Machine learning-based screening of potential diagnostic markers for prostate cancer with immune cell infiltration analysis and experimental validation.

ABSTRACT:

Background

Prostate adenocarcinoma (PRAD) poses a significant global health burden. Apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3C (APOBEC3C) exhibits context-dependent roles in cancer but its function in PRAD remains unclear. This study aims to resolve APOBEC3C's functional duality in PRAD and identify immune-related hub genes using integrated bioinformatics and experimental validation.

Methods

Screening of PRAD differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and CIBERSORT immune infiltration assessment were performed based on The Cancer Genome Atlas (TCGA) database. Second, key genes in DEGs were screened using random forest (RF) analysis, the least absolute shrinkage and selection operator (LAS

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12541518 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature