The skeletal muscle transcriptome profile of elderly men with metabolic syndrome based on weighted gene co-expression network analysis

Introduction: This study aims to understand the transcriptome characteristics of the skeletal muscle of old man with metabolic syndrome (MS), 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 adult (YO) men, healthy elderly (EL) men, and elderly men diagnosed with metabolic syndrome (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 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 may be regulated by age factor and metabolic syndrome 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 old men with metabolic syndrome. Conclusions: 65 differentially expressed genes and 5 modules may regulate the function of skeletal muscle of old men with MS, among which fifteen hub genes may play an essential role in the occurrence and development of MS.