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A Machine Learning Method for Predicting Biomarkers Associated with Prostate Cancer.

Yanqiu Tong1,2, Zhongle Tan3, Pu Wang4

  • 1Laboratory of Forensic Medicine and Biomedical Informatics, Chongqing Medical University, 400016 Chongqing, China.

Frontiers in Bioscience (Landmark Edition)
|January 5, 2024
PubMed
Summary

This study identified key genes using machine learning and protein-protein interaction networks to serve as diagnostic and prognostic biomarkers for prostate cancer (PCa). These biomarkers can improve PCa diagnosis and aid in developing new clinical trial drugs.

Keywords:
drug targetsmachine learningprognostic biomarkerprognostic modelprostate cancer

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Prostate cancer (PCa) is a common malignancy in Western males.
  • Identifying reliable diagnostic and prognostic biomarkers is crucial for PCa management.
  • Biomarkers aid in drug target screening, pathway understanding, and reducing experimental costs.

Purpose of the Study:

  • To identify and validate promising diagnostic and prognostic biomarkers for prostate cancer (PCa).
  • To leverage machine learning and network analysis for biomarker discovery.
  • To develop a prognostic risk model for PCa patients.

Main Methods:

  • Utilized machine learning and protein-protein interaction (PPI) networks to analyze PCa biomarkers.
  • Performed multi-database validation and literature review for diagnostic biomarkers.
  • Employed univariate and stepwise multivariate Cox regression for prognostic gene screening and risk model development.

Main Results:

  • Identified nine hub genes including UBE2C, CCNB1, TOP2A, TPX2, CENPM, F5, APOE, NPY, and TRIM36.
  • Validated these hub genes across multiple databases, revealing their involvement in significant pathways.
  • Developed a four-gene prognostic risk model comprising TOP2A, UBE2C, MYL9, and FLNA.

Conclusions:

  • Machine learning and PPI networks successfully identified diagnostic and prognostic biomarkers for PCa.
  • The developed risk model enhances diagnostic accuracy for PCa patients.
  • This approach aids in predicting new drug efficacy in clinical trials.