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

IF 2.2 3区医学 Q2 ORTHOPEDICS

BMC Musculoskeletal Disorders Pub Date : 2025-03-22 DOI:10.1186/s12891-025-08528-9

Yumin Zhang, Li Qin, Juan Liu

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引用次数: 0

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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来源期刊

BMC Musculoskeletal Disorders 医学-风湿病学

CiteScore

3.80

自引率

8.70%

发文量

1017

审稿时长

3-6 weeks

期刊介绍： BMC Musculoskeletal Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of musculoskeletal disorders, as well as related molecular genetics, pathophysiology, and epidemiology. The scope of the Journal covers research into rheumatic diseases where the primary focus relates specifically to a component(s) of the musculoskeletal system.