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

Mehmet Kıvrak,Hatice Sevim Nalkıran,O. Kesen,Ihsan Nalkiran

Published 2025 in Biology

ABSTRACT

Simple Summary Breast cancer is the most common cancer in women, and the Luminal A type is usually linked to better survival. However, age and menopause can affect how the disease behaves and how patients respond to treatment. In this study, we looked at both genetic information from tumors and clinical features such as age, tumor size, and treatments. Women with Luminal A breast cancer were divided into younger, older, and elderly groups. We found that gene activity differed between these groups and that some genes and clinical features were closely related to survival. By using computer-based learning methods, we created models that combined both genetic and clinical data. These models predicted survival more accurately than traditional methods. Our results suggest that, in future, considering both age-related genetic changes and clinical features may help doctors make better treatment decisions and improve outcomes for women with this type of breast cancer.

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