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Tan T, Hu H, Zhang W, Cui J, Lu Z, Li X, Song J. Novel immune classification based on machine learning of pathological images predicts early recurrence of hepatocellular carcinoma. Front Oncol 2024; 14:1391486. [PMID: 38826785 PMCID: PMC11140080 DOI: 10.3389/fonc.2024.1391486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction Immune infiltration within the tumor microenvironment (TME) plays a significant role in the onset and progression of hepatocellular carcinoma (HCC). Machine learning applied to pathological images offers a practical means to explore the TME at the cellular level. Our former research employed a transfer learning procedure to adapt a convolutional neural network (CNN) model for cell recognition, which could recognize tumor cells, lymphocytes, and stromal cells autonomously and accurately within the images. This study introduces a novel immune classification system based on the modified CNN model. Method Patients with HCC from both Beijing Hospital and The Cancer Genome Atlas (TCGA) database were included in this study. Additionally, least absolute shrinkage and selection operator (LASSO) analyses, along with logistic regression, were utilized to develop a prognostic model. We proposed an immune classification based on the percentage of lymphocytes, with a threshold set at the median lymphocyte percentage. Result Patients were categorized into high or low infiltration subtypes based on whether their lymphocyte percentages were above or below the median, respectively. Patients with different immune infiltration subtypes exhibited varying clinical features and distinct TME characteristics. The low-infiltration subtype showed a higher incidence of hypertension and fatty liver, more advanced tumor stages, downregulated immune-related genes, and higher infiltration of immunosuppressive cells. A reliable prognostic model for predicting early recurrence of HCC based on clinical features and immune classification was established. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was 0.918 and 0.814 for the training and test sets, respectively. Discussion In conclusion, we proposed a novel immune classification system based on cell information extracted from pathological slices, provides a novel tool for prognostic evaluation in HCC.
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Affiliation(s)
- Tianhua Tan
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Huijuan Hu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Wei Zhang
- Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ju Cui
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhenhua Lu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Cancer Center, Ward I, Peking University Cancer Hospital & Institute, Beijing, China
| | - Xuefei Li
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jinghai Song
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Lin H, Wang J, Shi Q, Wu M. Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma. PeerJ 2024; 12:e17338. [PMID: 38708353 PMCID: PMC11069361 DOI: 10.7717/peerj.17338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
Background This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC. Methods Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis. Results NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1. Conclusion We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.
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Affiliation(s)
- Huiyue Lin
- Oncology Department, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Juyong Wang
- Oncology Department, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qing Shi
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Minmin Wu
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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Schofer K, Laufer F, Stadler J, Hahn S, Gaiselmann G, Latz A, Birke KP. Machine Learning-Based Lifetime Prediction of Lithium-Ion Cells. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200630. [PMID: 36026576 PMCID: PMC9561774 DOI: 10.1002/advs.202200630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/25/2022] [Indexed: 06/15/2023]
Abstract
Precise lifetime predictions for lithium-ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero-emission mobility. However, limitations remain due to the complex degradation of lithium-ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium-ion pouch-cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.
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Affiliation(s)
- Kai Schofer
- Research & DevelopmentMercedes‐Benz AGMercedesstraße 12070327StuttgartGermany
- Insitute for Photovoltaics ‐ Electrical Energy Storage SystemsUniversity of StuttgartPfaffenwaldring 4770569StuttgartGermany
| | - Florian Laufer
- Research & DevelopmentMercedes‐Benz AGMercedesstraße 12070327StuttgartGermany
- Insitute for Photovoltaics ‐ Electrical Energy Storage SystemsUniversity of StuttgartPfaffenwaldring 4770569StuttgartGermany
| | - Jochen Stadler
- Research & DevelopmentMercedes‐Benz AGMercedesstraße 12070327StuttgartGermany
- Institute of ElectrochemistryUlm UniversityAlbert‐Einstein‐Allee 4789081UlmGermany
| | - Severin Hahn
- Research & DevelopmentMercedes‐Benz AGMercedesstraße 12070327StuttgartGermany
| | - Gerd Gaiselmann
- Research & DevelopmentMercedes‐Benz AGMercedesstraße 12070327StuttgartGermany
| | - Arnulf Latz
- Institute of ElectrochemistryUlm UniversityAlbert‐Einstein‐Allee 4789081UlmGermany
- Helmholtz Institute for Electrochemical Energy Storage (HIU)Helmholtzstraße 1189081UlmGermany
| | - Kai P. Birke
- Insitute for Photovoltaics ‐ Electrical Energy Storage SystemsUniversity of StuttgartPfaffenwaldring 4770569StuttgartGermany
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Zhao X, Ge L, Wang J, Song Z, Ni B, He X, Ruan Z, You Y. Exploration of Potential Integrated Models of N6-Methyladenosine Immunity in Systemic Lupus Erythematosus by Bioinformatic Analyses. Front Immunol 2022; 12:752736. [PMID: 35197962 PMCID: PMC8859446 DOI: 10.3389/fimmu.2021.752736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 12/31/2021] [Indexed: 01/27/2023] Open
Abstract
Systemic lupus erythematosus (SLE) is a prototypical systemic autoimmune disease of unknown etiology. The epigenetic regulation of N6-methyladenosine (m6A) modification in immunity is emerging. However, few studies have focused on SLE and m6A immune regulation. In this study, we aimed to explore a potential integrated model of m6A immunity in SLE. The models were constructed based on RNA-seq data of SLE. A consensus clustering algorithm was applied to reveal the m6A-immune signature using principal component analysis (PCA). Univariate and multivariate Cox regression analyses and Kaplan–Meier analysis were used to evaluate diagnostic differences between groups. The effects of m6A immune-related characteristics were investigated, including risk evaluation of m6A immune phenotype-related characteristics, immune cell infiltration profiles, diagnostic value, and enrichment pathways. CIBERSORT, ESTIMATE, and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the relative immune cell infiltrations (ICIs) of the samples. Conventional bioinformatics methods were used to identify key m6A regulators, pathways, gene modules, and the coexpression network of SLE. In summary, our study revealed that IGFBP3 (as a key m6A regulator) and two pivotal immune genes (CD14 and IDO1) may aid in the diagnosis and treatment of SLE. The potential integrated models of m6A immunity that we developed could guide clinical management and may contribute to the development of personalized immunotherapy strategies.
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Affiliation(s)
- Xingwang Zhao
- Department of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Lan Ge
- Department of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Juan Wang
- Department of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Zhiqiang Song
- Department of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Bing Ni
- Department of Pathophysiology, College of High Altitude Military Medicine, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xiaochong He
- Department of Nursing Administration, Faculty of Nursing, Army Medical University (Third Military Medical University), Chongqing, China
- *Correspondence: Yi You, ; Xiaochong He, ; Zhihua Ruan,
| | - Zhihua Ruan
- Department of Oncology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
- *Correspondence: Yi You, ; Xiaochong He, ; Zhihua Ruan,
| | - Yi You
- Department of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
- *Correspondence: Yi You, ; Xiaochong He, ; Zhihua Ruan,
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Papini GB, Fonseca P, van Gilst MM, Bergmans JWM, Vullings R, Overeem S. Wearable monitoring of sleep-disordered breathing: estimation of the apnea-hypopnea index using wrist-worn reflective photoplethysmography. Sci Rep 2020; 10:13512. [PMID: 32782313 PMCID: PMC7421543 DOI: 10.1038/s41598-020-69935-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/14/2020] [Indexed: 12/15/2022] Open
Abstract
A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea-hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen's kappa = 0.51) and screen for OSA (ROC-AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring.
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Affiliation(s)
- Gabriele B Papini
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Philips Research, High Tech Campus, 5656 AE, Eindhoven, The Netherlands.
- Sleep Medicine Centre Kempenhaeghe, 5591 VE, Heeze, The Netherlands.
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Philips Research, High Tech Campus, 5656 AE, Eindhoven, The Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Centre Kempenhaeghe, 5591 VE, Heeze, The Netherlands
| | - Jan W M Bergmans
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Philips Research, High Tech Campus, 5656 AE, Eindhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Centre Kempenhaeghe, 5591 VE, Heeze, The Netherlands
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Brink V, van Driel C, El Bouhaddani S, Wardenaar KJ, van Domburgh L, Schaefer B, van Beilen M, Bartels-Velthuis AA, Veling W. Spontaneous discontinuation of distressing auditory verbal hallucinations in a school-based sample of adolescents: a longitudinal study. Eur Child Adolesc Psychiatry 2020; 29:777-790. [PMID: 31455976 PMCID: PMC7305260 DOI: 10.1007/s00787-019-01393-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 08/17/2019] [Indexed: 11/24/2022]
Abstract
Auditory verbal hallucinations (AVH) can be transiently present in both clinical and healthy adolescent populations. It is not yet fully understood why AVH discontinue in some adolescents and persist in others. The aim of this explorative study is to investigate predictors of spontaneous discontinuation of distressing AVH in a school-based sample of adolescents. 1841 adolescents (mean age 12.4 years, 58% female) completed self-report questionnaires at baseline. The current study included 123 adolescents (7%; 63% female) who reported at least mild distressing AVH at baseline and completed follow-up measurements. LASSO analyses were used to uncover predictors of spontaneous discontinuation of distressing AVH. During follow-up, 43 adolescents (35%) reported having experienced distressing AVH during the last 12 months, while 80 adolescents did not. Spontaneous discontinuation of distressing AVH was predicted by never having used cannabis, parents not being divorced in the past year, never having been scared by seeing a deceased body, less prosocial behaviour, school grade repetition, having the feeling that others have it in for you, having anxiety when meeting new people, having lived through events exactly as if they happened before and having the feeling as if parts of the body have changed. No associations between spontaneous discontinuation of distressing AVH and age or ethnicity were found. Distressing AVH in non-clinical adolescents are mostly transient. Discontinuation was predicted up to a certain extent. However, several predictors were difficult to interpret and do not provide leads for preventive measures, except for discouraging cannabis use.
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Affiliation(s)
- Vera Brink
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, PO Box 30.001 (HPC CC60), 9700 RB, Groningen, The Netherlands.
| | - Catheleine van Driel
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, PO Box 30.001 (HPC CC60), 9700 RB, Groningen, The Netherlands
| | | | - Klaas J Wardenaar
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, , PO Box 30.001, Groningen, 9700 RB, The Netherlands
| | - Lieke van Domburgh
- Department of Child and Adolescent Psychiatry, VU University Medical Center, PO Box 303, 1115 ZG, Duivendrecht, The Netherlands
- Department of Research and Development, Pluryn-Intermetzo, PO Box 53, 6500 AB, Nijmegen, The Netherlands
| | - Barbara Schaefer
- Parnassia Institute, Carnissesingel 51, 3083 JA, Rotterdam, The Netherlands
| | - Marije van Beilen
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, PO Box 30.001 (HPC CC60), 9700 RB, Groningen, The Netherlands
| | - Agna A Bartels-Velthuis
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research center, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Wim Veling
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, PO Box 30.001 (HPC CC60), 9700 RB, Groningen, The Netherlands
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On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study. JOURNAL OF RISK AND FINANCIAL MANAGEMENT 2019. [DOI: 10.3390/jrfm12030109] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Model selection and model averaging are popular approaches for handling modeling uncertainties. The existing literature offers a unified framework for variable selection via penalized likelihood and the tuning parameter selection is vital for consistent selection and optimal estimation. Few studies have explored the finite sample performances of the class of ordinary least squares (OLS) post-selection estimators with the tuning parameter determined by different selection approaches. We aim to supplement the literature by studying the class of OLS post-selection estimators. Inspired by the shrinkage averaging estimator (SAE) and the Mallows model averaging (MMA) estimator, we further propose a shrinkage MMA (SMMA) estimator for averaging high-dimensional sparse models. Our Monte Carlo design features an expanding sparse parameter space and further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of estimators. We find that the OLS post-smoothly clipped absolute deviation (SCAD) estimator with the tuning parameter selected by the Bayesian information criterion (BIC) in finite sample outperforms most penalized estimators and that the SMMA performs better when averaging high-dimensional sparse models.
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Xiang XH, Yang L, Zhang X, Ma XH, Miao RC, Gu JX, Fu YN, Yao Q, Zhang JY, Liu C, Lin T, Qu K. Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma. World J Gastroenterol 2019; 25:1715-1728. [PMID: 31011256 PMCID: PMC6465944 DOI: 10.3748/wjg.v25.i14.1715] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 03/06/2019] [Accepted: 03/16/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Cellular senescence is a recognized barrier for progression of chronic liver diseases to hepatocellular carcinoma (HCC). The expression of a cluster of genes is altered in response to environmental factors during senescence. However, it is questionable whether these genes could serve as biomarkers for HCC patients.
AIM To develop a signature of senescence-associated genes (SAGs) that predicts patients’ overall survival (OS) to improve prognosis prediction of HCC.
METHODS SAGs were identified using two senescent cell models. Univariate COX regression analysis was performed to screen the candidate genes significantly associated with OS of HCC in a discovery cohort (GSE14520) for the least absolute shrinkage and selection operator modelling. Prognostic value of this seven-gene signature was evaluated using two independent cohorts retrieved from the GEO (GSE14520) and the Cancer Genome Atlas datasets, respectively. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the seven-SAG signature and serum α-fetoprotein (AFP).
RESULTS A total of 42 SAGs were screened and seven of them, including KIF18B, CEP55, CIT, MCM7, CDC45, EZH2, and MCM5, were used to construct a prognostic formula. All seven genes were significantly downregulated in senescent cells and upregulated in HCC tissues. Survival analysis indicated that our seven-SAG signature was strongly associated with OS, especially in Asian populations, both in discovery and validation cohorts. Moreover, time-dependent ROC curve analysis suggested the seven-gene signature had a better predictive accuracy than serum AFP in predicting HCC patients’ 1-, 3-, and 5-year OS.
CONCLUSION We developed a seven-SAG signature, which could predict OS of Asian HCC patients. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.
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Affiliation(s)
- Xiao-Hong Xiang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Li Yang
- Department of Clinical Laboratory, Liaocheng People’s Hospital, Taishan Medical College, Liaocheng 252000, Shandong Province, China
| | - Xing Zhang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Xiao-Hua Ma
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Run-Chen Miao
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Jing-Xian Gu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Yu-Nong Fu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Qing Yao
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Jing-Yao Zhang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Chang Liu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Ting Lin
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
| | - Kai Qu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
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