UQCR10 and TNNT1: novel biomarkers for sarcopenia identified through integrated transcriptomic analysis and machine learning.

ABSTRACT:

Background

Sarcopenia, the age-related loss of muscle mass and function, significantly impacts health outcomes in older adults. Despite its prevalence, molecular diagnostics and targeted therapies remain limited due to incomplete understanding of its pathophysiology.

Methods

We analyzed the GSE226151 dataset comparing skeletal muscle transcriptomes from sarcopenic (n = 20) and healthy adults (n = 20) using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Key genes were identified through the intersection of differentially expressed genes and hub genes. Machine learning feature selection (LASSO, XGBoost, and Random Forest) was employed to identify optimal biomarkers, followed by validation in an independent cohort (GSE111016, n = 40,

SUBMITTER: Sun W 

PROVIDER: S-EPMC12667071 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature