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Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic

Mehmet Kivrak1, Hatice Sevim Nalkiran2, Oguzhan Kesen3

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Recep Tayyip Erdogan University, 53020 Rize, Türkiye.

Biology
|November 27, 2025
PubMed
Summary

Age-related gene expression changes impact Luminal A breast cancer survival. Machine learning models integrating clinical and molecular data offer superior prognostic accuracy for this common cancer.

Keywords:
XGBoostagegene expressionluminal A breast cancermachine learningsurvival prediction

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Luminal A breast cancer, typically favorable, can be influenced by age and menopausal status.
  • Conventional prognostic models may not fully capture survival nuances in diverse age groups.

Purpose of the Study:

  • To investigate age-related transcriptomic differences in Luminal A breast cancer.
  • To develop an advanced prognostic model integrating clinical and genomic data for improved survival prediction.

Main Methods:

  • Analysis of transcriptomic and clinical data from the METABRIC cohort, stratifying patients by age and menopausal status.
  • Identification of differentially expressed genes (DEGs) and feature selection using Boruta.
  • Training and validation of machine learning models (Random Forest, Logistic Regression, Multilayer Perceptron, XGBoost) with cross-validation and SMOTE.

Main Results:

  • Distinct transcriptomic clustering observed across different age groups.
  • Identification of 41 age- and survival-associated genes, with key predictors including clinical variables and molecular markers (e.g., ATM, HERC2).
  • XGBoost model achieved high performance (accuracy 98%, AUC 0.86), outperforming other algorithms.

Conclusions:

  • Age-related transcriptomic alterations significantly affect Luminal A breast cancer prognosis.
  • An integrated machine learning approach combining clinical and molecular data enhances prognostic accuracy.
  • This ML-based strategy shows potential for clinical application in personalized breast cancer management.