Integrative Prognostic Modeling of Breast Cancer Survival with Gene Expression and Clinical Data
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912551167590400 |
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| author | Amevor, Robert Kubuafor, Emmanuel Baidoo, Dennis Salifu, Junaidu Gonnage, Koshali Muthunama Okeke, Onyedikachi Joshua |
| author_facet | Amevor, Robert Kubuafor, Emmanuel Baidoo, Dennis Salifu, Junaidu Gonnage, Koshali Muthunama Okeke, Onyedikachi Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16740 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrative Prognostic Modeling of Breast Cancer Survival with Gene Expression and Clinical Data Amevor, Robert Kubuafor, Emmanuel Baidoo, Dennis Salifu, Junaidu Gonnage, Koshali Muthunama Okeke, Onyedikachi Joshua Quantitative Methods 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. |
| title | Integrative Prognostic Modeling of Breast Cancer Survival with Gene Expression and Clinical Data |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2508.16740 |