Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway.
PLoS One. 2024 Sep 6;19(9):e0307912. doi: 10.1371/journal.pone.0307912. eCollection 2024.
Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.
人类 DNA 中的异常甲基化模式在发现新的诊断和疾病进展生物标志物方面具有巨大潜力。在本文中，我们使用机器学习算法来识别有希望用于诊断癌组织的甲基化位点，并根据这些位点的甲基化值对患者进行分类。我们使用来自基因组数据共享联盟的癌组织和正常组织样本的全基因组 DNA 甲基化模式，并在三种泌尿系统癌症上试用了我们的方法。决策树用于识别最有助于诊断的甲基化位点。然后，使用这些鉴定的位置来训练神经网络，以将样本分类为癌性或非癌性。使用这种两步法，我们为三种癌症中的每一种都找到了强有力的指示性生物标志物组合。这些方法可能可以转化为其他癌症，并通过使用非侵入性液体方法（如血液而不是活检组织）来改进。
