Nikas Jason B, Nikas Emily G
Research & Development, Genomix Incorporation, Minneapolis, Minnesota 55364, United States.
School of Mathematics, University of Minnesota, Minneapolis, Minnesota 55455, United States.
ACS Omega. 2019 Sep 5;4(12):14895-14901. doi: 10.1021/acsomega.9b01613. eCollection 2019 Sep 17.
Prostate cancer is the most prevalent and the second most lethal malignancy among males in the United States of America. Its diagnosis is almost entirely predicated upon histopathological analysis of the biopsied tissue, and it is associated with a substantial average error. Using genome-wide DNA methylation data derived from 469 prostatic tumor tissue samples and 50 normal prostatic tissue samples and interrogating over 485 000 CpG sites per sample (spanning across gene promoters, CpG islands, shores, shelves, gene bodies, and intergenic and other areas), we were able to develop a mathematical model that classified with a high accuracy (overall sensitivity = 95.31% and overall specificity = 94.00%) tumor tissue versus normal tissue. The methylation β values of five CpG sites, corresponding to the genes , , , and two unknown DNA areas in chromosome 1, provided the input to the model. The model was validated with unknown samples, as well as with a sixfold cross-validation and a leave-one-out cross-validation. This study presents a novel genomic model based on genome-wide DNA methylation analysis of biopsied prostatic tissue that could aid in the diagnosis of prostate cancer and help advance the transition to genomic medicine.
前列腺癌是美国男性中最常见且致死率第二高的恶性肿瘤。其诊断几乎完全基于活检组织的组织病理学分析，并且存在相当大的平均误差。利用来自469个前列腺肿瘤组织样本和50个正常前列腺组织样本的全基因组DNA甲基化数据，对每个样本的超过48.5万个CpG位点进行检测（涵盖基因启动子、CpG岛、岸、架、基因体以及基因间和其他区域），我们得以开发出一种数学模型，该模型对肿瘤组织与正常组织进行分类时具有很高的准确性（总体敏感性 = 95.31%，总体特异性 = 94.00%）。五个CpG位点的甲基化β值，对应于基因 、 、 以及1号染色体上两个未知的DNA区域，为该模型提供了输入数据。该模型通过未知样本以及六重交叉验证和留一法交叉验证进行了验证。本研究提出了一种基于活检前列腺组织全基因组DNA甲基化分析的新型基因组模型，该模型有助于前列腺癌的诊断，并推动向基因组医学的转变。
