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

ABSTRACT:

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

SUBMITTER: Tang H 

PROVIDER: S-EPMC12599034 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature