Identification and Experimental Verification of PDK4 as a Potential Biomarker for Diagnosis and Treatment in Rheumatoid Arthritis.

backgroundRheumatoid arthritis (RA) is a chronic autoimmune disorder marked by sustained joint inflammation, with an etiology that remains elusive. Achieving an early and precise diagnosis poses significant challenges. This study aims to elucidate the molecular pathways involved in RA pathogenesis by screening genes associated with its occurrence, analyzing the related molecular activities, and ultimately developing more effective molecular-level treatments for RA.

methodsMicroarray expression profiling datasets GSE1919, GSE10500, GSE15573, GSE77298, GSE206848, and GSE236924 were sourced from the Gene Expression Omnibus (GEO) database. Samples were divided into experimental (RA) and control (normal) groups. Differentially expressed genes (DEGs) were identified using R software packages such as limma, glmnet, e1071 as well as randomForest. Cross-validation of DEGs was conducted using lasso regression and the random forest (RF) algorithm in R software to pinpoint intersecting genes that met the criteria. Among these, one gene was selected as the target for correlation analysis to identify DEGs related to the target gene. Enrichment analysis utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases and Gene Ontology (GO) data. Gene Set Enrichment Analysis (GSEA) was performed to compare the expression levels of the target gene (PDK4) across various biological pathways and functions in groups with high and low expression. The relationship between target gene expression levels and cellular immune function was assessed using the immune function score technique. The discrepancy in immune cell distribution between the control and experimental groups, as well as their correlation with target gene expression levels, was elucidated using CIBERSORT. The relationships between mRNA, lncRNA, and miRNA were depicted in the ceRNA regulation network. The expression levels of the target gene were validated using Western blot and qRT-PCR.

resultsIn this study, six intersecting genes meeting the criteria were identified through cross-validation, and PDK4 was chosen as the target gene for further investigation. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that PDK4-associated DEGs are primarily enriched in the PPAR signaling pathway, thereby regulating synovial cell proliferation and migration, ultimately influencing the onset and progression of rheumatoid arthritis (RA). Immune infiltration analysis suggested that eosinophil quantity may influence the progression of RA. Experimental results from PCR and Western blot confirmed the downregulation of PDK4 in the RA group.

conclusionThe significant downregulation of PDK4 expression in patients diagnosed with rheumatoid arthritis (RA) was confirmed. PDK4 may function as a novel regulatory factor in the onset and progression of RA, with potential applications as a diagnostic biomarker for the condition.