Table 2_Integrated bioinformatics and mendelian randomization reveal a six-gene diagnostic signature and key role of CYP26B1 in sarcopenia.xlsx

BackgroundThe pathogenesis of sarcopenia involves complex molecular mechanisms, and treatment remains challenging, with a lack of reliable diagnostic biomarkers. The objective of this study is to identify biomarkers that may be linked to sarcopenia, examine how these biomarkers correlate with immune cell infiltration, and investigate the genes that exhibit a causal relationship with sarcopenia.
MethodsFour transcriptomic datasets were integrated to identify candidate biomarkers. Genes from the MEBrown module of weighted gene co-expression network analysis (WGCNA) analysis were cross-referenced with differentially expressed genes (DEGs). A diagnostic model was built using 113 machine learning algorithms, followed by protein-protein interaction (PPI) network analysis and SHapley Additive exPlanations (SHAP) evaluation. Immune cell quantification and correlation with sarcopenia-related genes were performed using CIBERSORT, while gene expression data was integrated with genome-wide association statistics (GWAS) and gene expression quantitative trait loci (eQTL) data. In vitro validation was carried out using C2C12 cells and quantitative polymerase chain reaction (qPCR) experiments.
ResultsWe found 318 DEGs. By comparing the WGCNA gene with these DEGs, we found 109 possible biomarkers, which are related to immune regulation, muscle cytoskeleton regulation and retinol metabolism. A six-gene diagnostic signature (FOXO1, ZBTB16, HOXB2, LYVE1, MGP, and CYP26B1) was developed using machine learning and PPI network analysis, achieving high predictive accuracy (AUC >0.80), with HOXB2 identified as the top predictor via SHAP analysis. CIBERSORT analysis showed the relationship between these genes and immune cell subsets, while Mendelian randomization (MR) analysis confirmed the causal relationship between the expression of CYP26B1 gene and the risk of sarcopenia. The result of qPCR analysis is the same as the mRNA expression found in Gene Expression Omnibus (GEO) data set.
ConclusionThis study identified a highly reliable six-gene diagnostic signature for sarcopenia. Mendelian randomization established CYP26B1 as the sole causal factor, linking retinoic acid metabolism to disease etiology. This dual evidence provides a robust six-gene diagnostic model and a prioritized therapeutic target, elucidating immune-metabolic mechanisms of sarcopenia. These findings offer new avenues for early diagnosis and metabolism-based precision therapy.