Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS
Biology Methods and Protocols Pub Date : 2024-06-20 eCollection Date: 2024-01-01 DOI:10.1093/biomethods/bpae028
Izzy Newsham, Marcin Sendera, Sri Ganesh Jammula, Shamith A Samarajiwa
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引用次数: 0

Abstract

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

利用可解释的机器学习揭示癌症特异性 DNA 甲基化模式,实现癌症的早期检测和诊断。
癌症是两百多种不同疾病的集合体,仍然是全球发病率和死亡率的主要原因。转移性癌症通常在疾病晚期才被发现,占癌症相关死亡人数的 90%。因此,癌症的早期检测与当前的疗法相结合,将对各种癌症的生存和治疗产生重大影响。DNA 甲基化等表观遗传学变化是导致癌变的一些早期事件。在这里,我们报告了一种可解释的机器学习模型,它能仅利用 DNA 甲基化数据对 13 种癌症类型和非癌症组织样本进行分类,准确率高达 98.2%。我们利用该模型识别出的特征开发了 EMethylNET,这是一个由 XGBoost 模型组成的稳健模型,该模型可为深度神经网络提供信息,而深度神经网络可泛化到独立的数据集。我们还证明了分类器检测到的甲基化相关基因组位点与癌症相关的基因、通路和网络有关,为我们深入了解致癌的表观基因组调控提供了线索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
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