The Skeletal Muscle Transcriptome Profile of Elderly Men with Metabolic Syndrome Based on Weighted Gene Co-Expression Network Analysis

Abstract

Introduction: This study aimed to understand the transcriptome characteristics of the skeletal muscle of elderly (EL) men with metabolic syndrome (MS) and to find the hub genes and insight into the molecular mechanisms of skeletal muscle in the occurrence and development of MS. Methods: In this study, the limma package of R software was used to analyze the differentially expressed genes in the skeletal muscle of healthy young (YO) adult men, healthy EL men, and EL men diagnosed with MS (SX) for at least 10 years. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis, and gene interaction network analysis, were used to explore the biological functions of differentially expressed genes, and weighted gene coexpression network analysis (WGCNA) was used to cluster differentially expressed genes into modules. Results: Among the YO group, EL group, and SX group, 65 co-differentially expressed genes were found maybe regulated by age factor and MS factor. Those co-differentially expressed genes were enriched into 25 biological process terms and 3 KEGG pathways. Based on the WGCNA results, a total of five modules were identified. Fifteen hub genes may play an essential role in regulating the function of skeletal muscle of EL men with MS. Conclusions: 65 differentially expressed genes and 5 modules may regulate the function of skeletal muscle of EL men with MS, among which fifteen hub genes may play an essential role in the occurrence and development of MS.