Unveiling shared PANoptosis mechanisms in LUAD and osteoarthritis via bioinformatics and machine learning

Lung adenocarcinoma (LUAD) and osteoarthritis (OA) pose significant therapeutic challenges due to their invasive heterogeneity and limited treatment options. This study investigates PANoptosis-related genes in LUAD and OA to develop a prognostic risk model using bioinformatics and machine learning. We obtained gene expression data from TCGA, GEO, and MSigDB, then applied consensus clustering to stratify LUAD samples and identified PANoptosis-associated differentially expressed genes (DEGs). For OA, ssGSEA and WGCNA were used to pinpoint hub genes linked to PANoptosis. Functional enrichment analyses (GO/KEGG) revealed key pathways in both diseases. By intersecting LUAD DEGs and OA hub genes, we refined candidate genes using LASSO and Random Forest algorithms, ultimately selecting five key genes (S100A3, PFN2, DEFB1, TSPO, and KMO) for model construction. The prognostic model demonstrated robust predictive performance. Additionally, immune infiltration and mutational profiling in LUAD provided mechanistic insights for potential therapeutic strategies. Our findings highlight shared PANoptosis-related pathways in LUAD and OA, offering a novel framework for risk stratification and targeted therapy.