Osteoarthritis (OA) is a common chronic joint disease, and cadmium (Cd) exposure may contribute to its progression. This study integrated network toxicology, transcriptomic datasets from the Gene Expression Omnibus (GEO), weighted gene co-expression network analysis (WGCNA), and machine-learning methods to identify molecular signatures potentially associated with Cd exposure-related OA. A total of 740 overlapping targets between Cd-associated genes and OA-related genes were identified. These targets were primarily enriched in oxidative stress, inflammation, apoptosis, extracellular matrix degradation, and the phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), mitogen-activated protein kinase (MAPK), tumor necrosis factor (TNF), nuclear factor kappa B (NF-κB), interleukin 17 (IL-17), and hypoxia-inducible factor 1 (HIF-1) signaling pathways. Integrated transcriptomic analysis identified 174 differentially expressed genes (DEGs), and WGCNA identified the yellow module as the module most strongly associated with OA. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest analyses identified BCL6 and FOSL2 as robust diagnostic feature genes. Both genes were consistently down-regulated in OA samples and demonstrated strong diagnostic performance across the analyzed cohorts. These findings suggest that BCL6 and FOSL2 may be potential diagnostic biomarkers of Cd-associated OA.
山东省济南市章丘区文博路2号
齐鲁师范学院 genelibs生信实验室
山东省济南市高新区舜华路750号
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