ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions
Introduction: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. Objectives: Usi...
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Elsevier
2025-07-01
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| Series: | Journal of Advanced Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090123224003588 |
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| author | Maojie Xue Ziang Xu Xiang Wang Jiajin Chen Xinxin Kong Shenxuan Zhou Jiamin Wu Yuhao Zhang Yi Li David C. Christiani Feng Chen Yang Zhao Ruyang Zhang |
| author_facet | Maojie Xue Ziang Xu Xiang Wang Jiajin Chen Xinxin Kong Shenxuan Zhou Jiamin Wu Yuhao Zhang Yi Li David C. Christiani Feng Chen Yang Zhao Ruyang Zhang |
| author_sort | Maojie Xue |
| collection | DOAJ |
| description | Introduction: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. Objectives: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. Results: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57–24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805–0.883), AUC5-year = 0.816 (95% CI: 0.775–0.857), and C-index = 0.803 (95% CI: 0.776–0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038–1.083) and 1.090 (95% CI: 1.057–1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. Conclusion: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction. |
| format | Article |
| id | doaj-art-d83df66a273844d6be049d2e3c1922f6 |
| institution | OA Journals |
| issn | 2090-1232 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Advanced Research |
| spelling | doaj-art-d83df66a273844d6be049d2e3c1922f62025-08-20T02:36:46ZengElsevierJournal of Advanced Research2090-12322025-07-017356157310.1016/j.jare.2024.08.015ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactionsMaojie Xue0Ziang Xu1Xiang Wang2Jiajin Chen3Xinxin Kong4Shenxuan Zhou5Jiamin Wu6Yuhao Zhang7Yi Li8David C. Christiani9Feng Chen10Yang Zhao11Ruyang Zhang12Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, ChinaState Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases, Nanjing Medical University, Nanjing, Jiangsu 210029, China; Department of Oral Special Consultation, Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Institute of Cardiovascular Diseases, Xiamen Cardiovascular Hospital of Xiamen University, Xiamen, Fujian 361006, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, ChinaDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, ChinaDepartment of General Biology, Eberly College of Science, Pennsylvania State University, Pennsylvania 16802, USADepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USAPulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USADepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Jiangsu 211166, China; Corresponding authors at: SPH Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (R. Zhang); SPH Building Room 402, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (Y. Zhao); SPH Building Room 412, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (F. Chen).Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Jiangsu 211166, China; Corresponding authors at: SPH Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (R. Zhang); SPH Building Room 402, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (Y. Zhao); SPH Building Room 412, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (F. Chen).Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Jiangsu 211166, China; Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu 213164, China; Corresponding authors at: SPH Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (R. Zhang); SPH Building Room 402, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (Y. Zhao); SPH Building Room 412, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China (F. Chen).Introduction: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. Objectives: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. Results: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57–24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805–0.883), AUC5-year = 0.816 (95% CI: 0.775–0.857), and C-index = 0.803 (95% CI: 0.776–0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038–1.083) and 1.090 (95% CI: 1.057–1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. Conclusion: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.http://www.sciencedirect.com/science/article/pii/S2090123224003588Breast cancerGene-gene interactionPrognostic prediction modelOverall survival timeSystematic reviewOnline tool |
| spellingShingle | Maojie Xue Ziang Xu Xiang Wang Jiajin Chen Xinxin Kong Shenxuan Zhou Jiamin Wu Yuhao Zhang Yi Li David C. Christiani Feng Chen Yang Zhao Ruyang Zhang ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions Journal of Advanced Research Breast cancer Gene-gene interaction Prognostic prediction model Overall survival time Systematic review Online tool |
| title | ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions |
| title_full | ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions |
| title_fullStr | ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions |
| title_full_unstemmed | ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions |
| title_short | ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions |
| title_sort | artemis an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene gene interactions |
| topic | Breast cancer Gene-gene interaction Prognostic prediction model Overall survival time Systematic review Online tool |
| url | http://www.sciencedirect.com/science/article/pii/S2090123224003588 |
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