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Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial

Burak Yagin1, Fatma Hilal Yagin1, Cemil Colak1

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This study developed a machine learning model to predict breast cancer metastasis, identifying key genomic biomarkers. The model achieved 96% accuracy, potentially improving patient treatment strategies.

Keywords:
SHAPbreast cancer metastasiseXplainable artificial intelligencegenomic biomarkersmachine learning algorithms

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Breast cancer (BC) metastasis poses a significant clinical challenge.
  • Predicting metastasis and understanding its genomic drivers are crucial for effective treatment.

Purpose of the Study:

  • To develop a predictive model for breast cancer metastasis using machine learning (ML) and eXplainable artificial intelligence (XAI).
  • To identify genomic biomarkers associated with metastasis in breast cancer patients.

Main Methods:

  • Analysis of genomic data from 98 primary breast cancer samples.
  • Application of elastic net for feature selection to identify genomic biomarkers.
  • Training and evaluation of multiple ML algorithms (LightGBM, CatBoost, XGBoost, GBT, AdaBoost).
  • Utilizing SHapley Additive exPlanations (XAI) for model interpretability.

Main Results:

  • The LightGBM model demonstrated high predictive performance with 96% accuracy and 99.3% AUC.
  • Identified specific genes with increased expression (e.g., TSPYL5, ATP5E) linked to higher metastasis risk.
  • Identified genes with decreased expression (e.g., CACTIN, TGFB3) also associated with increased metastasis risk.

Conclusions:

  • The developed ML/XAI model accurately predicts breast cancer metastasis.
  • Identified genomic biomarkers can inform personalized treatment strategies.
  • Findings may help prevent disease progression and improve patient outcomes.