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PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of

Alex Stanley Balraj1, Sangeetha Muthamilselvan1, Rachanaa Raja2

  • 1Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India.

Technology in Cancer Research & Treatment
|January 16, 2024
PubMed
Summary

This study identifies key genes for classifying prostate cancer aggressiveness using computational methods. An AI model, PRADclass, achieved 86% accuracy in predicting cancer differentiation, aiding early detection.

Keywords:
Gleason gradingWGCNA-based reconstructioncancer aggressivenesscancer differentiationconsensus biomarkergrade-salient genemachine learningprostate adenocarcinomarisk stratificationtrait-specific key gene

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Prostate adenocarcinoma (PRAD) is a prevalent cancer in men globally.
  • Significant knowledge gaps exist regarding the molecular drivers of PRAD progression and aggression.
  • Early identification of aggressive prostate cancers is crucial for effective treatment and mortality reduction.

Purpose of the Study:

  • To identify molecular biomarkers for differentiating prostate cancer grades.
  • To develop a computational model for predicting prostate cancer aggressiveness.
  • To aid in early detection and personalized treatment strategies for PRAD.

Main Methods:

  • Utilized TCGA transcriptomic data and clinical metadata for PRAD.
  • Performed Gleason-grade wise linear and network modeling (WGCNA).
  • Developed a machine learning model (RandomForest) using consensus biomarkers for classification.

Main Results:

  • Identified 35 consensus biomarkers specific to Gleason grades 1, 4, and 5.
  • Statistical modeling yielded 77 Gleason grade-salient genes.
  • A RandomForest model achieved ~86% balanced accuracy on external validation for ternary classification.

Conclusions:

  • Multiple computational strategies identified candidate Gleason grade-specific biomarkers.
  • PRADclass, an AI model utilizing these biomarkers, demonstrated good performance in predicting cancer differentiation.
  • PRADclass is available for academic use to aid in prostate cancer prognostication.