Construction and validation of a diagnostic model for rheumatoid arthritis based on mitochondrial autophagy-related genes

Abstract

Rheumatoid arthritis (RA) is an autoimmune disease associated with an increased risk of disability. Due to its slow progression, timely diagnosis and treatment during the early stages can effectively decelerate disease advancement. Consequently, there is a pressing need to investigate additional biomarkers and therapeutic targets relevant to RA diagnosis. Mitochondrial autophagy, a biological process that regulates the quantity of mitochondria, is intricately linked to the development of tumor diseases. However, the role of autophagy in RA remains unclear. To address this, transcriptome data from the GEO database were collected for RA and its controls and subjected to differential expression analysis. The differentially expressed genes obtained were then intersected with mitochondrial autophagy-related genes. Subsequently, the overlapping genes were further intersected with genes from critical modules obtained through the weighted co-expression network algorithm. Diagnostic genes were identified, and diagnostic models were constructed for the resulting genes using the random forest and LASSO algorithms. The model achieved an AUC of 0.916 in the GSE93272 dataset and 0.951 in the GSE17755 dataset. Additionally, qPCR experiments were conducted on the diagnostic genes. Finally, we explored the correlation between the abundance of immune cell infiltration and diagnostic genes, constructing a drug-gene interaction network. The diagnostic genes identified in this study can serve as a reference for early diagnosis and the discovery of therapeutic targets in RA.