通过机器学习DNA甲基化模式分析识别卵巢癌。
Identifying ovarian cancer with machine learning DNA methylation pattern analysis.
作者信息
Gonzalez Bosquet Jesus, Wagner Vincent M, Russo Douglas, Reyes Henry D, Newtson Andreea M, Bender David P, Goodheart Michael J
机构信息
Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA, 52242, USA.
Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
出版信息
Sci Rep. 2025 Jul 1;15(1):20910. doi: 10.1038/s41598-025-05460-9.
The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a case control study to predict high-grade serous cancer (HGSC) using artificial intelligence methodology and methylated DNA from surgical specimens. Initial prediction models with MethylNet were accurate but complex (AUC = 100%). We optimized these models by selecting the most informative probes with univariate ANOVA analyses first, and then multivariate lasso regression modelling. This step-wise approach resulted in 9 methylated probes predicting HGSC with an AUC of 100%. These models were validated with different analytics and with an independent DNA-methylation experiment with excellent performances.
尽管上皮性卵巢癌（EOC）的治疗取得了巨大进展，但大多数患者仍在晚期才被诊断出来。为了影响总体生存率，我们需要更好的EOC早期诊断方法。我们进行了一项病例对照研究，使用人工智能方法和手术标本中的甲基化DNA来预测高级别浆液性癌（HGSC）。最初使用MethylNet的预测模型准确但复杂（AUC = 100%）。我们首先通过单因素方差分析选择最具信息性的探针，然后进行多变量套索回归建模来优化这些模型。这种逐步方法产生了9个预测HGSC的甲基化探针，AUC为100%。这些模型通过不同的分析方法以及独立的DNA甲基化实验进行了验证，表现出色。
Zotero 插件