ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions

作者：Maojie Xue, Ziang Xu, Xiang Wang, Jiajin Chen, Xinxin Kong, Shenxuan Zhou, Jia‐Min Wu, Yuhao Zhang, Yi Li, David C. Christiani, Feng Chen, Yang Zhao, Ruyang Zhang · 发表于：Journal of Advanced Research · 年份：2024 · DOI：10.1016/j.jare.2024.08.015 · 被引用次数：7 · 研究领域：Bioinformatics and Genomic Networks、Epigenetics and DNA Methylation、AI in cancer detection

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. 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. 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...