Bioinformatics and machine learning approaches to explore key biomarkers in muscle aging linked to adipogenesis.

Yumin Zhang, Li Qin, Juan Liu

Division of Geriatric Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China. [email protected] of Geriatric Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China. [email protected]. · pubmed

Paper

Abstract

Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expression profiles from biopsies of the vastus lateralis muscle in both young and elderly population were retrieved from the GEO database. After intersecting with the results of differential gene analysis, weighted gene co-expression network analysis, and sets of adipogenesis-related genes, 29 adipogenesis-related differential expressed genes (ARDEGs) were selected. Connectivity Map (cMAP) analysis identified tamsulosin, fraxidin, and alaproclate as key target compounds. In further, using three machine learning algorithms and the friends analysis, four hub ARDEGs, ESRRA, RXRG, GADD45A, and CEBPB were identified and verified in vivo aged mice muscles. Immune infiltration analysis showed a strong link between several immune cells and hub ARDEGs. In all, these findings suggested that ESRRA, RXRG, GADD45A, and CEBPB could serve as adipogenesis related biomarkers in muscle aging.

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Research summary

The study identifies potential biomarkers associated with adipogenesis in muscle aging. This research is relevant as it explores the biological mechanisms underlying muscle aging, which is a significant aspect of the aging process and could contribute to understanding longevity.

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