Identification and immune landscape of sarcopenia-related molecular clusters in inflammatory bowel disease by machine learning and integrated bioinformatics.

Sarcopenia, a prevalent comorbidity of inflammatory bowel disease (IBD), is characterized by diminished skeletal muscle mass and strength. Nevertheless, the underlying interconnected mechanisms remain elusive. This study identified distinct expression patterns of sarcopenia-associated genes (SRGs) across individuals with IBD and in samples of normal tissue. By analyzing SRG expression profiles, we effectively segregated 541 IBD samples into three distinct clusters, each marked by its unique immune landscape. To unravel the transcriptional disruptions underlying these clusters, the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was employed to spotlight key genes linked to each cluster. A diagnostic model based on four key genes (TIMP1, PLAU, PHLDA1, TGFBI) was established using Random Forest and LASSO (least absolute shrinkage and selection operator) algorithms, and validated with the GSE179285 dataset. Moreover, the GSE112366 dataset facilitated the exploration of gene expression dynamics within the ileum mucosa of UC patients pre- and post-Ustekinumab treatment. Additionally, insights into the intricate relationship between immune cells and these pivotal genes were gleaned from the single-cell RNA dataset GSE162335. In conclusion, our findings collectively underscored the pivotal role of sarcopenia-related genes in the pathogenesis of IBD. Their potential as robust biomarkers for future diagnostic and therapeutic strategies is particularly promising, opening avenues for a deeper understanding and improved management of these interconnected conditions.