Abstract
INTRODUCTION: Sarcopenia is a common comorbidity in end-stage renal disease (ESRD) and is associated with increased risk of adverse clinical outcomes. The genetic mechanisms underlying sarcopenia in ESRD remain largely unclear. This study employed multi-omics bioinformatics approaches to elucidate potential genetic determinants. METHODS: Gene expression datasets GSE1428 and GSE142153 were retrieved from the Gene Expression Omnibus database to identify shared differentially expressed genes. Machine learning approaches were applied to pinpoint hub genes, followed by Mendelian randomization (MR) analyses to assess their associations with ESRD. Candidate therapeutic agents were subsequently predicted based on these hub genes. RESULTS: ADAM7 emerged as the principal hub gene, with MR analyses suggesting a protective role against ESRD and sarcopenia. Predicted therapeutics included arbutin, metribolone, and phenethyl isothiocyanate. Molecular docking studies revealed favorable interactions, with binding free energies consistently below 5.0 kcal/mol between ADAM7 and these compounds. CONCLUSION: Our findings identify ADAM7 as a potential biomarker and therapeutic target for ESRD-associated sarcopenia, offering insights for future intervention strategies.