Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis

Abstract

BackgroundMembranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive.MethodsIn this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer.ResultsWe conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers.ConclusionThis evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

“…The broad range of differential factors will be further narrowed down, followed by the validation using experimental methods or external patient cohorts [ 22 ], ultimately allowing for the identification of key genes and regulatory elements associated with kidney diseases [ 23–25 ]. For instance, to identify the key biomarkers, a recent study on membranous nephropathy (MN) and pan-cancer analysis [ 26 ] employed ML approaches to intersect a set of 318 senescence-related genes with 366 DEGs. This approach resulted in the identification of 13 senescence-related DEGs, leading to the discovery of six hub genes with further intersection and validation through immunohistochemical analysis of human renal biopsy tissues.…”