Targeting muscle wasting: Bioinformatics-derived ubiquitination genes as potential therapeutic targets for sarcopenia

Sarcopenia is a major health concern characterized by progressive loss of muscle mass and function among the elderly. Its prevalence ranges from 10% to 27% in individuals over 60 and increases further in those above 80. The condition reduces mobility and strength, increasing risks of falls, fractures, and other age-related health issues. The ubiquitin-proteasome system (UPS) plays a central role in muscle protein degradation and contribute to muscle wasting. However, therapeutic strategies targeting this pathway remain poorly understood. This study aimed to identify ubiquitination-related genes as potential therapeutic targets for sarcopenia using integrative bioinformatics analysis. This study utilized bioinformatics approaches to identify potential therapeutic targets for sarcopenia. Six GEO datasets (GSE136344, GSE28422, GSE1428, GSE8479, GSE9103, and GSE38718) were analyzed. Data were pre-processed, normalized, and batch effects were removed using the R packages "affy" and "sva." Differentially expressed genes were identified using the "limma" package. Weighted gene co-expression network analysis was performed to identify disease-associated genes. Functional enrichment analyses were conducted using the "clusterProfiler" package for Gene Ontology (GO) is a standardized, structured vocabulary (ontology) for describing the functions of genes and gene products across all species and Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database resource that integrates genomic, chemical, and systems-level functional information pathways. Differentially ubiquitinated genes were identified by intersecting differentially expressed genes with ubiquitination-related genes from MSigDB. Protein-protein interaction networks were constructed using STRING, and machine learning algorithms were applied to screen for diagnostic signature genes. Drug-gene interactions were analyzed using DGIdb, and molecular docking was performed using AutoDock. A total 734 differentially expressed genes were identified, including 373 up-regulated and 361 down-regulated genes. Gene set enrichment analysis revealed significant enrichment in 3 KEGG pathways. Weighted gene co-expression network analysis identified 1016 disease-associated genes for functional enrichment. Thirteen differentially ubiquitinated genes were identified, and 6 key diagnostic signature genes (CBLB, PSMD6, RNF115, SMAD3, UCHL3, and ZBTB16) were selected through machine learning. Five genes (CBLB, RNF115, SMAD3, UCHL3, and ZBTB16) exhibited significant differences between older and younger groups, with ROC AUC values > 0.7. Drug prediction identified ASPARTIC ACID and CETYLPYRIDINIUM as potential agents targeting CBLB and SMAD3. This study identified key differentially ubiquitinated genes with potential therapeutic implications for sarcopenia. The findings highlight the importance of the UPS in muscle wasting and provide a foundation for further mechanistic and therapeutic research.