A bioinformatics analysis of the PIEZO channel interaction network and the genes related to glycometabolic reprogramming and mitochondrial oxidative phosphorylation in the progression of prostatic hyperplasia

Background: Prostatic hyperplasia is a common condition among aging males characterized by excessive prostate cell proliferation, and while it is a benign process, it involves significant metabolic reprogramming and immune infiltration—features typically associated with malignant tumors. This study aimed to elucidate the interaction network between the PIEZO channels and genes related to glycometabolic reprogramming and mitochondrial oxidative phosphorylation (OXPHOS), highlighting their roles in the progression of prostatic hyperplasia. Methods: Gene expression datasets from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) associated with prostatic hyperplasia. Glycolysis- and OXPHOS-related pathways were obtained from the Molecular Signatures Database (MSigDB). Advanced bioinformatics methods, including differential expression analysis, functional enrichment analysis, immune infiltration analysis, and machine learning models, were employed to explore PIEZO interactions. A competitive endogenous RNA (ceRNA) network and a biomarker-based diagnostic nomogram were constructed and validated across multiple datasets. Results: A total of 4,617 DEGs were identified, among which 122 were glycolysis- and OXPHOS-related genes. The Spearman correlation analysis revealed 84 genes significantly associated with PIEZO1 and PIEZO2 expression. The functional enrichment analysis demonstrated that these PIEZO-related genes regulate critical processes, including ATP production, mitochondrial function, and glycolysis. The immune infiltration analysis revealed associations between PIEZO biomarkers and altered immune cell profiles. Machine learning models identified PGM2 and GFPT1 as robust diagnostic biomarkers with strong predictive performance [area under the curve (AUC) >0.75]. The ceRNA network revealed complex regulatory interactions involving PIEZO genes, biomarkers, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), underscoring their potential roles in disease pathogenesis. Conclusions: This study provides a comprehensive bioinformatics framework linking PIEZO channels to glycometabolic reprogramming and OXPHOS dysregulation in prostatic hyperplasia. The identified biomarkers and regulatory networks offer novel insights into the molecular mechanisms underlying disease progression and potential therapeutic targets.

Duke Scholars

Author Yibin Wang Medicine, Cardiology