Identification of programmed cell death associated key genes in benign prostatic hyperplasia and prostate cancer development by integrated bioinformatics analysis and machine learning.

Background: Up to now, the underlying molecular mechanisms of benign prostatic hyperplasia (BPH) and prostate cancer (PCa) remain unclear. This study aimed to identify programmed cell death (PCD) associated genes in BPH and PCa development by integrated bioinformatics analysis and machine learning using publicly available genomic datasets. Methods: The GSE119195 and GSE55597 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were obtained using the Limma package for differential expression analysis. The intersection of core genes was filtered using four machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, eXtreme gradient boosting (XGBoost), random forest, and Boruta]. Results: We identified 159 key genes from the intersection of two DEGs. Fifteen hub genes were obtained by intersecting 159 DEGs with PCD genes. Two hub genes [ Conclusions: We established a prediction model with a high predictive ability in the analyzed datasets. As a bioinformatics analysis, our study indicated that there are possible DEGs in the prostate, such as