Our study aims to investigate the role of pyrimidine metabolism in prostate cancer and its associations with the immune microenvironment, drug sensitivity, and tumor mutation burden. Through transcriptomic and single-cell RNA sequencing analyses, we explored metabolic pathway enrichment, immune infiltration patterns, and differential gene expression in prostate cancer samples. The results showed that pyrimidine metabolism-related genes were significantly upregulated in the P2 subgroup compared to the P1 subgroup, with enhanced metabolic activity observed in basal and luminal epithelial cells. In addition, immune infiltration analysis revealed a strong correlation between pyrimidine metabolism and immune cell regulation, particularly involving T cell activity. Tumors in the P2 subgroup, characterized by higher pyrimidine metabolism, exhibited greater infiltration of activated CD4 + T cells and M2 macrophages, indicating a potential link between metabolic reprogramming and the immune response in prostate cancer. Drug sensitivity analysis further demonstrated that tumors with elevated pyrimidine metabolism displayed increased responsiveness to several chemotherapeutic agents, including BI-2536, JW-7-24-1, and PAC-1, suggesting that targeting pyrimidine metabolism may enhance treatment efficacy. Moreover, key genes involved in pyrimidine de novo synthesis, such as RRM2, were identified as potential drivers of tumor progression, providing new insights into the molecular mechanisms underlying aggressive prostate cancer phenotypes. In conclusion, pyrimidine metabolism plays a critical role in prostate cancer progression, influencing immune infiltration and drug sensitivity. Targeting this metabolic pathway offers a promising strategy for the development of new therapeutic approaches, particularly for overcoming drug resistance and improving outcomes in patients with advanced prostate cancer.

Prostate cancer, one of the most prevalent malignancies among men worldwide, is intricately linked with androgen signaling, a key driver of its pathogenesis and progression. Understanding the diverse expression patterns of androgen-responsive genes holds paramount importance in unraveling the biological intricacies of this disease and prognosticating patient outcomes. In this study, utilizing consensus clustering analysis based on the expression profiles of androgen-responsive genes, prostate cancer patients from the TCGA database were stratified into two distinct subtypes, denoted as C1 and C2. Notably, the C1 subtype demonstrates a significant upregulation of certain genes, such as CGA and HSD17B12, along with a shorter progression-free survival duration, indicating a potentially unfavorable prognosis. Further analyses elucidated the immune infiltration disparities, mutation landscapes, and gene functional pathways characteristic of each subtype. Through integrated bioinformatics approaches and machine learning techniques, key genes such as BIRC5, CENPA, and MMP11 were identified as potential therapeutic targets, providing novel insights into tailored treatment strategies. Additionally, single-cell transcriptome analysis shed light on the heterogeneous expression patterns of these genes across different cell types within the tumor microenvironment. Furthermore, virtual screening identified candidate drugs targeting the BIRC5 receptor, offering promising avenues for drug development. Collectively, these findings deepen our understanding of prostate cancer biology, paving the way for personalized therapeutic interventions and advancing the quest for more effective treatments in prostate cancer management.

Prostate cancer (PCa) is one of the most common malignant tumors in men. In recent years, mitochondrial dysfunction has been found to be closely related to cancer progression. However, the role of mitochondria‑related genes in PCa remains unclear. The aim of the present study was to discover novel biomarkers based on differentially expressed mitochondrial‑related genes (DeMRGs) to aid in PCa diagnosis. In the present study, gene expression data from the Gene Expression Omnibus and The Cancer Genome Atlas databases were combined with a mitochondrial‑related gene list provided by the MitoCarta database to identify DeMRGs. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and Gene Set Enrichment Analysis were then used to investigate the functions and related pathways of these DeMRGs. Subsequently, Cytoscape software and the STRING website were used to explore the transcription factors and microRNAs related to the DeMRGs. The degree of infiltration of immune cells in the immune landscape of patients with PCa and the controls was assessed using CIBERSORT. Finally, the correlation between characteristic DeMRGs and immune cell infiltration and mitochondrial respiration was analyzed. The results indicated that 6 characteristic genes, including acetyl‑CoA carboxylase β (ACACB), pyruvate dehydrogenase kinase 4 (PDK4), glycine amidinotransferase (GATM), methylcrotonyl‑CoA carboxylase subunit 2, mitochondrial ribosomal protein L12 (MRPL12) and fatty acid synthase, were identified from the 60 DeMRGs. The results showed a close association between the characteristic DeMRGs and immune infiltrating cells. In addition, it was found that MRPL12, PDK4, ACACB and GATM were correlated with mitochondrial respiration. These 4 genes were selected as hub genes as they are closely related to gluconeogenesis, the tricarboxylic acid cycle, lipid metabolism, amino acid metabolism and other mitochondrial metabolic pathways in PCa. In conclusion, 4 novel mitochondrial‑related gene signatures that influence mitochondrial metabolism within the immune microenvironment were identified in PCa.

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein–protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein–protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.