To establish and validate autophagy related biomarkers for the diagnosis of IgA nephropathy - Sijia Ma; Chao Wang; Jing Liu; Hangyu Duan; Xiaoxin Tian; Shijie Xu; Yu Zhang (Scientific Reports)
Abstract IgA nephropathy (IgAN) is one of the most common immune-related primary glomerular diseases. The pathological mechanism of this disease is complex, and the specific pathogenesis is still unclear. To obtain a comprehensive understanding of its molecular mechanism and to provide new perspectives regarding the detection and treatment of the disease, this study investigated the role of immune cells in IgAN, as well as the role of autophagy-related biomarkers in IgAN development. The original datasets GSE93798, GSE35487, GSE58539, GSE116626 and GSE115857 were downloaded from Gene Expression Omnibus (GEO) and were further integrated and analyzed. The differentially expressed genes (DEGs) between IgAN and healthy control (HC) group were identified by the “limma” R package. The gene ontology (GO) function, Kyoto Encyclopedia of Genes and Genome (KEGG) pathway, GeneSet Enrichment Analysis (GSEA) and DisGeNet enrichment were adopted to analyze the genes from the intersection of DEGs. The hub genes were screened by the square least absolute shrinkage and selection operator (LASSO) and cross validation. Immune cell infiltration was analyzed using CIBERSORT. The correlation between hub genes and infiltrating immune cells was calculated by R software. For the purpose of exploring the value of hub genes for diagnosing IgAN, a receiver operating characteristic (ROC) curve was constructed. Finally, Real-time quantitative polymerase chain reaction (qRT-PCR) was used to verify the relative mRNA level of the AT-DEGs. 12 DEGs were screened out. Enrichment analysis revealed that autophagy-related DEGs (AT-DEGs) were mainly related to intrinsic apoptotic signaling pathway, cellular response to external stimulus, transcription repressor complex and other cellular functions, KEGG pathways enriched by AT-DEGs mainly included biological metabolic pathways related to autophagy, while DisGeNET analysis showed that these AT-DEGs were mainly related to immunological diseases. The optimal six hub genes were obtained by lasso analysis as potential biomarkers for IgAN. ROC curve analysis showed that 4 of the 6 HUB genes had great diagnostic value. Immune infiltration results showed B cells memory, macrophages M2, NK cells activated, T cells CD4+ memory resting, and monocytes are the predominant immune cells with the development of IgAN. The qRT-PCR results showed that, compared to the NC group, SIRT1 mRNA expression in PBMCs from IgAN patients was significantly reduced, while BAG3, CDKN1A, and FOS mRNA levels were markedly elevated. SIRT1, BAG3, COKN1A and FOS can be considered as effective biomarkers related to autophagy for the diagnosis of IgAN. These findings suggest some potential new serum biomarkers for IgAN diagnosis.