Analysis Identifies Four Potential Autophagy-Linked Biomarkers for IgAN

A leading cause of end-stage renal disease (ESRD) is immunoglobulin A nephropathy (IgAN). IgAN is an immune-related primary glomerular disease associated with a significant social and economic burden worldwide. An estimated 20% to 40% of patients with IgAN progress to ESRD within 10 to 20 years after diagnosis. Primary risk factors for the progression of IgAN are persistent proteinuria, hypertension, decline in renal function, and renal pathological damage.

The gold standard for diagnosing IgAN is renal biopsy, a procedure that carries several contraindications and risks of complications, creating concerns for some populations, including older adults and children. Citing the need for identification of biomarkers with high sensitivity and specificity for early diagnosis and management of IgAN, Sijia Ma, PhD, and colleagues in China conducted a study integrating multiple Gene Expression Omnibus (GEO) datasets with machine learning algorithms to identify autophagy-related biomarkers and delineate immune cell filtration patterns in IgAN.

The researchers performed differential expression profile analysis of biological samples of 45 patients with IgAN and 28 healthy patients in a control group. A total of 694 differentially expressed genes (DEGs) were screened out. Of those, 296 were upregulated genes and 398 were downregulated genes. There were significant differences in the expression levels of 12 autophagy DEGs (AT-DEGs) between the patients with IgAN and those in the control group.

Fifty-one potential biomarkers were revealed in a GeneSet Enrichment Analysis. The most active pathways in the IgAN group were asthma, allograft rejection, autoimmune thyroid disease, type 1 diabetes mellitus, and viral myocarditis. AT-DEGs were primarily related to the intrinsic apoptotic signaling pathway, cellular response to external stimuli, transcription repressor complex, and other cellular functions. Kyoto Encyclopedia of Genes and Genome pathways enriched by AT-DEGs mainly included biological metabolic pathways related to autophagy, while DisGeNET analysis revealed that these AT-DEGs were mainly related to immunological diseases.

Of the 12 candidate genes in the training dataset, six hub DE-ATG genes related to diagnosis were identified: FOS, CDKN1A, SIRT1, BAG3, EIF2AK3, and SERPINA1. A prediction model was constructed using those six genes. In receiver operating characteristic curve (ROC) analysis, four of the six hub genes had great diagnostic value.

In summary, the researchers said, “This study, for the first time, systematically analyzed the relationship between AT-DEGs and immune cell infiltration by integrating multiple GEO datasets, exploring the role of immune-related autophagy biomarkers in IgAN, thereby addressing a critical gap in this field. Through LASSO regression and cross-validation, four hub genes (SIRT1, BAG3, CDKN1A, and FOS) were identified as potential biomarkers, offering new possibilities for the noninvasive diagnosis of IgAN. The reliability of the bioinformatics analysis was preliminarily confirmed using ROC curve analysis and qRT-PCR [real-time quantitative polymerase chain reaction], laying the foundation for further research and generally providing a reference for early diagnosis and targeted drug research of IgAN.”

Source: Ma S, et al. Scientific Reports. doi:10.1038/s41598-025-98591-y