Division of Molecular Medicine, Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
Department of Human Genetics, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Int J Mol Sci. 2019 Aug 23;20(17):4119. doi: 10.3390/ijms20174119.
Ovarian cancer is a highly heterogeneous disease and its formation is affected by many epidemiological factors. It has typical lack of early signs and symptoms, and almost 70% of ovarian cancers are diagnosed in advanced stages. Robust, early and non-invasive ovarian cancer diagnosis will certainly be beneficial. Herein we analysed the regulatory sequence methylation profiles of the , , and tumour suppressor genes by pyrosequencing in healthy, benign and malignant ovarian tissues, and corresponding plasma samples. We recorded statistically significant higher methylation levels ( < 0.05) in the and genes in malignant tissues than in controls (39.06 ± 18.78 versus 24.22 ± 6.93; 13.55 ± 10.65 versus 5.73 ± 2.19). Higher values in the gene were also found in plasma samples (22.25 ± 14.13 versus 46.42 ± 20.91). A similar methylation pattern with positive correlation between plasma and benign lesions was noted in the gene ( = 0.886, = 0.019) and malignant lesions in the gene ( = 0.771, < 0.001). The random forest algorithm combining methylation indices of all four genes and age determined 0.932 AUC (area under the receiver operating characteristic (ROC) curve) prediction power in the model classifying malignant lesions and controls. Our study results indicate the effects of methylation changes in ovarian cancer development and suggest that the gene is a potential candidate for non-invasive diagnosis of ovarian cancer.
卵巢癌是一种高度异质性疾病,其形成受许多流行病学因素影响。它典型地缺乏早期迹象和症状,几乎 70%的卵巢癌在晚期被诊断出来。稳健、早期和非侵入性的卵巢癌诊断肯定会带来益处。在此,我们通过焦磷酸测序分析了健康、良性和恶性卵巢组织及其相应血浆样本中 、 、 和 肿瘤抑制基因的调控序列甲基化谱。我们记录到恶性组织中 基因和 基因的甲基化水平明显更高(<0.05),分别为 39.06±18.78 与 24.22±6.93;13.55±10.65 与 5.73±2.19。在血浆样本中也发现 基因的数值更高(22.25±14.13 与 46.42±20.91)。在 基因中,我们还观察到与良性病变之间存在正相关的类似甲基化模式(=0.886,=0.019),以及与恶性病变之间的正相关模式(=0.771,<0.001)。在模型中,结合所有四个基因的甲基化指数和年龄的随机森林算法确定了恶性病变和对照组的 0.932 AUC(接受者操作特征(ROC)曲线下面积)预测能力。我们的研究结果表明了甲基化变化在卵巢癌发展中的作用,并提示 基因是卵巢癌非侵入性诊断的潜在候选基因。