{"title":"Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis.","authors":"Xianyang Zhu, Wen Guo","doi":"10.1159/000517308","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms.</p><p><strong>Methods: </strong>Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD).</p><p><strong>Results: </strong>A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including \"Lysosome,\" \"Pathways in cancer,\" \"Wnt signaling pathway,\" \"ECM-receptor interaction\" and \"Focal adhesion\" in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified.</p><p><strong>Conclusion: </strong>In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.</p>","PeriodicalId":49650,"journal":{"name":"Public Health Genomics","volume":" ","pages":"267-279"},"PeriodicalIF":1.3000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Public Health Genomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000517308","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/8/2 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 2
Abstract
Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms.
Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD).
Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including "Lysosome," "Pathways in cancer," "Wnt signaling pathway," "ECM-receptor interaction" and "Focal adhesion" in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified.
Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.
背景:本研究旨在筛选和验证参与骨关节炎(OA)的关键基因，并探讨其潜在的分子机制。方法:从Gene expression Omnibus (GEO)下载与OA相关的4个表达谱数据集。通过荟萃分析方法鉴定4种微阵列模式的差异表达基因(DEGs)。采用加权基因共表达网络分析(WGCNA)方法对与OA关系最密切的稳定模块进行分析。此外，构建了蛋白-蛋白相互作用(PPI)网络，探索OA中的枢纽基因。此外，从比较毒物基因组学数据库(CTD)检索oa相关基因和途径。结果:从4个数据集中共鉴定出1136个deg。基于这些deg, WGCNA进一步探索了3个oa相关稳定模块中包含的370个基因。在PPI网络中共鉴定出10个枢纽基因，包括AKT1、CDC42、HLA-DQA2、TUBB、TWISTNB、GSK3B、FZD2、KLC1、GUSB和RHOG。此外，还鉴定出CTD中的“溶酶体”、“肿瘤通路”、“Wnt信号通路”、“ecm受体相互作用”、“局灶黏附”等5条通路和富集分析，以及5个与oa相关的枢纽基因(包括GSK3B、CDC42、AKT1、FZD2、GUSB)。结论:在本研究中，荟萃分析用于筛选各种基因表达谱中与OA相关的中心基因。通过WGCNA鉴定出3个oa相关模块(绿色、绿松石色和黄色)，共包含370个基因。通过基因通路网络发现GSK3B、CDC42、AKT1、FZD2和GUSB可能是与OA进展相关的关键基因，可能成为有希望的治疗靶点。
期刊介绍：
''Public Health Genomics'' is the leading international journal focusing on the timely translation of genome-based knowledge and technologies into public health, health policies, and healthcare as a whole. This peer-reviewed journal is a bimonthly forum featuring original papers, reviews, short communications, and policy statements. It is supplemented by topic-specific issues providing a comprehensive, holistic and ''all-inclusive'' picture of the chosen subject. Multidisciplinary in scope, it combines theoretical and empirical work from a range of disciplines, notably public health, molecular and medical sciences, the humanities and social sciences. In so doing, it also takes into account rapid scientific advances from fields such as systems biology, microbiomics, epigenomics or information and communication technologies as well as the hight potential of ''big data'' for public health.
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