Integrative Prognostic Modeling of Breast Cancer Survival with Gene Expression and Clinical Data

Abstract

Background: Accurate survival prediction in breast cancer is essential for patient stratification and personalized therapy. Integrating gene expression data with clinical factors may enhance prognostic performance and support precision medicine. Objective: To develop an integrative survival prediction model combining clinical variables and gene expression signatures, and to assess their contributions using penalized Cox regression and machine learning. Methods: We analyzed 1,867 patients from the METABRIC cohort with clinical annotations and microarray-based gene expression profiles. The top 5,000 most variable genes were retained. Elastic Net-penalized Cox regression identified 75 predictors (70 genes and 5 clinical variables: tumor size, stage, surgery type, age at diagnosis, and Nottingham Prognostic Index). Model performance was evaluated with Harrell's concordance index (C-index) and 36-month time-dependent AUC. Random Survival Forests (RSF) trained on the top 20 genes assessed nonlinear effects and validated variable importance. PCA and heatmaps visualized gene expression patterns across risk groups. Results: The integrative Cox model achieved a C-index of 0.922 and a 36-month AUC of 0.94, outperforming clinical-only models (C=0.64). RSF confirmed the prognostic value of top genes (e.g., OR2T27, TBATA, LINC01165, SLC10A4), yielding a 36-month AUC of 0.88. Conclusions: Combining gene expression signatures with clinical variables substantially improves survival prediction in breast cancer and provides a framework for individualized prognostic assessment and clinical decision-making.