Exp Gerontol. 2023 Jun 19:112244. doi: 10.1016/j.exger.2023.112244. Online ahead of print.

ABSTRACT

Sarcopenia is an age-related skeletal muscle disorder that causes falls, disability and death in the elderly, but its exact mechanism remains unknown. In this study, we merged three GEO datasets into the expression profiles of 118 samples and screened 22 differentially expressed genes (DEGs) as candidate genes. Pathway analysis demonstrated that the functional enrichment of DEGs is mainly in the cellular response to insulin stimulus, PPAR signaling pathway and other metabolism-related pathways. Then, we identified six key genes by machine learning, which were confirmed to be closely associated with sarcopenia by bioinformatics analysis. It was experimentally verified that SCD1 exhibits the most substantial alterations in the progression of sarcopenia with disturbed lipid metabolism and myosteatosis. In addition, the immune microenvironment of sarcopenia was found to be affected by these key genes, with Th17 cells down-regulated and NK cells up-regulated. Sarcopenic patients consequently presented a more significant systemic inflammatory state with higher CAR (p = 0.028) and PAR (p = 0.018). For the first time, we identified key genes in sarcopenia with high-throughput data and demonstrated that key genes can regulate the progression of sarcopenia by affecting the immune microenvironment. Among them, SCD1 may influence lipid metabolism and myosteatosis process. Screening of key genes and analyzing of immune microenvironment provide a more accurate target for treating sarcopenia.

PMID:37343810 | DOI:10.1016/j.exger.2023.112244