Bioinformatics-Based Identification of Potential Hypoxia-Related Genes Associated With Peyronie’s Disease

Abstract

Hypoxia is one of the most important predisposing conditions for Peyronie’s disease (PD) and the pathogenetic mechanism is yet to be completely elucidated. This study applied bioinformatic approaches to select candidate hypoxia-related genes involved in the pathogenesis of PD. The Gene Expression Omnibus (GEO) data set GSE146500 was introduced to compare the transcriptional profiling between normal and PD samples. The differential expression of hypoxia-related gene was determined with R software. On the selected candidate genes, further functional analyses were applied, including protein–protein interactions (PPIs), gene correlation, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. A total of 66 candidate genes (24 candidates overexpressed in PD and 42 showing reduced expression in PD) were distinguished according to the differential expression between human fibroblast cells from normal and PD patients. The interactions among these candidate genes were recognized according to PPI analysis. The functional enrichment analyses revealed the potential modulatory functions of the candidate genes in some major biological processes, especially in glycolysis/gluconeogenesis and carbon metabolism. The findings would facilitate further study on the pathogenesis of PD, which might consequently promote the improvement of clinical strategies against PD.