Song
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
- *Correspondence: Kai Song,
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Wang C, Zhang H, Ma H, Wang Y, Cai K, Guo T, Yang Y, Li Z, Zhu Y. Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model. Front Microbiol 2022; 13:963704. [PMID: 36267181 PMCID: PMC9577021 DOI: 10.3389/fmicb.2022.963704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.
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Affiliation(s)
- Chao Wang
- Department of Surgery, Hepatic Surgery Center, Institute of Hepato-Pancreato-Biliary Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Houwang Zhang
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Haishu Ma
- School of Automation, China University of Geosciences, Wuhan, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
- Engineering Research Center of Intelligent Technology for Geo-Exploration, Wuhan, China
| | - Yawen Wang
- School of Mathematics and Physics, China University of Geosciences, Wuhan, China
| | - Ke Cai
- School of Automation, China University of Geosciences, Wuhan, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
- Engineering Research Center of Intelligent Technology for Geo-Exploration, Wuhan, China
| | - Tingrui Guo
- School of Automation, China University of Geosciences, Wuhan, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
- Engineering Research Center of Intelligent Technology for Geo-Exploration, Wuhan, China
| | - Yuanhang Yang
- School of Mathematics and Physics, China University of Geosciences, Wuhan, China
| | - Zhen Li
- School of Mathematics and Physics, China University of Geosciences, Wuhan, China
| | - Yuan Zhu
- School of Automation, China University of Geosciences, Wuhan, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
- Engineering Research Center of Intelligent Technology for Geo-Exploration, Wuhan, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Shanghai, China
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Association between cancer genes and germ layer specificity. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:218. [PMID: 36175592 DOI: 10.1007/s12032-022-01823-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/14/2022] [Indexed: 10/14/2022]
Abstract
Cancer signaling pathways defining cell fates are related to differentiation. During the developmental process, three germ layers (endoderm, mesoderm, and ectoderm) are formed during embryonic development that differentiate into organs via the epigenetic regulation of specific genes. To examine the relationship, the specificities of cancer gene mutations that depend on the germ layers are studied. The major organs affected by cancer were determined based on statistics from the National Cancer Information Center of Korea, and were grouped according to their germ layer origins. Then, the gene mutation frequencies were evaluated to identify any bias based on the differentiation group using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. The chi-square test showed that the p-value of 152 of 166 genes was less than 0.05, and 151 genes showed p-values of less than 0.05 even after adjusting for the false discovery rate (FDR). The germ layer-specific genes were evaluated using visualization based on basic statistics, and the results matched the top ranking genes depending on organs in the COSMIC database.The current study confirmed the germ layer specificity of major cancer genes. The germ layer specificity of mutated driver genes is possibly important in cancer treatments because each mutated gene may react differently depending on the germ layer of origin. By understanding the mechanism of gene mutation in the development and progression of cancer in the context of cell-fate pathways, a more effective therapeutic strategy for cancer can be established.
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Prognostic and therapeutic prediction by screening signature combinations from transcriptome-methylome interactions in oral squamous cell carcinoma. Sci Rep 2022; 12:11400. [PMID: 35794182 PMCID: PMC9259703 DOI: 10.1038/s41598-022-15534-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
DNA methylation pattern in oral squamous cell carcinoma (OSCC) remains poorly described. This study aimed to perform a genome-wide integrated analysis of the transcriptome and methylome and assess the efficacy of their prognostic signature model in patients with OSCC. We analyzed transcriptome and methylome data from 391 OSCC samples and 41 adjacent normal samples. A total of 8074 differentially expressed genes (DEGs) and 10,084 differentially expressed CpGs (DMCpGs) were identified. Then 241 DEGs with DMCpGs were identified. According to the prognostic analysis, the prognostic signature of methylation-related differentially expressed genes (mrDEGPS) was established. mrDEGPS consisted of seven prognostic methylation-related genes, including ESRRG, CCNA1, SLC20A1, COL6A6, FCGBP, CDKN2A, and ZNF43. mrDEGPS was a significant stratification factor of survival (P < 0.00001) irrespective of the clinical stage. The immune effector components, including B cells, CD4+ T cells, and CD8+ T cells, were decreased in the tumor environment of patients with high mrDEGPS. Immune checkpoint expressions, including CTLA-4, PD-1, LAG3, LGALS9, HAVCR2, and TIGHT, were comprehensively elevated (P < 0.001). The estimated half-maximal inhibitory concentration difference between low- and high-risk patients was inconsistent among chemotherapeutic drugs. In conclusion, the transcriptome–methylome interaction pattern in OSCC is complex. mrDEGPS can predict patient survival and responses to immunotherapy and chemotherapy and facilitate clinical decision-making in patients with OSCC.
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Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet 2022; 23:369-383. [PMID: 35304597 DOI: 10.1038/s41576-022-00465-w] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 12/12/2022]
Abstract
DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.
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Affiliation(s)
- Paul D Yousefi
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Matthew Suderman
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Ryan Langdon
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Oliver Whitehurst
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
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Detilleux D, Spill YG, Balaramane D, Weber M, Bardet AF. Pan-cancer predictions of transcription factors mediating aberrant DNA methylation. Epigenetics Chromatin 2022; 15:10. [PMID: 35331302 PMCID: PMC8944071 DOI: 10.1186/s13072-022-00443-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/04/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns. RESULTS Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. CONCLUSION Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.
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Affiliation(s)
- Dylane Detilleux
- UMR7242 Biotechnology and Cell Signaling, CNRS, University of Strasbourg, 67412, Illkirch, France
| | - Yannick G Spill
- UMR7242 Biotechnology and Cell Signaling, CNRS, University of Strasbourg, 67412, Illkirch, France
| | - Delphine Balaramane
- UMR7242 Biotechnology and Cell Signaling, CNRS, University of Strasbourg, 67412, Illkirch, France
| | - Michaël Weber
- UMR7242 Biotechnology and Cell Signaling, CNRS, University of Strasbourg, 67412, Illkirch, France.
| | - Anaïs Flore Bardet
- UMR7242 Biotechnology and Cell Signaling, CNRS, University of Strasbourg, 67412, Illkirch, France.
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Iqbal MA, Li M, Lin J, Zhang G, Chen M, Moazzam NF, Qian W. Preliminary Study on the Sequencing of Whole Genomic Methylation and Transcriptome-Related Genes in Thyroid Carcinoma. Cancers (Basel) 2022; 14:cancers14051163. [PMID: 35267472 PMCID: PMC8909391 DOI: 10.3390/cancers14051163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/19/2022] [Accepted: 02/22/2022] [Indexed: 01/02/2023] Open
Abstract
Simple Summary Epigenetic alterations are critical for tumor onset and development. DNA methylation is one of the most studied pathways concerning various types of cancer. A promising and exciting avenue of research is the discovery of biomarkers of early-stage malignancies for disease prevention and prognostic indicators following cancer treatment by examining the DNA methylation modification of relevant genes implicated in cancer development. We have made significant advances in the study of DNA methylation and thyroid cancer. This study is novel in that it distinguished methylation changes that occurred primarily in the gene body region of the aforementioned hypermethylated or hypomethylated thyroid cancer genes. Our findings imply that exposing whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer provides new insight into the carcinogenesis of thyroid cancer, demonstrating that gene expression mediated by DNA methylation modifications may play a significant role in tumor growth. Abstract Thyroid carcinoma is the most prevalent endocrine cancer globally and the primary cause of cancer-related mortality. Epigenetic modifications are progressively being linked to metastasis. This study aimed to examine whole-genome DNA methylation patterns and the gene expression profiles in thyroid cancer tissue samples using a MethylationEPIC BeadChip (850K), RNA sequencing, and a targeted bisulfite sequencing assay. The results of the Illumina Infinium human methylation kit (850K) analyses identified differentially methylated CpG locations (DMPs) and differentially methylated CpG regions (DMRs) encompassing nearly the entire genome with high resolution and depth. Gene ontology and KEGG pathway analyses revealed that the genes associated with DMRs belonged to various domain-specific ontologies, including cell adhesion, molecule binding, and proliferation. The RNA-Seq study found 1627 differentially expressed genes, 1174 of which that were up-regulated and 453 of which that were down-regulated. The targeted bisulfite sequencing assay revealed that CHST2, DPP4, DUSP6, ITGA2, SLC1A5, TIAM1, TNIK, and ABTB2 methylation levels were dramatically lowered in thyroid cancer patients when compared to the controls, but GALNTL6, HTR7, SPOCD1, and GRM5 methylation levels were significantly raised. Our study revealed that the whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer shed new light on the tumorigenesis of thyroid cancer.
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Affiliation(s)
- Muhammad Asad Iqbal
- Department of Otolaryngology-Head & Neck Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212002, China;
| | - Mingyang Li
- Department of Basic Medical Sciences, Affiliated to School of Medicine, Jiangsu University, Zhenjiang 212002, China;
| | - Jiang Lin
- Laboratory Center, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212132, China;
| | - Guoliang Zhang
- Department of General Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212132, China;
| | - Miao Chen
- Department of Pathology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212132, China;
| | | | - Wei Qian
- Department of Otolaryngology-Head & Neck Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang 212002, China;
- Correspondence: ; Tel.: +86-0511-88917833 or +86-1535-8586188
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Tierling S, Jürgens-Wemheuer WM, Leismann A, Becker-Kettern J, Scherer M, Wrede A, Breuskin D, Urbschat S, Sippl C, Oertel J, Schulz-Schaeffer WJ, Walter J. Bisulfite profiling of the MGMT promoter and comparison with routine testing in glioblastoma diagnostics. Clin Epigenetics 2022; 14:26. [PMID: 35180887 PMCID: PMC8857788 DOI: 10.1186/s13148-022-01244-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 02/07/2022] [Indexed: 11/26/2022] Open
Abstract
Background Promoter methylation of the DNA repair gene O6-methylguanine-DNA methyltransferase (MGMT) is an acknowledged predictive epigenetic marker in glioblastoma multiforme and anaplastic astrocytoma. Patients with methylated CpGs in the MGMT promoter benefit from treatment with alkylating agents, such as temozolomide, and show an improved overall survival and progression-free interval. A precise determination of MGMT promoter methylation is of importance for diagnostic decisions. We experienced that different methods show partially divergent results in a daily routine. For an integrated neuropathological diagnosis of malignant gliomas, we therefore currently apply a combination of methylation-specific PCR assays and pyrosequencing. Results To better rationalize the variation across assays, we compared these standard techniques and assays to deep bisulfite sequencing results in a cohort of 80 malignant astrocytomas. Our deep analysis covers 49 CpG sites of the expanded MGMT promoter, including exon 1, parts of intron 1 and a region upstream of the transcription start site (TSS). We observed that deep sequencing data are in general in agreement with CpG-specific pyrosequencing, while the most widely used MSP assays published by Esteller et al. (N Engl J Med 343(19):1350–1354, 2000. 10.1056/NEJM200011093431901) and Felsberg et al. (Clin Cancer Res 15(21):6683–6693, 2009. 10.1158/1078-0432.CCR-08-2801) resulted in partially discordant results in 22 tumors (27.5%). Local deep bisulfite sequencing (LDBS) revealed that CpGs located in exon 1 are suited best to discriminate methylated from unmethylated samples. Based on LDBS data, we propose an optimized MSP primer pair with 83% and 85% concordance to pyrosequencing and LDBS data. A hitherto neglected region upstream of the TSS, with an overall higher methylation compared to exon 1 and intron 1 of MGMT, is also able to discriminate the methylation status. Conclusion Our integrated analysis allows to evaluate and redefine co-methylation domains within the MGMT promoter and to rationalize the practical impact on assays used in daily routine diagnostics. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01244-4.
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Affiliation(s)
- Sascha Tierling
- Fak.NT Life Sciences, Department of Genetics/Epigenetics, Saarland University, Campus, Building A2 4, 66041, Saarbrücken, Germany.
| | | | - Alea Leismann
- Fak.NT Life Sciences, Department of Genetics/Epigenetics, Saarland University, Campus, Building A2 4, 66041, Saarbrücken, Germany
| | - Julia Becker-Kettern
- Institute of Neuropathology, Medical Faculty of the Saarland University, Homburg, Germany
| | - Michael Scherer
- Fak.NT Life Sciences, Department of Genetics/Epigenetics, Saarland University, Campus, Building A2 4, 66041, Saarbrücken, Germany.,Department of Bioinformatics and Genomics, Centre for Genomic Regulation, Barcelona, Spain
| | - Arne Wrede
- Institute of Neuropathology, Medical Faculty of the Saarland University, Homburg, Germany
| | - David Breuskin
- Institute for Neurosurgery, Medical Faculty of the Saarland University, Homburg, Germany
| | - Steffi Urbschat
- Institute for Neurosurgery, Medical Faculty of the Saarland University, Homburg, Germany
| | - Christoph Sippl
- Institute for Neurosurgery, Medical Faculty of the Saarland University, Homburg, Germany
| | - Joachim Oertel
- Institute for Neurosurgery, Medical Faculty of the Saarland University, Homburg, Germany
| | | | - Jörn Walter
- Fak.NT Life Sciences, Department of Genetics/Epigenetics, Saarland University, Campus, Building A2 4, 66041, Saarbrücken, Germany
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10
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Liu P. Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics. Front Genet 2022; 12:798748. [PMID: 35069697 PMCID: PMC8770539 DOI: 10.3389/fgene.2021.798748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022] Open
Abstract
The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.
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Affiliation(s)
- Pengfei Liu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center For Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
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11
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Wang JY, Lao J, Luo Y, Guo JJ, Cheng H, Zhang HY, Yao J, Ma XP, Wang B. Integrative Analysis of DNA Methylation and Gene Expression Profiling Data Reveals Candidate Methylation-Regulated Genes in Hepatoblastoma. Int J Gen Med 2021; 14:9419-9431. [PMID: 34908869 PMCID: PMC8664605 DOI: 10.2147/ijgm.s331178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/09/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose This study aimed to identify novel methylation-regulated genes as diagnostic biomarkers and therapeutic targets for hepatoblastoma (HB). Materials and Methods The DNA methylation data of 19 HB tumor samples and 10 normal liver samples from the GSE78732 dataset and gene expression profiling data of 53 HB tumor samples and 14 normal liver samples from the GSE131329 dataset and 31 HB tumor samples and 32 normal liver samples from the GSE133039 dataset were downloaded form the Gene Expression Omnibus database. Next, differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified. Venn diagrams were used to identify methylation-regulated genes. The VarElect online tool was selected to identify key methylation-regulated genes, and a protein–protein interaction (PPI) network was constructed to show the interactions among key methylation-regulated genes and DEGs. Finally, Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to investigate the potential regulatory mechanisms of key methylation-regulated genes. Results A total of 457 DMGs and 1597 DEGs were identified between the HB and normal liver samples. After DMGs and DEGs overlapping, 22 hypomethylated and upregulated genes and 19 hypermethylated and downregulated genes in HB were screened. Survival analysis revealed that 13 methylation-regulated genes were associated with the prognosis of liver cancer. Moreover, SPP1, UHRF1, and HEY1 were selected as the key DNA methylation-regulated genes. The PPI network revealed that all of them could affect TP53, while both UHRF1 and HEY1 could influence BMP4. Enrichment analysis suggested that the DEGs were involved in TP53-related pathways, including the cell cycle and p53 signaling pathway. Finally, SPP1, UHRF1, and HEY1 were hypomethylated and upregulated in the HB samples compared with those in the normal liver samples. Conclusion SPP1, UHRE1, and HEY1 may play important roles in HB and be used as biomarkers for its diagnosis and treatment.
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Affiliation(s)
- Jian-Yao Wang
- Department of General Surgery, Shenzhen Children's Hospital, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Jing Lao
- Shenzhen Children's Hospital of China Medical University, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Yu Luo
- Zhuhai Campus of Zunyi Medical University, Zhuhai, 519090, Guangdong Province, People's Republic of China
| | - Jing-Jie Guo
- Shenzhen Children's Hospital of China Medical University, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Hao Cheng
- Shenzhen Children's Hospital of China Medical University, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Hong-Yan Zhang
- Shenzhen Children's Hospital of China Medical University, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Jun Yao
- Department of Gastroenterology, Jinan University of Medical Sciences, Shenzhen Municipal People's Hospital, Shenzhen, 518020, Guangdong Province, People's Republic of China
| | - Xiao-Peng Ma
- Department of General Surgery, Shenzhen Children's Hospital, Shenzhen, 518026, Guangdong Province, People's Republic of China
| | - Bin Wang
- Department of General Surgery, Shenzhen Children's Hospital, Shenzhen, 518026, Guangdong Province, People's Republic of China
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12
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Qiu BQ, Lin XH, Lai SQ, Lu F, Lin K, Long X, Zhu SQ, Zou HX, Xu JJ, Liu JC, Wu YB. ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation. Cancer Cell Int 2021; 21:665. [PMID: 34906142 PMCID: PMC8670189 DOI: 10.1186/s12935-021-02380-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 11/30/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. METHODS Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. RESULTS We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. CONCLUSION Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.
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Affiliation(s)
- Bai-Quan Qiu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xia-Hui Lin
- Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai, China
| | - Song-Qing Lai
- Institute of Cardiovascular Disease, Jiangxi Academy of Clinical Medical Sciences, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Feng Lu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Kun Lin
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiang Long
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Shu-Qiang Zhu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Hua-Xi Zou
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jian-Jun Xu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Ji-Chun Liu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
| | - Yong-Bing Wu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
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13
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Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer. BMC Genomics 2021; 22:791. [PMID: 34732125 PMCID: PMC8567715 DOI: 10.1186/s12864-021-08097-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
Background The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC. Methods The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation. Results A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively. Conclusions In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-08097-w.
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14
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Kerachian MA, Azghandi M, Mozaffari-Jovin S, Thierry AR. Guidelines for pre-analytical conditions for assessing the methylation of circulating cell-free DNA. Clin Epigenetics 2021; 13:193. [PMID: 34663458 PMCID: PMC8525023 DOI: 10.1186/s13148-021-01182-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/04/2021] [Indexed: 02/06/2023] Open
Abstract
Methylation analysis of circulating cell-free DNA (cirDNA), as a liquid biopsy, has a significant potential to advance the detection, prognosis, and treatment of cancer, as well as many genetic disorders. The role of epigenetics in disease development has been reported in several hereditary disorders, and epigenetic modifications are regarded as one of the earliest and most significant genomic aberrations that arise during carcinogenesis. Liquid biopsy can be employed for the detection of these epigenetic biomarkers. It consists of isolation (pre-analytical) and detection (analytical) phases. The choice of pre-analytical variables comprising cirDNA extraction and bisulfite conversion methods can affect the identification of cirDNA methylation. Indeed, different techniques give a different return of cirDNA, which confirms the importance of pre-analytical procedures in clinical diagnostics. Although novel techniques have been developed for the simplification of methylation analysis, the process remains complex, as the steps of DNA extraction, bisulfite treatment, and methylation detection are each carried out separately. Recent studies have noted the absence of any standard method for the pre-analytical processing of methylated cirDNA. We have therefore conducted a comprehensive and systematic review of the important pre-analytical and analytical variables and the patient-related factors which form the basis of our guidelines for analyzing methylated cirDNA in liquid biopsy.
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Affiliation(s)
- Mohammad Amin Kerachian
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran.
| | - Marjan Azghandi
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran
- Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Sina Mozaffari-Jovin
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alain R Thierry
- IRCM, Institute of Research in Oncology of Montpellier, Montpellier, France.
- INSERM, U1194, Montpellier, France.
- University of Montpellier, Montpellier, France.
- ICM, Regional Institute of Cancer of Montpellier, Montpellier, France.
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15
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Zhao X, Ji J, Wang S, Wang R, Yu Q, Li D. The regulatory pattern of target gene expression by aberrant enhancer methylation in glioblastoma. BMC Bioinformatics 2021; 22:420. [PMID: 34482818 PMCID: PMC8420065 DOI: 10.1186/s12859-021-04345-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/23/2021] [Indexed: 12/21/2022] Open
Abstract
Background Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor with grim prognosis. Aberrant DNA methylation is an epigenetic mechanism that promotes GBM carcinogenesis, while the function of DNA methylation at enhancer regions in GBM remains poorly described. Results We integrated multi-omics data to identify differential methylation enhancer region (DMER)-genes and revealed global enhancer hypomethylation in GBM. In addition, a DMER-mediated target genes regulatory network and functional enrichment analysis of target genes that might be regulated by hypomethylation enhancer regions showed that aberrant enhancer regions could contribute to tumorigenesis and progression in GBM. Further, we identified 22 modules in which lncRNAs and mRNAs synergistically competed with each other. Finally, through the construction of drug-target association networks, our study identified potential small-molecule drugs for GBM treatment. Conclusions Our study provides novel insights for understanding the regulation of aberrant enhancer region methylation and developing methylation-based biomarkers for the diagnosis and treatment of GBM. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04345-8.
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Affiliation(s)
- Xiaoxiao Zhao
- School of Biomedical Engineering, Capital Medical University, 10 You An Men Wai, Xi Tou Tiao, Beijing, 100069, People's Republic of China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Jianghuai Ji
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, 310022, People's Republic of China.,Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, 310022, People's Republic of China
| | - Shijia Wang
- School of Biomedical Engineering, Capital Medical University, 10 You An Men Wai, Xi Tou Tiao, Beijing, 100069, People's Republic of China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Rendong Wang
- School of Biomedical Engineering, Capital Medical University, 10 You An Men Wai, Xi Tou Tiao, Beijing, 100069, People's Republic of China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Qiuhong Yu
- Department of Hyperbaric Oxygen, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan Xi Lu, Fengtai District, Beijing, 100070, People's Republic of China.
| | - Dongguo Li
- School of Biomedical Engineering, Capital Medical University, 10 You An Men Wai, Xi Tou Tiao, Beijing, 100069, People's Republic of China. .,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical, Capital Medical University, Beijing, 100069, People's Republic of China.
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16
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Yu X, Han Y, Liu S, Jiang W, Song Y, Tong J, Qiao T, Lv Z, Li D. Analysis of Genetic Alterations Related to DNA Methylation in Testicular Germ Cell Tumors Based on Data Mining. Cytogenet Genome Res 2021; 161:382-394. [PMID: 34433169 DOI: 10.1159/000516385] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 08/27/2020] [Indexed: 11/19/2022] Open
Abstract
Embryonal carcinoma (EC) and seminoma (SE) are both derived from germ cell neoplasia in situ but show big differences in growth patterns and clinical prognosis. Epigenetic regulation may play an important role in the development of EC and SE. This study investigated the DNA methylation-based genetic alterations between EC and SE by analyzing the datasets of mRNA expression and DNA methylation profiling. The datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified between EC and SE by limma package in R environment. Gene function enrichment analysis of the DEGs was performed on the DAVID tool, the results of which suggested differences in capability of pluripotency and genomic stability between EC and SE. The minfi package and wANNOVAR tool were used to identify differentially methylated genes. A total of 37 genes were discovered with both mRNA expression and the accordant DNA methylation changes. The findings were verified by the sequencing data from The Cancer Genome Atlas database, and Kaplan-Meier survival analysis was performed. Finally, 5 genes (PRDM1, LMO2, FAM53B, HCN4, and FAM124B) were found that showed both low expression and high methylation in EC, and were significantly associated with relapse-free survival. The findings of methylation-based genetic features between EC and SE might be helpful in studying the role of DNA methylation in cancer development.
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Affiliation(s)
- Xiaqing Yu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yali Han
- Shanghai Center of Thyroid Diseases, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Simin Liu
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wen Jiang
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yingchun Song
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Junyu Tong
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tingting Qiao
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhongwei Lv
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.,Imaging Clinical Medical Center, Tongji University School of Medicine, Shanghai, China.,Clinical Nuclear Medicine Center, Tongji University School of Medicine, Shanghai, China
| | - Dan Li
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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17
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Ling S, He Y, Li X, Ma Y, Li Y, Kong B, Huang P. Significant Gene Biomarker Tyrosine Kinase Non-receptor 2 Mediated Cell Proliferation and Invasion in Colon Cancer. Front Genet 2021; 12:653657. [PMID: 34421982 PMCID: PMC8371684 DOI: 10.3389/fgene.2021.653657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 06/21/2021] [Indexed: 01/22/2023] Open
Abstract
Objective: This study aimed to investigate the expression and biological functions of TNK2 and miR-125a-3p in colon cancer. Materials and methods: The expression of TNK2 and miR-125a-3p in colon cancer tissues was analyzed using data deposited on public databases including UALCAN and ONCOMINE. We verified their expression in colon cancer cell lines by RT-qPCR and western blotting. By regulating the expression of TNK2 and miR-125a-3p in colon cancer cells, their functions and potential mechanisms were explored. Results:TNK2 was overexpressed in colon cancer cell lines, and it was found to directly bind to miR-125a-3p, which was downregulated in these cell lines. Their expression affected the proliferation and invasion of colon cancer cells. Additionally, colon cancer patients with lower TNK2 expression had better prognoses than those with higher TNK2 expression. Conclusion: Our results indicated that TNK2 and miR-125a-3p play critical roles in colon cancer, and could also serve as biomarkers for the diagnosis and prognosis of this malignant disease.
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Affiliation(s)
- Sunkai Ling
- Medical School of Southeast University, Nanjing, China
| | - Yanru He
- Department of Cardiology, Zhongda Hospital Affiliated to Southeast University, Nanjing, China
| | - Xiaoxue Li
- Medical School of Southeast University, Nanjing, China
| | - Yu Ma
- Medical School of Southeast University, Nanjing, China
| | - Yuan Li
- Medical School of Southeast University, Nanjing, China
| | - Bo Kong
- Department of Surgery, Klinikum rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany.,Department of General Surgery, University of Ulm, Ulm, Germany
| | - Peilin Huang
- Medical School of Southeast University, Nanjing, China
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18
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Duan R, Gao L, Gao Y, Hu Y, Xu H, Huang M, Song K, Wang H, Dong Y, Jiang C, Zhang C, Jia S. Evaluation and comparison of multi-omics data integration methods for cancer subtyping. PLoS Comput Biol 2021; 17:e1009224. [PMID: 34383739 PMCID: PMC8384175 DOI: 10.1371/journal.pcbi.1009224] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 08/24/2021] [Accepted: 06/28/2021] [Indexed: 11/18/2022] Open
Abstract
Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis. Cancer is one of the most heterogeneous diseases, characterized by diverse morphological, phenotypic, and genomic profiles between tumors and their subtypes. Identifying cancer subtypes can help patients receive precise treatments. With the development of high-throughput technologies, genomics, epigenomics, and transcriptomics data have been generated for large cancer patient cohorts. It is believed that the more omics data we use, the more accurate identification of cancer subtypes. To examine this assumption, we first constructed three classes of benchmarking datasets to conduct a comprehensive evaluation and comparison of ten representative multi-omics data integration methods for cancer subtyping by considering their accuracy, robustness, and computational efficiency. Then, we investigated the influence of different omics data and their various combinations on the effectiveness of cancer subtyping. Our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. We hope that our work may help researchers choose a proper method and an effective data combination when identifying cancer subtypes using data integration methods.
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Affiliation(s)
- Ran Duan
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an, China
- * E-mail:
| | - Yong Gao
- Department of Computer Science, The University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Han Xu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Mingfeng Huang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Kuo Song
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Hongda Wang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Yongqiang Dong
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Chaoqun Jiang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Chenxing Zhang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Songwei Jia
- School of Computer Science and Technology, Xidian University, Xi’an, China
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19
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Tang Y, Qing C, Wang J, Zeng Z. DNA Methylation-based Diagnostic and Prognostic Biomarkers for Glioblastoma. Cell Transplant 2021; 29:963689720933241. [PMID: 32510239 PMCID: PMC7563836 DOI: 10.1177/0963689720933241] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Glioblastomas are the most common primary central nervous system malignancy tumor in adults. Glioblastoma patients have poor prognosis, with an average survival period of approximately 14 mo after diagnosis. To date, there are a limited number of effective treatment methods for glioblastoma, and its molecular mechanisms remain elusive. In this article, we analyzed the key biomarkers and pathways in glioblastoma patients based on gene expression and DNA methylation datasets. The 60 hypomethylated/upregulated genes and 110 hypermethylated/downregulated genes were identified in GSE50923, GSE50161, and GSE116520 microarrays. Functional enrichment analyses indicated that these methylated-differentially expressed genes were primarily involved in collagen fibril organization, chemical synaptic transmission, extracellular matrix-receptor interaction, and GABAergic synapse. The hub genes were screened from a protein–protein interaction network; in selected genes, increased NMB mRNA level was associated with favorable overall survival, while elevated CHI3L1, POSTN, S100A4, LOX, S100A11, IGFBP2, SLC12A5, VSNL1, and RGS4 mRNA levels were associated with poor overall survival in glioblastoma patients. Additionally, CHI3L1, S100A4, LOX, and S100A11 expressions were negatively correlated with their corresponding methylation status. Furthermore, the receiver-operator characteristic curve analysis indicated that CHI3L1, S100A4, LOX, and S100A11 can also serve as highly specific and sensitive diagnostic biomarkers for glioblastoma patients. Collectively, our study revealed the possible methylated-differentially expressed genes and associated pathways in glioblastoma and identified four DNA methylation-based biomarkers of glioblastoma. These results may provide insight on diagnostic and prognostic biomarkers, and therapeutic targets in glioblastoma.
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Affiliation(s)
- Yunliang Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China.,Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Cheng Qing
- Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Jiao Wang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Zhenguo Zeng
- Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China
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20
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TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers. PLoS Comput Biol 2021; 17:e1009044. [PMID: 34061840 PMCID: PMC8195367 DOI: 10.1371/journal.pcbi.1009044] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 06/11/2021] [Accepted: 05/05/2021] [Indexed: 12/22/2022] Open
Abstract
Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer. MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.
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21
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Xu Q, Hu Y, Chen S, Zhu Y, Li S, Shen F, Guo Y, Sun T, Chen X, Jiang J, Huang W. Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma. Front Mol Biosci 2021; 8:683240. [PMID: 34124163 PMCID: PMC8187884 DOI: 10.3389/fmolb.2021.683240] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer. Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB). Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status. Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.
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Affiliation(s)
- Qianhui Xu
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanbo Hu
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shaohuai Chen
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yulun Zhu
- Zhejiang University School of Medicine, Hangzhou, China
| | - Siwei Li
- Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Shen
- Zhejiang University School of Medicine, Hangzhou, China
| | - Yifan Guo
- Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Sun
- Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyu Chen
- Zhejiang University School of Medicine, Hangzhou, China
| | - Jinpeng Jiang
- Zhejiang University School of Medicine, Hangzhou, China
| | - Wen Huang
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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22
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Common DNA methylation dynamics in endometriod adenocarcinoma and glioblastoma suggest universal epigenomic alterations in tumorigenesis. Commun Biol 2021; 4:607. [PMID: 34021236 PMCID: PMC8140130 DOI: 10.1038/s42003-021-02094-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/10/2021] [Indexed: 02/04/2023] Open
Abstract
Trends in altered DNA methylation have been defined across human cancers, revealing global loss of methylation (hypomethylation) and focal gain of methylation (hypermethylation) as frequent cancer hallmarks. Although many cancers share these trends, little is known about the specific differences in DNA methylation changes across cancer types, particularly outside of promoters. Here, we present a comprehensive comparison of DNA methylation changes between two distinct cancers, endometrioid adenocarcinoma (EAC) and glioblastoma multiforme (GBM), to elucidate common rules of methylation dysregulation and changes unique to cancers derived from specific cells. Both cancers exhibit significant changes in methylation over regulatory elements. Notably, hypermethylated enhancers within EAC samples contain several transcription factor binding site clusters with enriched disease ontology terms highlighting uterine function, while hypermethylated enhancers in GBM are found to overlap active enhancer marks in adult brain. These findings suggest that loss of original cellular identity may be a shared step in tumorigenesis.
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23
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Moderate DNA hypomethylation suppresses intestinal tumorigenesis by promoting caspase-3 expression and apoptosis. Oncogenesis 2021; 10:38. [PMID: 33947834 PMCID: PMC8096944 DOI: 10.1038/s41389-021-00328-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/18/2021] [Accepted: 04/09/2021] [Indexed: 12/14/2022] Open
Abstract
Global DNA hypomethylation is a most common epigenetic alteration in human neoplasia. However, accumulative evidence shows that global DNA hypomethylation impacts tumorigenesis in a tissue-specific manner, promoting tumorigenesis in some but suppressing tumorigenesis in others including colorectal cancer. The underlying mechanisms, especially how DNA hypomethylation suppresses tumorigenesis, remain largely unknown. Here, we investigate how DNA hypomethylation affects intestinal tumorigenesis by using an Uhrf1 tandem tudor domain knockin mutant mouse model (Uhrf1ki/ki) that exhibits a moderate ~10% reduction of global DNA methylation. We found that both chemical-induced colorectal carcinogenesis and Apc loss of heterozygosity (LOH)-induced intestinal tumorigenesis are substantially suppressed in the Uhrf1 mutant mice. Furthermore, unlike Dnmt1 hypomorphic mice in which DNA hypomethylation suppresses the incidence of macroscopic intestinal tumors but promotes the formation of microadenoma in ApcMin/+ background, Uhrf1ki/ki/ApcMin/+ mice have markedly reduced incidence of both microadenoma and macroadenoma. DNA hypomethylation does not appear to affect Apc LOH, activation of the Wnt or Hippo pathway, or tumor cell proliferation, but acts cooperatively with activated Wnt pathway to enhance the caspase-3 gene expression, activation, and apoptosis. Furthermore, increased caspase-3 expression correlates with DNA hypomethylation within the caspase-3 enhancer regions. Taken together, we present a new mouse model for investigating the role of and the molecular mechanisms by which DNA hypomethylation suppresses intestinal tumorigenesis. Our finding that a moderate DNA hypomethylation is sufficient to suppress intestinal tumorigenesis by promoting caspase-3 expression and apoptosis sheds new light on DNA-methylation inhibitor-based colorectal cancer therapeutics.
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24
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Saliva Gene Promoter Hypermethylation as a Biomarker in Oral Cancer. J Clin Med 2021; 10:jcm10091931. [PMID: 33947071 PMCID: PMC8124791 DOI: 10.3390/jcm10091931] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/07/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
Oral carcinogenesis is a multistep process characterized by a summation of multiple genetic and epigenetic alterations in key regulatory genes. The silencing of genes by aberrant promoter hypermethylation is thought to be an important epigenetic event in cancer development and progression which has great potential as a biomarker for early diagnosis, tumor molecular subtyping, prognosis, monitoring, and therapy. Aberrant DNA methylation has been detected in different liquid biopsies, which may represent a potential alternative to solid biopsies. The detection of methylated genes in saliva may have clinical application for noninvasive oral cancer screening and early diagnosis. Here, we review the current evidence on gene promoter hypermethylation in saliva.
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25
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Chen Y, Verbeek FJ, Wolstencroft K. Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations. BMC Bioinformatics 2021; 22:178. [PMID: 33823788 PMCID: PMC8025515 DOI: 10.1186/s12859-021-04105-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/22/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. RESULTS Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. CONCLUSIONS Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.
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Affiliation(s)
- Yi Chen
- The Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, Leiden, The Netherlands
| | - Fons. J. Verbeek
- The Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, Leiden, The Netherlands
| | - Katherine Wolstencroft
- The Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, Leiden, The Netherlands
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26
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Liu G, Liu Z, Sun X, Xia X, Liu Y, Liu L. Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers. Front Cell Dev Biol 2021; 9:649168. [PMID: 33816499 PMCID: PMC8012915 DOI: 10.3389/fcell.2021.649168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 02/01/2021] [Indexed: 01/22/2023] Open
Abstract
DNA methylation dysregulation during carcinogenesis has been widely discussed in recent years. However, the pan-cancer DNA methylation biomarkers and corresponding biological mechanisms were seldom investigated. We identified differentially methylated sites and regions from 5,056 The Cancer Genome Atlas (TCGA) samples across 10 cancer types and then validated the findings using 48 manually annotated datasets consisting of 3,394 samples across nine cancer types from Gene Expression Omnibus (GEO). All samples’ DNA methylation profile was evaluated with Illumina 450K microarray to narrow down the batch effect. Nine regions were identified as commonly differentially methylated regions across cancers in TCGA and GEO cohorts. Among these regions, a DNA fragment consisting of ∼1,400 bp detected inside the HOXA locus instead of the boundary may relate to the co-expression attenuation of genes inside the locus during carcinogenesis. We further analyzed the 3D DNA interaction profile by the publicly accessible Hi-C database. Consistently, the HOXA locus in normal cell lines compromised isolated topological domains while merging to the domain nearby in cancer cell lines. In conclusion, the dysregulation of the HOXA locus provides a novel insight into pan-cancer carcinogenesis.
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Affiliation(s)
- Gang Liu
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Zhenhao Liu
- Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Key Laboratory of Carcinogenesis, National Health and Family Planning Commission, Xiangya Hospital, Central South University, Changsha, China.,Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Xiaomeng Sun
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xiaoqiong Xia
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunhe Liu
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
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27
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Rahman MM, Tollefsbol TO. Targeting cancer epigenetics with CRISPR-dCAS9: Principles and prospects. Methods 2021; 187:77-91. [PMID: 32315755 PMCID: PMC7572534 DOI: 10.1016/j.ymeth.2020.04.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/15/2020] [Accepted: 04/15/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer therapeutics is an ever-evolving field due to incessant demands for effective and precise treatment options. Over the last few decades, cancer treatment strategies have shifted somewhat from surgery to targeted precision medicine. CRISPR-dCas9 is an emerging version of precision cancer therapy that has been adapted from the prokaryotic CRISPR-Cas system. Once ligated to epigenetic effectors (EE), CRISPR-dCas9 can function as an epigenetic editing tool and CRISPR-dCas9-EE complexes could be exploited to alter cancerous epigenetic features associated with different cancer hallmarks. In this article, we discuss the rationale of epigenetic editing as a therapeutic strategy against cancer. We also outline how sgRNA-dCas9 was derived from the CRISPR-Cas system. In addition, the current status of sgRNA-dCas9 use (in vivo and in vitro) in cancer is updated with a molecular illustration of CRISPR-dCas9-mediated epigenetic and transcriptional modulation. As sgRNA-dCas9 is still at the developmental phase, challenges are inherent to its use. We evaluate major challenges in targeting cancer with sgRNA-dCas9 such as off-target effects, lack of sgRNA designing rubrics, target site selection dilemmas and deficient sgRNA-dCas9 delivery systems. Finally, we appraise the sgRNA-dCas9 as a prospective cancer therapeutic by summarizing ongoing improvements of sgRNA-dCas9 methodology.
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Affiliation(s)
- Mohammad Mijanur Rahman
- Department of Biology, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA.
| | - Trygve O Tollefsbol
- Department of Biology, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA; Comprehensive Center for Healthy Aging, University of Alabama Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294, USA; Comprehensive Cancer Center, University of Alabama Birmingham, 1802 6th Avenue South, Birmingham, AL 35294, USA; Nutrition Obesity Research Center, University of Alabama Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA; Comprehensive Diabetes Center, University of Alabama Birmingham, 1825 University Boulevard, Birmingham, AL 35294, USA.
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28
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Liu X, Wang P, Teng X, Zhang Z, Song S. Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers. Front Oncol 2021; 11:624395. [PMID: 33718187 PMCID: PMC7946859 DOI: 10.3389/fonc.2021.624395] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/06/2021] [Indexed: 12/26/2022] Open
Abstract
Background N6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely "writers," "erasers," and "readers." Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers. Results Here we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients. Conclusions Our findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators' expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.
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Affiliation(s)
- Xiaonan Liu
- National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Pei Wang
- National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xufei Teng
- National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
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29
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Zhang X, Lai H, Zhang F, Wang Y, Zhang L, Yang N, Wang C, Liang Z, Zeng J, Yang J. Visualization and Analysis in the Field of Pan-Cancer Studies and Its Application in Breast Cancer Treatment. Front Med (Lausanne) 2021; 8:635035. [PMID: 33681260 PMCID: PMC7926202 DOI: 10.3389/fmed.2021.635035] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 01/26/2021] [Indexed: 12/21/2022] Open
Abstract
Although all cancers are molecularly distinct, many share common driver mutations. Pan-cancer analysis, utilizes next-generation sequencing (NGS), pan-cancer model systems, and pan-cancer projects such as The Cancer Genome Atlas (TCGA), to assess frequently mutated genes and other genomic abnormalities that are common among many cancer types, regardless of the tumor origin, providing new directions for tumor biology research. However, there is currently no study that has objectively analyzed the results of pan-cancer studies on cancer biology. For this study, 999 articles on pan-cancer published from 2006 to 2020 were obtained from the Scopus database, and bibliometric methods were used to analyze citations, international cooperation, co-authorship and keyword co-occurrence clusters. Furthermore, we also focused on and summarized the application of pan-cancer in breast cancer. Our result shows that the pan-cancer studies were first published in 2006 and entered a period of rapid development after 2013. So far, 86 countries have carried out international cooperation in sharing research. Researchers form the United States and Canada have published the most articles and have made the most extensive contribution to this field, respectively. Through author keyword analysis of the 999 articles, TCGA, biomarkers, NGS, immunotherapy, DNA methylation, prognosis, and several other keywords appear frequently, and these terms are hot spots in pan-cancer studies. There are four subtypes of breast cancer (luminalA, luminalB, HER2, and basal-like) according to pan-cancer analysis of breast cancer. Meanwhile, it was found that breast cancer has genetic similarity to pan-gynecological cancers, such as ovarian cancer, which indicates related etiology and possibly similar treatments. Collectively, with the emergence of new detection methods, new cancer databases, and the involvement of more researchers, pan-cancer analyses will play a greater role in cancer biology research.
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Affiliation(s)
- Xianwen Zhang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Han Lai
- School of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fan Zhang
- Department of General Surgery, The 5th Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yixi Wang
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Li Zhang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ni Yang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chunrong Wang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zheng Liang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jieping Zeng
- Department of Ophthalmology, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (Sichuan Provincial Hospital of Traditional Chinese Medicine), Chengdu, China
| | - Jinrong Yang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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30
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Xie J, Yin Y, Yang F, Sun J, Wang J. Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision. Interdiscip Sci 2021; 13:91-102. [PMID: 33439459 DOI: 10.1007/s12539-020-00415-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 11/30/2022]
Abstract
Deciphering regulatory patterns of neural stem cell (NSC) differentiation with multiple stages is essential to understand NSC differentiation mechanisms. Recent single-cell transcriptome datasets became available at individual differentiation. However, a systematic and integrative analysis of multiple datasets at multiple temporal stages of NSC differentiation is lacking. In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is essential to all differentiation paths, but it plays a different role in each path when regulating other genes. Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths.
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Affiliation(s)
- Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yiting Yin
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fuzhang Yang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiamin Sun
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiao Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
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31
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Sazonova MA, Sinyov VV, Ryzhkova AI, Sazonova MD, Kirichenko TV, Khotina VA, Khasanova ZB, Doroschuk NA, Karagodin VP, Orekhov AN, Sobenin IA. Some Molecular and Cellular Stress Mechanisms Associated with Neurodegenerative Diseases and Atherosclerosis. Int J Mol Sci 2021; 22:E699. [PMID: 33445687 PMCID: PMC7828120 DOI: 10.3390/ijms22020699] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 12/29/2020] [Accepted: 01/04/2021] [Indexed: 12/14/2022] Open
Abstract
Chronic stress is a combination of nonspecific adaptive reactions of the body to the influence of various adverse stress factors which disrupt its homeostasis, and it is also a corresponding state of the organism's nervous system (or the body in general). We hypothesized that chronic stress may be one of the causes occurence of several molecular and cellular types of stress. We analyzed literary sources and considered most of these types of stress in our review article. We examined genes and mutations of nuclear and mitochondrial genomes and also molecular variants which lead to various types of stress. The end result of chronic stress can be metabolic disturbance in humans and animals, leading to accumulation of reactive oxygen species (ROS), oxidative stress, energy deficiency in cells (due to a decrease in ATP synthesis) and mitochondrial dysfunction. These changes can last for the lifetime and lead to severe pathologies, including neurodegenerative diseases and atherosclerosis. The analysis of literature allowed us to conclude that under the influence of chronic stress, metabolism in the human body can be disrupted, mutations of the mitochondrial and nuclear genome and dysfunction of cells and their compartments can occur. As a result of these processes, oxidative, genotoxic, and cellular stress can occur. Therefore, chronic stress can be one of the causes forthe occurrence and development of neurodegenerative diseases and atherosclerosis. In particular, chronic stress can play a large role in the occurrence and development of oxidative, genotoxic, and cellular types of stress.
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Affiliation(s)
- Margarita A. Sazonova
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
| | - Vasily V. Sinyov
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
| | - Anastasia I. Ryzhkova
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
| | - Marina D. Sazonova
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
| | - Tatiana V. Kirichenko
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Research Institute of Human Morphology, 117418 Moscow, Russia
| | - Victoria A. Khotina
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Research Institute of Human Morphology, 117418 Moscow, Russia
| | - Zukhra B. Khasanova
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
| | - Natalya A. Doroschuk
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
| | - Vasily P. Karagodin
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Department of Commodity Science and Expertise, Plekhanov Russian University of Economics, 125993 Moscow, Russia
| | - Alexander N. Orekhov
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Research Institute of Human Morphology, 117418 Moscow, Russia
- Institute for Atherosclerosis Research, Skolkovo Innovative Centre, 143024 Moscow, Russia
| | - Igor A. Sobenin
- Laboratory of Angiopathology, Institute of General Pathology and Pathophysiology, Russian Academy of Medical Sciences, 125315 Moscow, Russia; (V.V.S.); (A.I.R.); (M.D.S.); (T.V.K.); (V.A.K.); (V.P.K.); (A.N.O.); (I.A.S.)
- Laboratory of Medical Genetics, National Medical Research Center of Cardiology, 121552 Moscow, Russia; (Z.B.K.); (N.A.D.)
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Zhu J, Zhou Y, Zhu S, Li F, Xu J, Zhang L, Shu H. circRNA circ_102049 Implicates in Pancreatic Ductal Adenocarcinoma Progression through Activating CD80 by Targeting miR-455-3p. Mediators Inflamm 2021; 2021:8819990. [PMID: 33505218 PMCID: PMC7811564 DOI: 10.1155/2021/8819990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/27/2020] [Accepted: 12/13/2020] [Indexed: 12/27/2022] Open
Abstract
Emerging evidence has shown that circular RNAs (circRNAs) and DNA methylation play important roles in the causation and progression of cancers. However, the roles of circRNAs and abnormal methylation genes in the tumorigenesis of pancreatic ductal adenocarcinoma (PDAC) are still largely unknown. Expression profiles of circRNA, gene methylation, and mRNA were downloaded from the GEO database, and differentially expressed genes were obtained via GEO2R, and a ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Inflammation-associated genes were collected from the GeneCards database. Then, functional enrichment analysis and protein-protein interaction (PPI) networks of inflammation-associated methylated expressed genes were investigated using Metascape and STRING databases, respectively, and visualized in Cytoscape. Hub genes of PPI networks were identified using the NetworkAnalyzer plugin. Also, we analyzed the methylation, protein expression levels, and prognostic value of hub genes in PDAC patients through the UALCAN, Human Protein Atlas (HPA), and Kaplan-Meier plotter databases, respectively. The circRNA_102049/miR-455-3p/CD80 axis was identified by the ceRNA network and hub genes. In vitro and in vivo experiments were performed to evaluate the functions of circRNA_102049. The regulatory mechanisms of circRNA_102049 and miR-455-3p were explored by RT-PCR, western blot, and dual-luciferase assays. In the present study, twelve hub genes (STAT1, CCND1, KRAS, CD80, ICAM1, ESR1, RAF1, RPS6KA2, KDM6B, TNRC6A, FOSB, and DNM1) were determined from the PPI networks. Additionally, the circRNA_102049 was upregulated in PDAC cell lines. Functionally, the knockdown of circRNA_102049 by siRNAs inhibited cell growth, inflammatory factors, and migratory and invasive potential and promoted cell apoptosis. Mechanistically, circRNA_102049 functioned as a sponge of miR-455-3p and partially reversed the effect of miR-455-3p and consequently upregulated CD80 expression. Our findings showed that circRNA_102049 and methylated hub genes play an important role in the proliferation, apoptosis, migration, invasion, and inflammatory response of PDAC, which might be selected as a promising prognostic marker and therapeutic target for PDAC.
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Affiliation(s)
- Jie Zhu
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Yong Zhou
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Shanshan Zhu
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Fei Li
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Jiajia Xu
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Liming Zhang
- Medical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Hairong Shu
- Department of Medical Service, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
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Scott MKD, Limaye M, Schaffert S, West R, Ozawa MG, Chu P, Nair VS, Koong AC, Khatri P. A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2021; 26:297-308. [PMID: 33691026 PMCID: PMC7958996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.
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Affiliation(s)
- Madeleine K D Scott
- Biophysics Program, Department of Medicine, Stanford University, Stanford, CA, USA,
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Ortega MA, Fraile-Martínez O, García-Honduvilla N, Coca S, Álvarez-Mon M, Buján J, Teus MA. Update on uveal melanoma: Translational research from biology to clinical practice (Review). Int J Oncol 2020; 57:1262-1279. [PMID: 33173970 PMCID: PMC7646582 DOI: 10.3892/ijo.2020.5140] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 09/24/2020] [Indexed: 02/06/2023] Open
Abstract
Uveal melanoma is the most common type of intraocular cancer with a low mean annual incidence of 5‑10 cases per million. Tumours are located in the choroid (90%), ciliary body (6%) or iris (4%) and of 85% are primary tumours. As in cutaneous melanoma, tumours arise in melanocytes; however, the characteristics of uveal melanoma differ, accounting for 3‑5% of melanocytic cancers. Among the numerous risk factors are age, sex, genetic and phenotypic predisposition, the work environment and dermatological conditions. Management is usually multidisciplinary, including several specialists such as ophthalmologists, oncologists and maxillofacial surgeons, who participate in the diagnosis, treatment and complex follow‑up of these patients, without excluding the management of the immense emotional burden. Clinically, uveal melanoma generates symptoms that depend as much on the affected ocular globe site as on the tumour size. The anatomopathological study of uveal melanoma has recently benefited from developments in molecular biology. In effect, disease classification or staging according to molecular profile is proving useful for the assessment of this type of tumour. Further, the improved knowledge of tumour biology is giving rise to a more targeted approach to diagnosis, prognosis and treatment development; for example, epigenetics driven by microRNAs as a target for disease control. In the present study, the main epidemiological, clinical, physiopathological and molecular features of this disease are reviewed, and the associations among all these factors are discussed.
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Affiliation(s)
- Miguel A. Ortega
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid
- University Center for The Defense of Madrid (CUD-ACD), 28047 Madrid
| | - Oscar Fraile-Martínez
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
| | - Natalio García-Honduvilla
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid
- University Center for The Defense of Madrid (CUD-ACD), 28047 Madrid
| | - Santiago Coca
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid
- University Center for The Defense of Madrid (CUD-ACD), 28047 Madrid
| | - Melchor Álvarez-Mon
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid
- University Center for The Defense of Madrid (CUD-ACD), 28047 Madrid
- Internal and Oncology Service (CIBER-EHD), University Hospital Príncipe de Asturias, Alcalá de Henares, 28805 Madrid
| | - Julia Buján
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid
- University Center for The Defense of Madrid (CUD-ACD), 28047 Madrid
| | - Miguel A. Teus
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, 28871 Madrid
- Ophthalmology Service, University Hospital Príncipe de Asturias, Alcalá de Henares, 28805 Madrid, Spain
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Feltes BC, Poloni JDF, Nunes IJG, Faria SS, Dorn M. Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types. Front Genet 2020; 11:586602. [PMID: 33329726 PMCID: PMC7719697 DOI: 10.3389/fgene.2020.586602] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/09/2020] [Indexed: 12/19/2022] Open
Abstract
Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.
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Affiliation(s)
- Bruno César Feltes
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Joice de Faria Poloni
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Sara Socorro Faria
- Laboratory of Immunology and Inflammation, Department of Cell Biology, University of Brasilia, Brasilia, Brazil
| | - Marcio Dorn
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
- Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Science and Technology - Forensic Science, Porto Alegre, Brazil
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Zuo Y, Song M, Li H, Chen X, Cao P, Zheng L, Cao G. Analysis of the Epigenetic Signature of Cell Reprogramming by Computational DNA Methylation Profiles. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190919103752] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
DNA methylation plays an important role in the reprogramming process.
Understanding the underlying molecular mechanism of reprogramming is crucial for answering
fundamental questions regarding the transition of cell identity.
Methods:
In this study, based on the genome-wide DNA methylation data from different cell lines,
comparative methylation profiles were proposed to identify the epigenetic signature of cell
reprogramming.
Results:
The density profile of CpG methylation showed that pluripotent cells are more polarized
than Human Dermal Fibroblasts (HDF) cells. The heterogeneity of iPS has a greater deviation in
the DNA hypermethylation pattern. The result of regional distribution showed that the differential
CpG sites between pluripotent cells and HDFs tend to accumulate in the gene body and CpG shelf
regions, whereas the internal differential methylation CpG sites (DMCs) of three types of
pluripotent cells tend to accumulate in the TSS1500 region. Furthermore, a series of endogenous
markers of cell reprogramming were identified based on the integrative analysis, including focal
adhesion, pluripotency maintenance and transcription regulation. The calcium signaling pathway
was detected as one of the signatures between NT cells and iPS cells. Finally, the regional bias of
DNA methylation for key pluripotency factors was discussed. Our studies provide new insight into
the barrier identification of cell reprogramming.
Conclusion:
Our studies analyzed some epigenetic markers and barriers of nuclear reprogramming,
hoping to provide new insight into understanding the underlying molecular mechanism
of reprogramming.
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Affiliation(s)
- Yongchun Zuo
- The College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Mingmin Song
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Hanshuang Li
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Xing Chen
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengbo Cao
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Guifang Cao
- The College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China
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Pitto L, Gorini F, Bianchi F, Guzzolino E. New Insights into Mechanisms of Endocrine-Disrupting Chemicals in Thyroid Diseases: The Epigenetic Way. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17217787. [PMID: 33114343 PMCID: PMC7662297 DOI: 10.3390/ijerph17217787] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/18/2020] [Accepted: 10/22/2020] [Indexed: 12/26/2022]
Abstract
In recent years, the presence in the environment of chemical compounds with thyroid-disrupting effects is progressively increased. This phenomenon has risen concern for human health as the preservation of thyroid system homeostasis is essential for fetal development and for maintaining psychological and physiological wellbeing. An increasing number of studies explored the role of different classes of toxicants in the occurrence and severity of thyroid diseases, but large epidemiological studies are limited and only a few animal or in vitro studies have attempted to identify the mechanisms of chemical action. Recently, epigenetic changes such as alteration of methylation status or modification of non-coding RNAs have been suggested as correlated to possible deleterious effects leading to different thyroid disorders in susceptible individuals. This review aims to analyze the epigenetic alterations putatively induced by chemical exposures and involved in the onset of frequent thyroid diseases such as thyroid cancer, autoimmune thyroiditis and disruption of fetal thyroid homeostasis.
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Affiliation(s)
- Letizia Pitto
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.G.); (F.B.); (E.G.)
- Correspondence: ; Tel.: + 39-050-3153090
| | - Francesca Gorini
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.G.); (F.B.); (E.G.)
| | - Fabrizio Bianchi
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.G.); (F.B.); (E.G.)
| | - Elena Guzzolino
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.G.); (F.B.); (E.G.)
- Department of Biosciences, University of Milan, 20133 Milan, Italy
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Abstract
Background DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers. Methods In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM. Results PanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect. Conclusions PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html.
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Affiliation(s)
- Mai Shi
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.,Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, Georgia, 30322, USA
| | - Yingying Wei
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.
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Chen Z, Huang J, Feng Y, Li Z, Jiang Y. Screening and bioinformatics analysis of a ceRNA network based on the circular RNAs, miRNAs, and mRNAs in pan-cancer. Cancer Med 2020; 9:7279-7292. [PMID: 33094914 PMCID: PMC7541145 DOI: 10.1002/cam4.3375] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 07/09/2020] [Accepted: 07/21/2020] [Indexed: 01/12/2023] Open
Abstract
Background The pan‐cancer analysis has recently brought us into a novel level of cancer research. Nowadays, the Circular RNAs (circRNAs) is becoming increasingly important in the occurrence and progression of tumors. Nevertheless, the specific expression patterns and functions of circRNAs in the pan‐cancer remains unclear. Here we aimed to explore the expression patterns and functions of circRNAs in pan‐cancer. Methods We combined our microarray with seven circRNA arrays from the Gene Expression Omnibus (GEO) database and transcriptome profiles were acquired from The Cancer Genome Atlas (TCGA) database. A circRNA‐miRNA‐mRNA network was created and analyzed using multiple bioinformatic approaches including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Search Tool for the Retrieval of Interacting Genes (STRING) database, cytoHubba and MCODE app. Cell function assays including CCK‐8 analysis, colony formation, and transwell assay were used to explore pan‐circRNAs’ functions. Results A panel of 6 circRNAs, 11 miRNAs, and 318 mRNAs was found to be differentially expressed (DE) in pan‐cancer. A circRNA‐miRNA‐mRNA network was also constructed. Then, a circRNA‐miRNA‐hub gene network was created according to 5 pan‐circRNAs, 8 pan‐miRNAs, and 16 pan‐mRNAs. Enrichment analysis pointed out the possible association of DEmRNAs with pan‐cancer is transcriptional misregulation in cancer. Overexpression of hsa_circ_0004639 and silence of hsa_circ_0008310 can inhibit the malignant biological properties of cancer cells. Conclusions Six pan‐circRNAs were discovered and their regulating mechanisms were predicted. Those findings together will give a new insight into pan‐cancer research and present potential therapy targeting as well as promising biomarkers.
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Affiliation(s)
- Zhanghan Chen
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jie Huang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanling Feng
- Department of Pathology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zehuan Li
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ying Jiang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Department of General Surgery, Xiamen Branch, Zhongshan Hospital, Fudan University, Fujian, China
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40
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Zhou X, Chai H, Zhao H, Luo CH, Yang Y. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network. Gigascience 2020; 9:giaa076. [PMID: 32649756 PMCID: PMC7350980 DOI: 10.1093/gigascience/giaa076] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 04/23/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets. RESULTS Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7-11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. CONCLUSIONS TDimpute is an effective method for RNA-seq imputation with limited training samples.
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Affiliation(s)
- Xiang Zhou
- School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou 510006, China
| | - Hua Chai
- School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou 510006, China
| | - Huiying Zhao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yan Jiang West Road, Guangzhou 510120, China
| | - Ching-Hsing Luo
- School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou 510006, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou 510006, China
- Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, 132 East Waihuan Road, Guangzhou 510006, China
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Sang L, Yu Z, Wang A, Li H, Dai X, Sun L, Liu H, Yuan Y. Identification of methylated-differentially expressed genes and pathways in esophageal squamous cell carcinoma. Pathol Res Pract 2020; 216:153050. [PMID: 32825936 PMCID: PMC7283077 DOI: 10.1016/j.prp.2020.153050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/26/2020] [Accepted: 06/07/2020] [Indexed: 12/19/2022]
Abstract
Methylation, as an epigenetic modification, can affect gene expression and play a role in the occurrence and development of cancer. This research is devoted to discover methylated-differentially expressed genes (MDEGs) in esophageal squamous cell carcinoma (ESCC) and explore special associated pathways. We downloaded GSE51287 methylation profiles and GSE26886 expression profiles from GEO DataSets, and performed a comprehensive bioinformatics analysis. Totally, 19 hypermethylated, lowly expressed genes (Hyper-LGs) were identified, and involved in regulation of cell proliferation, phosphorus metabolic process and protein kinase activity. Meanwhile, 17 hypomethylated, highly expressed genes (Hypo-HGs) were participated in collagen catabolic process, metallopeptidase and cytokine activity. Pathway analysis determined that Hyper-LGs were enriched in arachidonic acid metabolism pathway, while Hypo-HGs were primarily associated with the cytokine-cytokine receptor interaction pathway. IL 6, MMP3, MMP9, SPP1 were identified as hub genes based on the PPI network that combined 7 ranked methods included in cytoHubba, and verification was performed in human tissues. Our integrated analysis identified many novel genetic lesions in ESCC and provides a crucial molecular foundation to improve our understanding of ESCC. Hub genes, including IL 6, MMP3, MMP9 and SPP1, could be considered for use as aberrant methylation-based biomarkers to facilitate the accurate diagnosis and therapy of ESCC.
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Affiliation(s)
- Liang Sang
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Ultrasound Department, the First Hospital of China Medical University, Shenyang 110001, China
| | - Zhanwu Yu
- Department of Thoracic Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No. 44 Xiaoheyan Road, Shenyang, Liaoning 110042, China
| | - Ang Wang
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Hao Li
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Xiantong Dai
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Liping Sun
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Hongxu Liu
- Department of Thoracic Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No. 44 Xiaoheyan Road, Shenyang, Liaoning 110042, China.
| | - Yuan Yuan
- Cancer Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang 110001, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China.
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42
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Pan MS, Cao J, Fan YZ. Insight into norcantharidin, a small-molecule synthetic compound with potential multi-target anticancer activities. Chin Med 2020; 15:55. [PMID: 32514288 PMCID: PMC7260769 DOI: 10.1186/s13020-020-00338-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 05/25/2020] [Indexed: 02/07/2023] Open
Abstract
Norcantharidin (NCTD) is a demethylated derivative of cantharidin, which is an anticancer active ingredient of traditional Chinese medicine, and is currently used clinically as a routine anti-cancer drug in China. Clarifying the anticancer effect and molecular mechanism of NCTD is critical for its clinical application. Here, we summarized the physiological, chemical, pharmacokinetic characteristics and clinical applications of NCTD. Besides, we mainly focus on its potential multi-target anticancer activities and underlying mechanisms, and discuss the problems existing in clinical application and scientific research of NCTD, so as to provide a potential anticancer therapeutic agent for human malignant tumors.
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Affiliation(s)
- Mu-Su Pan
- Department of Surgery, Tongji Hospital, Tongji University School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200065 People’s Republic of China
| | - Jin Cao
- Department of Surgery, Tongji Hospital, Tongji University School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200065 People’s Republic of China
| | - Yue-Zu Fan
- Department of Surgery, Tongji Hospital, Tongji University School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200065 People’s Republic of China
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43
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González-Reymúndez A, Vázquez AI. Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin. Sci Rep 2020; 10:8341. [PMID: 32433524 PMCID: PMC7239905 DOI: 10.1038/s41598-020-65119-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 04/07/2020] [Indexed: 02/08/2023] Open
Abstract
Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.
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Affiliation(s)
- Agustín González-Reymúndez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering (IQ), Michigan State University, East Lansing, MI, USA
| | - Ana I Vázquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
- Institute for Quantitative Health Science and Engineering (IQ), Michigan State University, East Lansing, MI, USA.
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44
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Ji J, Zhao L, Zhao X, Li Q, An Y, Li L, Li D. Genome‑wide DNA methylation regulation analysis of long non‑coding RNAs in glioblastoma. Int J Mol Med 2020; 46:224-238. [PMID: 32319552 PMCID: PMC7255472 DOI: 10.3892/ijmm.2020.4579] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 01/22/2020] [Indexed: 12/12/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor associated with high mortality. Long non-coding RNAs (lncRNAs) are increasingly being recognized as its modulators. However, it remains mostly unexplored how lncRNAs are mediated by DNA methylation in GBM. The present study integrated multi-omics data to analyze the epigenetic dysregulation of lncRNAs in GBM. Widely aberrant methylation in the lncRNA promoters was observed, and the lncRNA promoters exhibited a more hypomethylated pattern in GBM. By combining transcriptional datasets, it was possible identify the lncRNAs whose transcriptional changes might be associated with the aberrant promoter methylation. Then, a methylation-mediated lncRNA regulatory network and functional enrichment analysis of aberrantly methylated lncRNAs showed that lncRNAs with different methylation patterns were involved in diverse GBM progression-related biological functions and pathways. Specifically, four lncRNAs whose increased expression may be regulated by the corresponding promoter hypomethylation were evaluated to have an excellent diagnostic effect and clinical prognostic value. Finally, through the construction of drug-target association networks, the present study identified potential therapeutic targets and small-molecule drugs for GBM treatment. The present study provides novel insights for understanding the regulation of lncRNAs by DNA methylation and developing cancer biomarkers in GBM.
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Affiliation(s)
- Jianghuai Ji
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, P.R. China
| | - Lei Zhao
- Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
| | - Xiaoxiao Zhao
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, P.R. China
| | - Qianpeng Li
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, P.R. China
| | - Yi An
- Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P.R. China
| | - Li Li
- Luoyang Central Hospital Affiliated To Zhengzhou University, Luoyang, Henan 471009, P.R. China
| | - Dongguo Li
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, P.R. China
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45
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Lv L, Cao L, Hu G, Shen Q, Wu J. Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma. Front Genet 2020; 11:294. [PMID: 32296463 PMCID: PMC7136565 DOI: 10.3389/fgene.2020.00294] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 03/12/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Aberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. Nevertheless, the exact role of methylation in the carcinogenesis of thyroid cancer and its association with prognosis remains unclear. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis. METHODS The transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from The Cancer Genome Atlas (TCGA) database. The methylmix R package was used to screen DNA methylation-driven genes in thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to annotate the function of methylation-driven genes. Univariate Cox regression analyses was performed to distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses was utilized to build a prognostic multi-gene signature. A survival analysis was carried out to determine the individual prognostic significance of this multi-gene signature. RESULTS A total of 51 methylation-driven genes were identified. The functional analysis indicated that these genes were significantly enriched in diverse biological processes (BP) and pathways related to the malignancy processes. Four of these genes (RDH5, TREM1, BIRC7, and SLC26A7) were selected to construct the risk evaluation model. Patients in the low-risk group had an conspicuously better overall survival (OS) than those in high-risk group (p < 0.001). The area under the receiver operating characteristic (ROC) curve for this model was 0.836, suggesting a good specificity and sensitivity. Subsequent survival analysis revealed that this four-gene signature served as an independent indicator for the prognosis of thyroid cancer. Moreover, the prognostic signature was well validated in a external thyroid cancer cohort. CONCLUSION We identified methylation-driven genes in thyroid cancer with independent prognostic value, which may offer new insight into molecular mechanisms of thyroid cancer and provide new possibility for individualized treatment of thyroid cancer patients.
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Affiliation(s)
- Liting Lv
- Department of Thyroid and Breast Surgery, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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46
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Gan Y, Li N, Xin Y, Zou G. TriPCE: A Novel Tri-Clustering Algorithm for Identifying Pan-Cancer Epigenetic Patterns. Front Genet 2020; 10:1298. [PMID: 32010182 PMCID: PMC6974616 DOI: 10.3389/fgene.2019.01298] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 11/25/2019] [Indexed: 11/20/2022] Open
Abstract
Epigenetic alteration is a fundamental characteristic of nearly all human cancers. Tumor cells not only harbor genetic alterations, but also are regulated by diverse epigenetic modifications. Identification of epigenetic similarities across different cancer types is beneficial for the discovery of treatments that can be extended to different cancers. Nowadays, abundant epigenetic modification profiles have provided a great opportunity to achieve this goal. Here, we proposed a new approach TriPCE, introducing tri-clustering strategy to integrative pan-cancer epigenomic analysis. The method is able to identify coherent patterns of various epigenetic modifications across different cancer types. To validate its capability, we applied the proposed TriPCE to analyze six important epigenetic marks among seven cancer types, and identified significant cross-cancer epigenetic similarities. These results suggest that specific epigenetic patterns indeed exist among these investigated cancers. Furthermore, the gene functional analysis performed on the associated gene sets demonstrates strong relevance with cancer development and reveals consistent risk tendency among these investigated cancer types.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Ning Li
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Yongchang Xin
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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47
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Liu Y, Gu Y, Su M, Liu H, Zhang S, Zhang Y. An analysis about heterogeneity among cancers based on the DNA methylation patterns. BMC Cancer 2019; 19:1259. [PMID: 31888612 PMCID: PMC6937830 DOI: 10.1186/s12885-019-6455-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 12/11/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. METHODS This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. RESULTS First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. CONCLUSIONS This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer.
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Affiliation(s)
- Yang Liu
- School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, 150001, China
| | - Yue Gu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Mu Su
- School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, 150001, China
| | - Hui Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shumei Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040, China.
| | - Yan Zhang
- School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, 150001, China.
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48
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Locke WJ, Guanzon D, Ma C, Liew YJ, Duesing KR, Fung KYC, Ross JP. DNA Methylation Cancer Biomarkers: Translation to the Clinic. Front Genet 2019; 10:1150. [PMID: 31803237 PMCID: PMC6870840 DOI: 10.3389/fgene.2019.01150] [Citation(s) in RCA: 296] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 10/22/2019] [Indexed: 12/23/2022] Open
Abstract
Carcinogenesis is accompanied by widespread DNA methylation changes within the cell. These changes are characterized by a globally hypomethylated genome with focal hypermethylation of numerous 5’-cytosine-phosphate-guanine-3’ (CpG) islands, often spanning gene promoters and first exons. Many of these epigenetic changes occur early in tumorigenesis and are highly pervasive across a tumor type. This allows DNA methylation cancer biomarkers to be suitable for early detection and also to have utility across a range of areas relevant to cancer detection and treatment. Such tests are also simple in construction, as only one or a few loci need to be targeted for good test coverage. These properties make cancer-associated DNA methylation changes very attractive for development of cancer biomarker tests with substantive clinical utility. Across the patient journey from initial detection, to treatment and then monitoring, there are several points where DNA methylation assays can inform clinical practice. Assays on surgically removed tumor tissue are useful to determine indicators of treatment resistance, prognostication of outcome, or to molecularly characterize, classify, and determine the tissue of origin of a tumor. Cancer-associated DNA methylation changes can also be detected with accuracy in the cell-free DNA present in blood, stool, urine, and other biosamples. Such tests hold great promise for the development of simple, economical, and highly specific cancer detection tests suitable for population-wide screening, with several successfully translated examples already. The ability of circulating tumor DNA liquid biopsy assays to monitor cancer in situ also allows for the ability to monitor response to therapy, to detect minimal residual disease and as an early biomarker for cancer recurrence. This review will summarize existing DNA methylation cancer biomarkers used in clinical practice across the application domains above, discuss what makes a suitable DNA methylation cancer biomarker, and identify barriers to translation. We discuss technical factors such as the analytical performance and product-market fit, factors that contribute to successful downstream investment, including geography, and how this impacts intellectual property, regulatory hurdles, and the future of the marketplace and healthcare system.
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Affiliation(s)
- Warwick J Locke
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Probing Biosystems Future Science Platform, CSIRO Health and Biosecurity, Canberra, ACT, Australia
| | - Dominic Guanzon
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Probing Biosystems Future Science Platform, CSIRO Health and Biosecurity, Canberra, ACT, Australia
| | - Chenkai Ma
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia
| | - Yi Jin Liew
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Probing Biosystems Future Science Platform, CSIRO Health and Biosecurity, Canberra, ACT, Australia
| | - Konsta R Duesing
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia
| | - Kim Y C Fung
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Probing Biosystems Future Science Platform, CSIRO Health and Biosecurity, Canberra, ACT, Australia
| | - Jason P Ross
- Molecular Diagnostics Solutions, CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Probing Biosystems Future Science Platform, CSIRO Health and Biosecurity, Canberra, ACT, Australia
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49
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Liu B, Liu Y, Pan X, Li M, Yang S, Li SC. DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning. Genes (Basel) 2019; 10:genes10100778. [PMID: 31590287 PMCID: PMC6826785 DOI: 10.3390/genes10100778] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/21/2019] [Accepted: 09/30/2019] [Indexed: 12/13/2022] Open
Abstract
For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.
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Affiliation(s)
- Biao Liu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China.
| | - Yulu Liu
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China.
| | - Xingxin Pan
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China.
| | - Mengyao Li
- Research and Development Department, Shenzhen Byoryn Technology Co.,Ltd, Shenzhen 518000, China.
| | - Shuang Yang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China.
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong.
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50
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Long J, Chen P, Lin J, Bai Y, Yang X, Bian J, Lin Y, Wang D, Yang X, Zheng Y, Sang X, Zhao H. DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma. Am J Cancer Res 2019; 9:7251-7267. [PMID: 31695766 PMCID: PMC6831284 DOI: 10.7150/thno.31155] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 08/05/2019] [Indexed: 12/21/2022] Open
Abstract
In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes. Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively. Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.
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