Bioinformatics on Screening and Pathway Analysis of Early Diabetic Nephropathy Related Genes - Sang, Yao (Transactions on Materials, Biotechnology and Life Sciences, 2024)
Diabetic nephropathy (DN), a severe complication arising from diabetes, necessitates timely diagnosis and therapeutic intervention to mitigate the disease’s progression. The objective of this article is to identify pivotal genes linked to the early stages of DN through the application of bioinformatics techniques. Furthermore, this this article endeavors to elucidate the signaling pathways associated with these genes, thereby offering novel therapeutic targets for the preemptive identification and management of DN. Methods: this article obtained the relevant gene chip data from the public dataset Gene Expression Omnibus (GEO) database, and obtained the information derived from the analysis of the complete set of genes being expressed of kidney tissue of DN patients and healthy controls. The R programming language, in conjunction with the Bioconductor package, was employed to perform an analysis of gene expression variations, with the aim of identifying obvious differential expressed genes (DEGs). Functional annotation and enrichment analysis: Functional categorization and enrichment of DEGs were conducted utilizing the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, offering a deeper understanding of the molecular functions and pathways related to these genes. Protein-protein interaction (PPI) network construction: PPI networks of DEGs were constructed adopting search tool for the retrieval of interacting genes (STRING) database, and network visualization and key gene screening were performed by Cytoscape software. Other independent gene expression datasets and literature reports were used to verify the selected key genes. The results suggested that GSE176230 dataset was retrieved and downloaded from CEO database. After screening, there were 1,715 DEGs in DN and control samples, of which 1,026 were up-regulated and 689 were down-regulated. DEGs markedly associated with early DN (PBMCs, CXCL8, MMP9, 1L1B, C-C chemokine receptor type 2 (CCR2), TLR10, CX3CR1, P2RY14, APAF1, GREM1, FAM30A, PRKY, TMSB4Y, GDF3) were screened out. GO analysis indicated that a visible enrichment of these genes was observed in key biological processes, including inflammatory response, oxidative stress, apoptosis. KEGG pathway analysis suggested that the main signaling pathways involved included TGF-β, MAPK, and PI3K-Akt. PPI network analysis identified multiple key genes (CXCL8, MMP9, and CCR2). The mRNA levels of CXCL8, MMP9, and CCR2 in patients with early DN were markedly higher as against normal healthy people (P < 0.05). Utilizing bioinformatics techniques, this article effectively identified pivotal genes and associated signaling pathways that are linked to the early stages of DN. The insights gained from this article offer novel therapeutic targets and a foundational framework for the preemptive identification and management of DN. Upcoming research endeavors are poised to confirm the particular roles and mechanisms of these genetic elements and molecular pathwaysy.