Introduction: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. Methods: We used single-cell and bulk RNA sequencing data from GEO and TCGA databases. We characterized the tumor microenvironment at the single-cell level, analyzing cell interactions and identifying fibroblasts linked to mitophagy. Target genes were narrowed down at the bulk transcriptome level to construct a PARD prognosis prediction nomogram. Unsupervised consensus clustering classified PARD into subtypes, analyzing differences in clinical features, immune infiltration, and immunotherapy. Furthermore, the cellular functions of the genes of interest were verified in vitro. Results: We identified ten cell types and 160 mitophagy-related single-cell differentially expressed genes (MR-scDEGs). Strong interactions were observed between fibroblasts, endothelial cells, CD8⁺ T cells, and NK cells. Fibroblasts linked to mitophagy were divided into six subtypes. Intersection of DEGs from three bulk datasets with MR-scDEGs identified 26 key genes clustered into two subgroups. COX regression analysis identified seven prognostic key genes, enabling a prognostic nomogram model. High and low-risk groups showed significant differences in clinical features, immune infiltration, immunotherapy, and drug sensitivity. In prostate cancer cell lines, CAV1, PALLD, and ITGB8 are upregulated, while CLDN7 is downregulated. Knockdown of PALLD significantly inhibits the proliferation and colony-forming ability of PC3 and DU145 cells, suggesting the important roles of this gene in prostate cancer progression. Conclusions: This study analyzed mitophagy-related genes in PARD, predicting prognosis and aiding in subtype identification and immunotherapy response analysis. This approach offers new strategies for treating prostate cancer with specific molecular subtypes and helps develop potential biomarkers for personalized medicine strategies.

Background: Urinary tract stone (UTS) is a common disease significantly impacting human health. Obesity influences stone formation and increases UTS incidence, yet the differences in the urinary microbiota and pathways between overweight and healthy-weight UTS patients remain unclear. Methods: In this study, 16 patients were analyzed: 8 overweight and 8 healthy-weight UTS patients. Bladder urine samples were collected during surgery, and DNA was extracted for microbial analysis using 2bRAD markers. Microbial diversity and KEGG pathway differences were studied. Results: The results showed that overweight UTS patients had a significantly higher urinary microbial diversity than healthy-weight patients. The analysis identified differences in microbiota at various taxonomic levels. LEfSe analysis revealed Sphingomonas_paucimobilis as abundant in overweight patients, while Bifidobacterium_piotii dominated in healthy-weight patients. Key species, including Ralstonia_sp000620465, Sphingomonas_paucimobilis, and Campylobacter_D_coli, were identified. KEGG analysis highlighted enriched pathways in overweight UTS patients, including the porphyrin and chlorophyll metabolism, fatty acid metabolism, amino acid degradation, and renin–angiotensin and mineral absorption pathways. Conclusions: This study is the first to use 2bRAD-M microbiome analysis to compare the urinary microbiota between overweight and healthy-weight UTS patients. It identified significant microbiota and pathway differences, suggesting a link between microbiota imbalance, obesity, and stone formation. These findings provide potential targets for further research on obesity-related stone susceptibility mechanisms.