Core autophagy genes and immune infiltration characteristics in rheumatoid arthritis: A bioinformatics study - Zining Peng; Qian Deng; Yuanbo Huang; Fanyu Meng; Yuan Long; Yuanyuan Wei; Weitian Yan; Xiaoyu Zhang; Jiangyun Peng; Zhaofu Li; Nian Liu (PLoS ONE, 2025)
Objective This study aims to identify autophagy-related biomarkers in rheumatoid arthritis (RA) synovium, analyze their immune infiltration characteristics, and validate therapeutic potential through multi-level experimental approaches. Methods We used public datasets to obtain synovial tissue genes of healthy people and RA patients, screened differentially expressed genes (DEGs) of RA, and intersected with the human autophagy gene database (HADb) to obtain RA autophagy genes. GO and KEGG enrichment analysis and single-gene genome enrichment analysis were performed. The diagnostic value of RA core autophagy genes in the validation set was screened and verified. The immune cell correlation analysis of RA autophagy core genes was performed to obtain the correlation between single disease autophagy core genes and immune cells. Finally, we prepared CIA rat models to verify the autophagy protein P62, Beclin-1 and the 11 core genes associated with RA-autophagy. Results A total of 1098 RA DEGs were obtained. Intersecting with 222 autophagy genes obtained from the HADb database yielded 27 RA autophagy genes. GO analysis of RA autophagy genes showed 307 biological mechanisms. KEGG enrichment analysis obtained 86 signaling pathways, including FoxO, Necroptosis and other pathways related to RA autophagy. GSEA analysis found that the control group had a higher correlation with adipokine signaling pathways and others. And 11 RA autophagy-related core genes (IFNG, EGFR, MYC, CXCR4, MAPK8, CASP1, TNFSF10, CTSB, FAS, FOXO1, FOXO3) were obtained by screening the PPI network, and there were differences in expression in the training set (P < 0.001). External validation set verification showed diagnostic efficacy. Analysis of immune infiltration in RA autophagy-related genes revealed 14 immune cell types differentially abundant in synovial tissues of RA patients vs. normal controls. Significant correlations exist between autophagy genes and immune subsets. Finally, animal experiments showed that joint autophagy was enhanced (P < 0.001), and the mRNA of 11 RA-autophagy core genes had significant changes (P < 0.001). Conclusion We systematically identified 11 autophagy-related core genes as potential therapeutic targets for RA. Our CIA model validation provides preclinical evidence supporting their translational potential. These genes showed significant correlations with 14 synovial immune cell subtypes, may serve as novel therapeutic targets by modulating immune infiltration and inflammatory pathways. Future investigations should focus on elucidating the mechanistic basis of the observed gene-immune cell interactions in both autophagic and immune pathways to facilitate the development of precision therapies.