Identification of biomarkers related to prostatic hyperplasia based on bioinformatics and machine learning

Abstract

<abstract> <p>In older adults, benign prostatic hyperplasia (BPH) is the most common cause of lower urinary tract symptoms (LUTS). This study aimed to explore the genes with diagnostic value in patients with BPH, reveal the relationship between the expression of diagnosis-related genes and the immune microenvironment, and provide a reference for molecular diagnosis and immunotherapy of BPH. The combined gene expression data of GSE6099, GSE7307 and GSE119195 in the GEO database were used. The differential expression of autophagy-related genes between BPH patients and healthy controls was obtained by differential analysis. Then the genes related to BPH diagnosis were screened by a machine learning algorithm and verified. Finally, five important genes (IGF1, PSIP1, SLC1A3, SLC2A1 and T1A1) were obtained by random forest (RF) algorithm, and their relationships with the immune microenvironment were discussed. Five genes play an essential role in the occurrence and development of BPH and may become new diagnostic markers of BPH. Among them, immune cells have significant correlation with some genes. The signal transduction of IL-4 mediated by M2 macrophages is closely related to the progress of BPH. There are abundant active mast cells in BPH. The adoption and metastasis of regulatory T cells may be an important method to treat BPH.</p> </abstract>