Investigating key genes and molecular mechanisms of prostate cancer and coronary heart disease through transcriptomics and experimental validation.

Background: Prostate cancer (PC), a common male urogenital malignancy, and coronary heart disease (CHD), a cardiovascular disease from coronary lesions causing myocardial ischemia, interact in comorbidity. This study integrated their transcriptome data to reveal comorbid mechanisms and develop cross-disease targets. Methods: In this research, candidate genes were derived from differential analysis and intersection analysis. Subsequently, machine learning algorithms were integrated with receiver operating characteristic (ROC) curve assessment and expression confirmation to identify key genes. Nomograms were further constructed, and analyses were carried out on the subcellular and chromosomal localization, enrichment pathways, molecular regulatory networks, and immune infiltration of these key genes. Potential drugs were predicted and molecular docking was performed. Ultimately, to confirm whether the expression patterns of key genes in clinical samples aligned with the bioinformatics analysis results, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted. Results: A total of 84 candidate genes were identified using bioinformatics approaches in this study. Through machine learning and validation with multiple datasets, Conclusions: This study determined the key genes related to PC and CHD, providing new bases and targets for diagnosis, treatment, and drug development.