SLC10A2 has prognostic implications for individuals afflicted with diabetic pancreatic cancer, and its associated molecules have the potential to serve as biomarkers for diabetic nephropathy

Abstract

Abstract Background Diabetic nephropathy (DN)is the primary precipitant of renal failure. Nevertheless, the gene expression profile of the kidney in diabetic nephropathy remains inadequately scrutinized, resulting in a paucity of robust diagnostic markers and therapeutic targets for DN. The principal objective of this investigation was to interrogate the molecular intricacies governed by bile acid transport-related genes in diabetic nephropathy, leveraging bioinformatics methodologies. The overarching aim is to discern potential key biomarkers, thereby advancing our understanding of the pathogenesis of DN and facilitating the identification of promising diagnostic and therapeutic avenues. Methods In this investigation, we utilized the RNA expression profiles from the GSE72540 dataset for diabetic nephropathy (DN) as the training set, with GSE57178 serving as the verification set. Our analysis focused on differentially expressed bile acid transport-related protein genes (DEPRGs), encompassing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) assessments. To unravel pivotal genes, we conducted protein-protein interaction analyses. Subsequently, employing the random forest and support vector machine (SVM) algorithms, we assessed these key genes, selecting those common to both methods to construct a robust DN diagnosis model. Model reliability and validity were evaluated through a nomogram model, and the composition of immune cells was estimated using CIBERSORT. Furthermore, we established a comprehensive drug-gene interaction network. Finally, leveraging data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, we gleaned pan-cancer information, identifying key genes for subsequent pan-cancer analyses. Results We identified 23 differentially expressed bile acid transport-related genes (DEBCTGs), and subsequent enrichment analysis revealed their predominant association with the inflammatory response and immune regulation. Furthermore, immune infiltration analysis demonstrated a significantly elevated mast cell activation rate and decreased naive rate of CD4 T cells in DN compared to normal tissues. Utilizing seven core genes (EGF, ATP8B1, SLC10A2, CYP3A4, ABCC3, JUN, and CASP3) derived from protein-protein interaction (PPI) and machine learning analyses, we constructed a diagnostic model for DN. Upon evaluation, the nomogram model exhibited robust reliability and validity. Receiver operating characteristic (ROC) curves for the seven genes underscored their pivotal role in the pathogenesis of DN. Subsequent gene correlation analysis, drug-gene interaction network exploration, and immune infiltration analysis culminated in the selection of SLC10A2 for pan-cancer scrutiny. Pancancer analysis revealed a significant correlation between SLC10A2 and the prognosis of patients across various tumors. Additionally, the observed patterns of immune cell infiltration suggest that SLC10A2 may serve as a potential intervention target for human diseases, including diabetic nephropathy and tumors. Conclusion Bile acid transporters emerge as crucial markers in diabetic nephropathy, with SLC10A2 assuming a pivotal role in this context. Notably, SLC10A2 exhibits divergent expression patterns across various tumors, demonstrating significant associations with both prognosis and immune infiltration.