Screening of Potential Biomarkers and Immune Analysis for Osteoarthritis Based on Machine Learning and WGCNA

The real pathogenesis of osteoarthritis (OA) remains unknown, leaving a significant burden of social and medical experiences. Thus, this study aimed to identify potential novel biomarkers in OA. The OA dataset (GSE55235) was from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) for filtering the dataset to generate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to explore functional biology and related diseases. Subsequently, a further selection of latent biomarkers using three techniques (least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), and random forest (RF)). Receiver operating curve (ROC) of potential biomarkers were drawn to evaluate the diagnostic validity. The infiltration of immune cells for OA was evaluated using CIBERSORT, and the association with potential biomarkers and immune infiltrating cells was analyzed. Lastly, correlations and expression differences of potential biomarkers were investigated. In total, 803 DEGs were identified in OA and control samples. By overlapping DEGs and two module genes of WGCNA, we obtained 137 genes. LTC4S, XIST, CXCL8 and PIM1 were identified after validation by machine learning methods and ROC. Immune infiltration analysis demonstrated that T cells, and mast cells were linked to the pathogenesis of OA. The research might now help in understanding the etiology of OA.