Bioinformatic gene analysis for potential biomarkers and therapeutic targets of diabetic nephropathy associated renal cell carcinoma

Abstract

Background: Numerous epidemiological studies have confirmed that diabetes can promote the development of malignant tumors. However, the relationship between renal cell carcinoma (RCC) and diabetic nephropathy (DN) is still controversial. This study aimed to investigate the genes that are co-expressed in DN and RCC in order to gain a better understanding of the relationship between these diseases, and to identify potential biomarkers and targets for the treatment of DN-related RCC. Methods: We evaluated the differentially expressed genes (DEGs) that are co-expressed in DN and RCC using a wide range of target prediction and analysis methods. Twenty-four genes were identified by intersecting the differential genes of 3 DN datasets and 2 RCC datasets. We predicted the micro-ribonucleic acids (miRNAs) of these genes that may be controlled using the miRNA Data Integration Portal (mirDIP) database, and rated them according to each data forecast based on the Comparative Toxicogenomics Database (CTD) and the StarBase database. Results: Four genes were associated with DN and RCC patients: the predicted miRNAs hsa-miR-200b-3p and hsa-miR-429 of fibronectin 1 (FN1); the predicted miRNA hsa-miR-29c-3p of collagen type 1 alpha 2 (COL1A2); the predicted miRNA hsa-miR-29c-3p of collagen type 3 alpha 1 (COL3A1); and the predicted miRNA hsa-miR-29a-3p and hsa-miR-200c-3p of glucose-6-phosphatase catalytic subunit (G6PC). These genes may serve as potential biomarkers or specific targets in the treatment of DN-related RCC. Conclusions: A significant correlation was identified between DN and RCC. The FN1, COL1A2, COL3A1, and G6PC genes could be novel biomarkers of DN-related RCC.