Application of Bioinformatics Methods to Identify Key Genes and Functions in Chronic Pelvic Pain

Abstract

Neuropathologic pain (NPP) occurs in most patients with chronic pelvic pain (CPP), and the unique physiological characteristics of visceral sensory neurons make the current analgesic effect of CPP patients not optimistic. Therefore, this study explored the possible biological characteristics of key genes in CPP through the bioinformatics method. CPP-related dataset GSE131619 was downloaded from Gene Expression Omnibus to investigate the differentially expressed genes (DEGs) between lumbar dorsal root ganglia (DRG) and sacral DRG, and the functional enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed to search subnet modules of specific biological processes, and then, the genes in the subnet were enriched by single gene set analysis. A CPP mouse model was established, and the expression of key genes were identified by qPCR. The results showed that 127 upregulated DEGs and 103 downregulated DEGs are identified. Functional enrichment analysis showed that most of the genes involved in signal transduction were involved in the pathway of receptor interaction. A subnet module related to neural signal regulation was identified in PPI, including CHRNB4, CHRNA3, and CHRNB2. All three genes were associated with neurological or inflammatory activity and are downregulated in the sacral spinal cord of CPP mice. This study provided three key candidate genes for CPP: CHRNB4, CHRNA3, and CHRNB2, which may be involved in the occurrence and development of CPP, and provided a powerful molecular target for the clinical diagnosis and treatment of CPP.