Single-cell RNA sequencing and integrated bioinformatics reveal new mitochondrial biomarkers in sarcopenia.

Background: Sarcopenia, characterized by age-related skeletal muscle loss and dysfunction, affects approximately 10% of adults over 60 years worldwide. Current diagnostic methods often detect sarcopenia only after substantial muscle deterioration has occurred, highlighting the critical need for early diagnostic biomarkers. Methods: We conducted an integrated analysis of several public transcriptomic datasets (GSE1428, GSE117525, GSE167186, GSE111006, GSE111010, and GSE111016) employing differential gene expression analysis, weighted gene co-expression network analysis, and machine learning techniques. Single-cell RNA sequencing (scRNA-seq) was employed to determine cell type-specific expression. Quantitative PCR validated the findings in C2C12 myoblasts cultured under sarcopenia-like conditions. A nomogram-based predictive model was developed and assessed through ROC analysis and decision curve analysis. Results: We discovered a conserved three-gene mitochondrial signature (CHCHD10, SAMM50, MDH2) significantly dysregulated across multiple independent cohorts. Single-cell RNA sequencing identified distinct expression patterns across cell types, highlighting significant mitochondrial changes in myocytes. A nomogram model integrating these three genes demonstrated superior diagnostic accuracy (AUC = 0.883, 95% CI: 0.732-1.000) compared to conventional clinical parameters. Conclusion: Our findings suggest that a three-gene mitochondrial signature, comprising CHCHD10, SAMM50, and MDH2, could serve as a valuable biomarker for early sarcopenia diagnosis. This signature underscoring the contribution of mitochondrial dysfunction to muscle aging. By potentially bridging basic research with clinical application, this panel may offer novel targets for developing mitochondria-targeted therapies and monitoring their efficacy.