Identification and characterization of immunogenic cell death-related molecular clusters in rheumatoid arthritis

Abstract

Background: This study is to analyze the potential mechanisms of immunogenic cell death genes (ICDs) in rheumatoid arthritis (RA) using bioinformatics methods and identify potential biomarkers. Method: We utilized the GSE93777 dataset to systematically evaluate the differential expression and immune characteristics of ICDs in RA patients. Thus, molecular clusters related to ICD, immune cell infiltration, and biological characteristics were explored. Weighted gene co-expression network analysis (WGCNA) was then performed to identify cluster-specific differentially expressed genes. Subsequently, we employed a Support Vector Machine (SVM) machine learning model for prediction analysis, with validation conducted using the external dataset GSE15573. Results: A total of 52 differentially expressed ICDs were identified between healthy individuals and RA patients. Compared to healthy individuals, RA patients exhibited high infiltration of T cells CD4 memory activated, T cells gamma delta, Monocytes, and Neutrophils. The ICD subtypes in RA patients displayed significant heterogeneity in terms of immunity. Specifically, Cluster 2 demonstrated elevated immune scores and relatively high levels of immune infiltration. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that cluster-specific differentially expressed genes in Cluster 2 were closely associated with amino acid and glucose metabolism and degradation, as well as the biosynthesis of N-glycosylation. For the diagnosis of RA, the SVM machine model demonstrated optimal performance with relatively low residual and high area under the curve (AUC=0.998) and was validated using an external validation dataset (GSE15573, AUC=0.700). Analysis of the column chart model indicated that CKS2, NDUFB1, CHCHD1, MAGOH, and MAP7D1 could be used as diagnostic markers for RA diseases. Conclusion: This study systematically elucidates the complex relationship between ICD and RA disease and establishes a promising predictive model to evaluate the risk of ICD subtypes and pathological outcomes in RA patients.