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Christou CD, Tsoulfas G. Challenges involved in the application of artificial intelligence in gastroenterology: The race is on! World J Gastroenterol 2023; 29:6168-6178. [PMID: 38186861 PMCID: PMC10768398 DOI: 10.3748/wjg.v29.i48.6168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023] Open
Abstract
Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Chen Q, Wang Y, Liu Y, Xi B. ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning. Front Physiol 2022; 13:905523. [PMID: 35812327 PMCID: PMC9262247 DOI: 10.3389/fphys.2022.905523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.
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Affiliation(s)
- Qiu Chen
- Medical College, Yangzhou University, Yangzhou, China
| | - Yu Wang
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Yongjun Liu
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
| | - Bin Xi
- College of Physics Science and Technology, Yangzhou University, Yangzhou, China
- *Correspondence: Bin Xi,
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Davoodi Z, Shafiee F. Internalizing RGD, a great motif for targeted peptide and protein delivery: a review article. Drug Deliv Transl Res 2022; 12:2261-2274. [PMID: 35015253 DOI: 10.1007/s13346-022-01116-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 01/10/2023]
Abstract
Understanding that cancer is one of the most important health problems, especially in advanced societies, is not difficult. The term of targeted cancer therapy has also been well known as an ideal treatment strategy in the recent years. Peptides with ability to specifically recognize the cancer cells with suitable penetration properties have been used as the targeting motif in this regard. In the present review article, we focus on an individual RGD-derived peptide with ability to recognize the integrin receptor on the cancer cell surface like its ancestor with an additional outstanding feature to penetrate to extravascular space of tumor and ability to penetrate to cancer cells unlike the original peptide. This peptide which has been named "internalizing RGD" or "iRGD" has been the focus of researches as a new targeting motif since it was discovered. To date, many types of molecules have been associated with this peptide for their targeted delivery to cancer cells. In this review article, we have discussed a summary of penetration mechanisms of iRGD and all introduced peptides and proteins attached to this attractive cell-penetrating peptide and have expressed the results of the studies.
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Affiliation(s)
- Zeinabosadat Davoodi
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Hezar Jarib Ave., Isfahan, Iran
| | - Fatemeh Shafiee
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Hezar Jarib Ave., Isfahan, Iran.
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Xie Y, Meng WY, Li RZ, Wang YW, Qian X, Chan C, Yu ZF, Fan XX, Pan HD, Xie C, Wu QB, Yan PY, Liu L, Tang YJ, Yao XJ, Wang MF, Leung ELH. Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol 2021; 14:100907. [PMID: 33217646 PMCID: PMC7683339 DOI: 10.1016/j.tranon.2020.100907] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/21/2020] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients' plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.
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Affiliation(s)
- Ying Xie
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Wei-Yu Meng
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Run-Ze Li
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Yu-Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Xin Qian
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Chang Chan
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Zhi-Fang Yu
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Xing-Xing Fan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Hu-Dan Pan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Chun Xie
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Qi-Biao Wu
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Pei-Yu Yan
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Liang Liu
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China
| | - Yi-Jun Tang
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Xiao-Jun Yao
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China.
| | - Mei-Fang Wang
- Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China.
| | - Elaine Lai-Han Leung
- State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau (SAR), China; Respiratory Medicine department of Taihe Hospital, Hubei University of Medicine, Hubei, China.
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Non-coding RNAs underlying chemoresistance in gastric cancer. Cell Oncol (Dordr) 2020; 43:961-988. [PMID: 32495294 DOI: 10.1007/s13402-020-00528-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 04/17/2020] [Accepted: 04/24/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Gastric cancer (GC) is a major health issue in the Western world. Current clinical imperatives for this disease include the identification of more effective biomarkers to detect GC at early stages and enhance the prevention and treatment of metastatic and chemoresistant GC. The advent of non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long-non coding RNAs (lncRNAs), has led to a better understanding of the mechanisms by which GC cells acquire features of therapy resistance. ncRNAs play critical roles in normal physiology, but their dysregulation has been detected in a variety of cancers, including GC. A subset of ncRNAs is GC-specific, implying their potential application as biomarkers and/or therapeutic targets. Hence, evaluating the specific functions of ncRNAs will help to expand novel treatment options for GC. CONCLUSIONS In this review, we summarize some of the well-known ncRNAs that play a role in the development and progression of GC. We also review the application of such ncRNAs in clinical diagnostics and trials as potential biomarkers. Obviously, a deeper understanding of the biology and function of ncRNAs underlying chemoresistance can broaden horizons toward the development of personalized therapy against GC.
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Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020; 146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 06/26/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer. METHODS A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications. RESULTS A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance. CONCLUSIONS In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.
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Affiliation(s)
- Peng Jin
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaoyan Ji
- Department of Emergency Ward, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Wenzhe Kang
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hao Liu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fuhai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Haitao Hu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Weikun Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yantao Tian
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Comprehensive Analysis of a circRNA-miRNA-mRNA Network to Reveal Potential Inflammation-Related Targets for Gastric Adenocarcinoma. Mediators Inflamm 2020; 2020:9435608. [PMID: 32801999 PMCID: PMC7416288 DOI: 10.1155/2020/9435608] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/28/2020] [Indexed: 12/14/2022] Open
Abstract
Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.
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Application of artificial intelligence in the diagnosis and prediction of gastric cancer. Artif Intell Gastroenterol 2020. [DOI: 10.35712/wjg.v1.i1.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Qie YY, Xue XF, Wang XG, Dang SC. Application of artificial intelligence in the diagnosis and prediction of gastric cancer. Artif Intell Gastroenterol 2020; 1:12-18. [DOI: 10.35712/aig.v1.i1.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer is the second leading cause of cancer deaths worldwide. Despite the great progress in the diagnosis and treatment of gastric cancer, the incidence and mortality rate of the disease in China are still relatively high. The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis. Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer, in order to more accurately predict the survival times of gastric cancer patients. Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer. In the past decade, artificial intelligence technology has been applied to various fields of medicine as a branch of computer science. This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.
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Affiliation(s)
- Yin-Yin Qie
- Department of General Surgery, The Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
| | - Xiao-Fei Xue
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Xiao-Gang Wang
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Sheng-Chun Dang
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
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Dang H, Ye Y, Zhao X, Zeng Y. Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database. BMC Cardiovasc Disord 2020; 20:320. [PMID: 32631246 PMCID: PMC7336680 DOI: 10.1186/s12872-020-01596-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 06/24/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Ischemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM. METHODS We studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR). RESULTS From three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results. CONCLUSIONS The candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.
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Affiliation(s)
- Haiming Dang
- Department of cardiac surgery, Capital medical university, Beijing Anzhen hospital, Beijing, China
| | - Yicong Ye
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China
| | - Xiliang Zhao
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China
| | - Yong Zeng
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China.
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Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects. Updates Surg 2020; 72:355-378. [PMID: 32306277 DOI: 10.1007/s13304-020-00771-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022]
Abstract
Gastric cancer is the fifth malignancy and the third cause of cancer death worldwide, according to the global cancer statistics presented in 2018. Its definition and staging have been revised in the eight edition of the AJCC/TNM classification, which took effect in 2018. Novel molecular classifications for GC have been recently established and the process of translating these classifications into clinical practice is ongoing. The cornerstone of GC treatment is surgical, in a context of multimodal therapy. Surgical treatment is being standardized, and is evolving according to new anatomical concepts and to the recent technological developments. This is leading to a massive improvement in the use of mini-invasive techniques. Mini-invasive techniques aim to be equivalent to open surgery from an oncologic point of view, with better short-term outcomes. The persecution of better short-term outcomes also includes the optimization of the perioperative management, which is being implemented on large scale according to the enhanced recovery after surgery principles. In the era of precision medicine, multimodal treatment is also evolving. The long-time-awaited results of many trials investigating the role for preoperative and postoperative management have been published, changing the clinical practice. Novel investigations focused both on traditional chemotherapeutic regimens and targeted therapies are currently ongoing. Modern platforms increase the possibility for further standardization of the different treatments, promote the use of big data and open new possibilities for surgical learning. This systematic review in two parts assesses all the current updates in GC treatment.
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Weston WW, Ganey T, Temple HT. The Relationship between Exosomes and Cancer: Implications for Diagnostics and Therapeutics. BioDrugs 2019; 33:137-158. [DOI: 10.1007/s40259-019-00338-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Zhong S, Chen C, Liu N, Yang L, Hu Z, Duan P, Shuai D, Zhang Q, Wang Y. Overexpression Of hsa-miR-664a-3p Is Associated With Cigarette Smoke-Induced Chronic Obstructive Pulmonary Disease Via Targeting FHL1. Int J Chron Obstruct Pulmon Dis 2019; 14:2319-2329. [PMID: 31632001 PMCID: PMC6790409 DOI: 10.2147/copd.s224763] [Citation(s) in RCA: 17] [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: 07/25/2019] [Accepted: 09/12/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is recognized as a chronic lung disease with incomplete reversible airflow limitation, but its pathophysiology was still not clear. This study aimed at investigating regulatory roles of special miRNA-mRNA axis in COPD development. METHODS Differentially expressed miRNAs and downstream mRNAs were screened from the Gene Expression Omnibus (GEO) dataset by using the LIMMA package in R software. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct a co-expression network for COPD. The correlation of dysregulated miRNA(s) and COPD was analyzed, and miRNAs with significant differences were validated in peripheral blood mononuclear cells (PBMCs) from COPD patients by real-time PCR. Regulatory roles of candidate miRNAs and targeted mRNAs were investigated in vitro study. RESULTS Thirteen modules of co-expressed miRNAs and mRNAs were constructed from a selected cohort with WGCNA. Turquoise module with 12 differentially expressed miRNAs and 120 mRNAs was significantly correlated with COPD. The expression of hsa-miR-664a-3p, an upregulated miRNA in the module, was increased both in lung tissue and PBMCs from COPD patients, whereas that targeted four and a half LIM domains 1 (FHL1) gene was decreased and positively correlated with forced expiratory volume in 1 sec (FEV1)/forced vital capacity (FVC%) (r = 0.59, p < 0.01). In vitro, luciferase activity assay revealed FHL1 as a target of hsa-miR-664a-3p and it could be directly downregulated by overexpression of hsa-miR-664a-3p. Furthermore, cigarette smoke extract could increase hsa-miR-664a-3p level and decrease FHL1 level in Beas-2B cells. CONCLUSION The present study validated significant upregulation of hsa-miR-664a-3p in COPD patients, and its target gene FHL1 was downregulated and positively correlated with FEV1/FVC%; both hsa-miR-664a-3p and FHL1 could be regulated by cigarette smoke extract. Results of bioinformatic analyses and expanded validation suggest that the axis from hsa-miR-664a-3p to FHL1 might play a key role in cigarette smoke-induced COPD, and the exact mechanism should be confirmed in further studies.
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Affiliation(s)
- Shan Zhong
- Department of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong515041, People’s Republic of China
- Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong518055, People’s Republic of China
| | - Chengshui Chen
- Department of Respiratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang325000, People’s Republic of China
| | - Naijia Liu
- Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong518055, People’s Republic of China
| | - Li Yang
- Department of Respiratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang325000, People’s Republic of China
| | - Zhangli Hu
- Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong518055, People’s Republic of China
| | - Pengfei Duan
- Department of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong515041, People’s Republic of China
| | - Diquan Shuai
- Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong518055, People’s Republic of China
| | - Qingying Zhang
- Department of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong515041, People’s Republic of China
- Qingying Zhang Department of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong515041, People’s Republic of ChinaTel +86 754 8825 9850Fax +86 754 8856 6774 Email
| | - Yun Wang
- Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong518055, People’s Republic of China
- Correspondence: Yun Wang Center for Research and Technology of Precision Medicine, College of Life Sciences and Oceanography, Shenzhen University (Xili Campus), No. 1066, Xueyuan Ave, Nanshan Distract, Shenzhen, Guangdong518055, People’s Republic of ChinaTel +86 755 2695 8895Fax +86 755 2653 4274 Email
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Li D, Hao X, Song Y. Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioinformatics Methods. BIOMED RESEARCH INTERNATIONAL 2018; 2018:6204128. [PMID: 30027097 PMCID: PMC6031162 DOI: 10.1155/2018/6204128] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 05/22/2018] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer. METHODS Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data. RESULTS We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival. CONCLUSIONS We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.
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Affiliation(s)
- Dongyang Li
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Xuanyu Hao
- Department of Rheumatology and Immunology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, China
| | - Yongsheng Song
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
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Liu X, Qu J, Xue W, He L, Wang J, Xi X, Liu X, Yin Y, Qu Y. Bioinformatics-based identification of potential microRNA biomarkers in frequent and non-frequent exacerbators of COPD. Int J Chron Obstruct Pulmon Dis 2018; 13:1217-1228. [PMID: 29713155 PMCID: PMC5909781 DOI: 10.2147/copd.s163459] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Objectives MicroRNAs (miRNAs) play essential roles in the development of COPD. In this study, we aimed to identify and validate potential miRNA biomarkers in frequent and non-frequent exacerbators of COPD patients using bioinformatic analysis. Materials and methods The candidate miRNA biomarkers in COPD were screened from Gene Expression Omnibus (GEO) dataset and identified using GEO2R online tool. Then, we performed bioinformatic analyses including target prediction, gene ontology (GO), pathway enrichment analysis and construction of protein–protein interaction (PPI) network. Furthermore, the expression of the identified miRNAs in peripheral blood monocular cells (PBMCs) of COPD patients was validated using quantitative real-time polymerase chain reaction (qRT-PCR). Results MiR-23a, miR-25, miR-145 and miR-224 were identified to be significantly downregulated in COPD patients compared with healthy controls. GO analysis showed the four miRNAs involved in apoptotic, cell differentiation, cell proliferation and innate immune response. Pathway analysis showed that the targets of these miRNAs were associated with p53, TGF-β, Wnt, VEGF and MAPK signal pathway. In healthy controls, the miR-25 and miR-224 levels were significantly decreased in smokers compared with nonsmokers (P<0.001 and P<0.05, respectively). In COPD patients, the levels of miR-23a, miR-25, miR-145 and miR-224 were associated with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages. Notably, miR-23a and miR-145 were significantly elevated in non-frequent exacerbators compared with frequent exacerbators (P<0.05), and miR-23a showed higher area under the receiver–operator characteristic curve (AUROC) than miR-145 (0.707 vs 0.665, P<0.05). Conclusion MiR-23a, miR-25, miR-145 and miR-224 were associated with the development of COPD, and miR-23a might be a potential biomarker for discriminating the frequent exacerbators from non-frequent exacerbators.
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Affiliation(s)
- Xiao Liu
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Jingge Qu
- Department of Rheumatology, Second Hospital of Harbin Medical University, Harbin, People's Republic of China
| | - Weixiao Xue
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Liangai He
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Jun Wang
- Department of Respiratory Medicine, Second Hospital of Shandong Traditional Chinese Medicine University, Jinan, People's Republic of China
| | - Xuejiao Xi
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Xiaoxia Liu
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Yunhong Yin
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | - Yiqing Qu
- Department of Respiratory Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
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Schnell A, Schmidl C, Herr W, Siska PJ. The Peripheral and Intratumoral Immune Cell Landscape in Cancer Patients: A Proxy for Tumor Biology and a Tool for Outcome Prediction. Biomedicines 2018; 6:E25. [PMID: 29495308 PMCID: PMC5874682 DOI: 10.3390/biomedicines6010025] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 02/18/2018] [Accepted: 02/22/2018] [Indexed: 02/06/2023] Open
Abstract
Functional systemic and local immunity is required for effective anti-tumor responses. In addition to an active engagement with cancer cells and tumor stroma, immune cells can be affected and are often found to be dysregulated in cancer patients. The impact of tumors on local and systemic immunity can be assessed using a variety of approaches ranging from low-dimensional analyses that are performed on large patient cohorts to multi-dimensional assays that are technically and logistically challenging and are therefore confined to a limited sample size. Many of these strategies have been established in recent years leading to exciting findings. Not only were analyses of immune cells in tumor patients able to predict the clinical course of the disease and patients' survival, numerous studies also detected changes in the immune landscape that correlated with responses to novel immunotherapies. This review will provide an overview of established and novel tools for assessing immune cells in tumor patients and will discuss exemplary studies that utilized these techniques to predict patient outcomes.
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Affiliation(s)
- Annette Schnell
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, 93053 Regensburg, Germany.
| | - Christian Schmidl
- Regensburg Centre for Interventional Immunology and University Medical Center of Regensburg, 93053 Regensburg, Germany.
| | - Wolfgang Herr
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, 93053 Regensburg, Germany.
- Regensburg Centre for Interventional Immunology and University Medical Center of Regensburg, 93053 Regensburg, Germany.
| | - Peter J Siska
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, 93053 Regensburg, Germany.
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