Abstract
BACKGROUND: Erectile dysfunction (ED) and Parkinson's disease (PD) are prevalent conditions that considerably impair patients' quality of life. Emerging evidence suggests a potential relationship between ED and PD, possibly mediated by shared biological mechanisms. This research seeks to examine shared transcriptomic alterations and the underlying biological pathways associated with ED and PD. METHODS: Gene expression profiles related to ED and PD were derived from the Gene Expression Omnibus database, specifically the GSE2457 and GSE7621 datasets. Differentially expressed genes (DEGs) between patients and controls were identified through differential expression analysis. Functional enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology analyses, were carried out to uncover the biological roles of the identified DEGs. To refine and validate potential key genes, machine learning algorithms, such as support vector machine-recursive feature elimination and LASSO regression, were employed. Immune infiltration analysis was carried out to examine potential immune responses related to the identified genes. Additionally, miRNA-gene and protein-protein interaction networks were established. Finally, the reliability of the selected genes was validated through external and experimental verification. RESULTS: In total, 25 overlapping DEGs were identified between ED and PD. Functional enrichment analysis demonstrated that these DEGs were involved in such biological processes as redox homeostasis and neuronal cell body function. KEGG pathway analysis indicated significant enrichment in pathways such as adrenergic signaling, cGMP-PKG signaling. Machine learning algorithms further refined the candidate genes, with SHOX2 and PIK3R6 demonstrating strong diagnostic potential. Immune infiltration analysis demonstrated correlations between the gene expression levels and various immune cell types. The constructed miRNA-gene regulatory networks revealed possible post-transcriptional regulatory mechanisms that modulated the expression of these genes. Finally, the diagnostic performance of these genes was verified in external datasets, with their performance further confirmed by ROC analysis and experimental verification. CONCLUSION: This study identified the shared biological target between ED and PD through bioinformatics analyses. The key genes SHOX2 and PIK3R6 may serve as potential biomarkers. These results may offer new insights into the molecular mechanisms linking ED and PD.