ECG2Gene：用心电图预测遗传性肿瘤易感基因风险
ECG2Gene: Deep Learning-Based Germline Tumor Susceptibility Gene Risk Assessment Using Electrocardiograms
一份用普通心电图就能估计 BRCA1/BRCA2 等基因携带风险的研究，外部验证还用了中国人群队列，挺有意思的方向。
ECG2Gene 是一个两阶段深度学习框架，从常规 12 导联心电图中提取与 ATM、BRCA1、BRCA2、PALB2 等六个胚系肿瘤易感基因相关的数字表型。第一阶段用致病性变异携带状态监督多任务模型，生成六个连续的基因评分；第二阶段用 XGBoost 结合临床变量预测后续癌症风险。在 UK Biobank 上，性别特异性模型对乳腺癌、卵巢癌和前列腺癌的 AUROC 分别达到 0.683、0.633 和 0.660。外部验证中，结合年龄的模型在中国 Kadoorie Biobank 男性前列腺癌上取得 0.709 的 AUROC。
ECG2Gene: Deep Learning-Based Germline Tumor Susceptibility Gene Risk Assessment Using Electrocardiograms
Electrocardiograms (ECGs) may contain physiological information related to hereditary cancer susceptibility, but its relationship with future cancer risk remains unclear. Here, we present ECG2Gene, a two-stage deep-learning framework that derives digital phenotypes related to germline tumor susceptibility from routine 12-lead ECGs and predicts future cancer risk. In Stage 1, carrier status for pathogenic or likely pathogenic variants in ATM, BRIP1, CHEK2, PALB2, BRCA1, and BRCA2 supervises a multitask model that generates six continuous ECG-derived gene scores. In Stage 2, XGBoost integrates these scores with clinical variables to predict subsequent breast and ovarian cancer in women and prostate cancer in men. We also introduce ECG-Wide Association Analysis (EWAS) to characterize associations between ECG-derived gene scores and ECG waveforms. Across the clinical feature combinations evaluated in UK Biobank, the best-performing sex-specific models achieve AUROCs of 0.683, 0.633, and 0.660 for breast, ovarian, and prostate cancer, respectively. In external evaluations, models combining ECG-derived gene scores with age achieve prostate cancer AUROCs of 0.709 and 0.579 among men in the overall China Kadoorie Biobank and MIMIC-IV-ECG cohorts, respectively. ECG2Gene links genetically supervised ECG phenotyping and waveform association analysis to future cancer outcomes, demonstrating the potential of routine ECGs combined with clinical information for non-invasive, opportunistic cancer risk assessment.