Unveiling prognostic biomarkers and immunotherapeutic insights in prostate cancer through multi-omics and machine learning.

BACKGROUND: As a predominant form of cancer affecting male populations, prostate cancer (PCa) demonstrates notably high incidence rates globally. The significant heterogeneity in tumor microenvironment (TME) composition (including epithelial and diverse cell populations) hinders clear interpretation of gene and biomarker roles in disease advancement and immune response modulation. Through combined analysis of bulk and single-cell RNA sequencing data, this investigation evaluates prostate cancer-related genes' clinical relevance and prognostic potential. METHODS: We applied approaches like FindAllMarkers, Dseq2 R package, ssGSEA, and WGCNA with both single-cell and bulk transcriptome scales to uncover genes associated with prognosis. Furthermore, we developed a machine learning approach integrating 14 algorithms and 162 algorithmic combinations to support the formation of consensus immune and prognostic-related signatures (IPRS). The IPRS underwent systematic validation in both training and test cohorts, and a multivariate nomogram was constructed to demonstrate its potential utility in prognosis quantification. Through comprehensive multi-omics analyses, which included genomic, single-cell transcriptomic, and bulk transcriptomic data, we sought to achieve a thorough understanding of prognostic characteristics. Furthermore, we evaluated the clinical applicability of the IPRS in the context of immunotherapy and personalized drug selection. Additionally, we examined and confirmed both the gene expression associated with IPRS and the expression level and function of B-cell adhesion molecule (BCAM) within prostate cancer cells and tissue. RESULT: We discovered 91 genes associated with prognosis in the TME, with 15 of these genes connected to biochemical recurrence. The consensus IPRS constructed based on a machine learning computational framework demonstrates potential value in prognosis prediction and clinical relevance. Multivariate analysis further supports the possibility of IPRS serving as an independent prognostic marker for prostate cancer disease progression. Significant differences in biological functions, immune infiltration, and genomic mutations were also observed among different risk groups. Significantly, the submap method revealed enhanced immunotherapy responsiveness in high-risk patients while highlighting potential pharmacological targets for certain risk subgroups. CONCLUSION: We selected a collection of genes relevant to PCa prognosis and immune characteristics, which may serve as potential biomarkers with certain clinical translational value.