Identification of inflammation‑related biomarkers for osteoarthritis diagnosis and stratification through bulk and single‑cell RNA‑sequencing integration

Osteoarthritis (OA) is a degenerative joint disease, which cannot be cured with present treatment methods. Increasing evidence implicates inflammation in OA pathogenesis, and this has led to investigations into inflammation‑related biomarkers (TNF‑a, IL‑6 and IL‑1b) that may guide diagnosis and targeted interventions. In the present study, a total of six Gene Expression Omnibus transcriptomic datasets, including 60 OA and 39 normal samples, were analyzed. Intersecting differentially expressed genes (DEGs) with an inflammatory response gene set defined inflammation‑related DEGs (IRDGs). Consensus clustering, gene set variation analysis and estimation of stromal and immune cells in malignant tumor tissues using expression data/cell‑type identification by estimating relative subsets of RNA transcripts immune profiling were also carried out. The features were reduced by least absolute shrinkage and selection operator (LASSO) and modeled using generalized linear models, random forests, support vector machines (SVM) and extreme gradient boosting. Single‑cell data were subjected to Seurat clustering, SingleR annotation, Monocle pseudotime and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment. Additionally, following modulation of low‑density lipoprotein receptor (LDLR), adrenomedullin (ADM), MYC or NF‑κB inhibitor‑α (NFKBIA), the viability and apoptosis of ATDC5 cells were assessed. Subsequently, a total of 537 DEGs and 11 IRDGs were identified in the present study. In addition, two OA subtypes, cluster C1 and C2, were identified. Presenilin 1 expression was increased in cluster C2, while the expression of the other IRDGs was upregulated in cluster C1. Only the stromal scores differed significantly. LASSO and machine learning nominated four biomarkers, MYC, ADM, LDLR and NFKBIA, with SVM providing the best overall and robust external validation. Single‑cell analysis of 1,464 chondrocytes revealed broad NFKBIA expression across nine subpopulations. Furthermore, it was demonstrated that downregulation of LDLR, ADM, MYC and NFKBIA reduced cell viability and induced apoptosis in ATDC5 chondrocytes. Integrative bulk single‑cell transcriptomics and machine learning identified MYC, ADM, LDLR and NFKBIA as inflammation‑associated OA biomarkers, revealing subtype‑specific immune heterogeneity. Clinically, this signature may possibly enable earlier diagnosis, patient stratification and targeted interventions to slow cartilage degeneration.