Exploring the genetic characteristics of overweight-related osteoarthritis using machine learning.

This investigation employed a synergistic approach integrating bioinformatics and machine learning methodologies to scrutinize overweight-related osteoarthritis characteristic genes (OROCGs). The research team procured gene expression profiles from osteoarthritis (OA) patients' cartilage and meniscus, derived from GEO database datasets GSE98918 and GSE117999. These profiles underwent meticulous examination through differential gene expression (DEG) identification, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), support vector machine - recursive feature elimination (SVM-RFE), and single-sample gene set enrichment analysis (ssGSEA), culminating in the identification of six OROCGs. Furthermore, the study unveiled an augmented presence of myeloid-derived suppressor cells (MDSCs) and B cells in overweight-associated OA. The investigators formulated a diagnostic model encompassing pivotal genes related to DNA replication, chronic inflammation, and epigenetics, including CHTH18, CYSLTR2, HSF4, KDM6B, NR4A2, and UCKL1. The model's diagnostic precision was corroborated through receiver operating characteristic (ROC) curves and a nomogram applied to the test set and validation set GSE129147. This model efficaciously delineates the expression alterations and immune infiltration linked to overweight-related OA, thereby nominating these genes as prospective candidates for immunomodulatory therapeutic interventions.