Computational Prediction of Diabetic Nephropathy-Associated Comorbidities and Their Key Genes by Analyzing RNA-Seq Data

Abstract

Diabetic Nephropathy (DN) is a complex health condition that leads to various kidney diseases. According to the World Health Organization, over 2 million deaths have been reported due to diabetes and diabetes related kidney diseases in 2021. And at the recent 78th World Health Assembly, chronic kidney disease was reported to be one of the most common diabetes-related complications, and projected that about 30 to 40% of people living with diabetes will develop the disease. The purpose of this study is to predict the comorbidities of DN and their associated key genes by analyzing publicly available gene expression data of advanced and early stages of diabetes affected kidney samples. First, we collected the gene expression data from the NCBI GEO repository. After preprocessing the data, we identified the highly expressed sample-specific genes for all the samples in the data. Then, we obtained diabetes affected kidney samples’ specific genes and investigated their biological processes, pathway activities, disease analyses, and PPI networks. Based on the results, comorbidities and their associated key genes have been predicted for the advanced and early stages of DN. In advanced DN, four diseases, i.e., glomerulonephritis, Ehlers-Danlos syndrome, kidney failure, and collagen disease, and their three key genes, i.e., COL1A1, COL5A1, and ITGB3, have been observed. COL1A1 is known as a potential biomarker of type 2 diabetes (T2D), COL5A1 has the capability to diagnose T2D, and ITGB3 is known as a predictor of T2D. In early DN, two diseases, i.e., amino acid metabolic disorder and proteinuria disease, and their three key genes, i.e., MAOA, MAOB, and DDC, have been examined. MAOA and MAOB expressions are required for insulin secretion, and their inhibition causes T2D. We didn’t find any relation of DDC with T2D; based on our analyses, it might be associated with T2D. The findings of this study might be helpful to predict, diagnose, and make treatment plans for diabetes-related kidney diseases and their complexities.