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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Interpretable machine learning driven biomarker identification and validation for prostate cancer.

Jianxu Yuan1, Dalin Zhou1, Shengjie Yu1

  • 1Department of Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China.

Translational Andrology and Urology
|July 21, 2025
PubMed
Summary
This summary is machine-generated.

This study identified eight key genes (TRPM4, EDN3, EFCAB4A, FAM83B, PENK, NUDT10, KRT14, CXCL13) as potential biomarkers for prostate cancer (PCa) diagnosis and progression. These findings offer a theoretical basis for PCa treatment strategies.

Keywords:
Prostate cancer (PCa)SHapley Additive exPlanations (SHAP)least absolute shrinkage and selection operator regression (LASSO regression)random forest (RF)support vector machine (SVM)

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Prostate cancer (PCa) is a prevalent malignancy globally.
  • Early diagnosis and progression prediction require reliable biomarkers.
  • Identifying key genes is crucial for understanding PCa development.

Purpose of the Study:

  • To identify key genes involved in the occurrence and development of prostate cancer.
  • To discover potential biomarkers for early diagnosis and progression prediction of PCa.
  • To explore the molecular mechanisms underlying PCa.

Main Methods:

  • Integrated multi-chip datasets from the Gene Expression Omnibus (GEO) database.
  • Applied differential expression analysis and enrichment analysis to identify PCa-related genes.
  • Constructed and evaluated machine learning models (LASSO, SVM, RF) with SHAP analysis for gene contribution and utilized GSEA and immune cell infiltration analysis.

Main Results:

  • Identified 222 differentially expressed genes (DEGs) enriched in PCa-associated functions and pathways.
  • Pinpointed eight core PCa-related genes: TRPM4, EDN3, EFCAB4A, FAM83B, PENK, NUDT10, KRT14, and CXCL13.
  • Uncovered significant distinctions between PCa and normal tissues through GSEA and immune cell infiltration analysis.

Conclusions:

  • Identified eight core genes as potential biomarkers for prostate cancer.
  • Provided a theoretical foundation for the diagnosis and treatment of PCa.
  • Highlighted the utility of machine learning and bioinformatics in cancer research.