Background: Mitochondrial dysfunction is a significant contributor to sarcopenia, but the mechanism remains unclear. Methods: In the present study, we downloaded GSE117525 and GSE8479 datasets from Gene Expression Omnibus (GEO), then the weighted correlation network analysis (WGCNA) was used to construct scale-free co-expression networks respectively. The key genes of aging muscle were obtained by overlapping key modules of two networks. Receiver operating characteristic (ROC) curve was drawn to explore the diagnostic efficacy of key genes. Finally, a transcription factor-key gene network was constructed based on ChEA3 platform and hTFtarget database, and a miRNA-key gene network was constructed using starBase and the multimiR R package. Results: The most positively or negatively correlated modules of the two datasets were identified, and genes related to oxidative phosphorylation and mitochondrial ribosomal proteins were identified as key genes. The diagnostic values were confirmed with ROC curves by self-verification (GSE117525 and GSE8479) and external verification (GSE47881). Then, Yin Yang 1 (YY1) was identified as the most important transcription factor of the transcription factor-key gene network. In addition, miRNAs related to key genes were also predicted. Conclusion: The findings of the present study provide a novel insight into the pathological mechanism of sarcopenia.
Keywords: WGCNA; miRNA; mitochondria; sarcopenia; transcription factor.
Copyright © 2022 Fu, Kadeer, He and Feng.