Identification of PTPRR gene associated with cirrhosis and sarcopenia based on bioinformatics and machine learning.

backgroundCirrhosis and sarcopenia frequently coexist and are associated with poor clinical outcomes; however, their shared genetic basis remains incompletely understood.

methodsWe applied conditional and conjunctional false discovery rate (cFDR/ccFDR) analyses to genome-wide association study (GWAS) summary statistics for cirrhosis and sarcopenia-related traits, including appendicular lean mass (ALM) and usual walking pace (UWP). In parallel, weighted gene co-expression network analysis (WGCNA) and three machine learning algorithms (LASSO, random forest, and support vector machine-recursive feature elimination) were applied to liver and skeletal muscle transcriptomes. External validation was performed using independent transcriptomic cohorts. Two-sample Mendelian randomization (MR) was conducted to explore causal directions.

resultsGWAS-based pleiotropic analysis identified seven shared genetic loci for both conditions cirrhosis and sarcopenia. Transcriptomic and machine learning analyses prioritized eight shared candidate genes across liver and skeletal muscle tissues, among which PTPRR emerged as a convergent candidate identified by multiple analytical layers. Functional enrichment revealed pleiotropic loci were primarily associated with lipid metabolism and inflammatory pathways, whereas machine learning-derived genes were enriched in intracellular signaling and transcriptional regulation. MR analyses further suggested that genetically predicted higher ALM and faster UWP were associated with a lower risk of cirrhosis (inverse-variance weighted [IVW] P = 0.0127 and 0.0211, respectively).

conclusionsBy jointly reporting pleiotropic genetic loci and shared candidate genes, this study provides a multi-layered view of the genetic architecture underlying cirrhosis-sarcopenia comorbidity and supports the robustness of the identified gene signature across independent transcriptomic datasets, highlighting candidate molecular targets for future mechanistic investigation.