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首页> 外文期刊>BMC Medical Genomics >Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation
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Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

机译：筛选基因组，用于HCC特异性CpG甲基化签名作为生物标志物，用于诊断和预后评估

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Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.
机译：肝细胞癌（HCC）是世界上最常见和侵入性的恶性肿瘤之一。 DNA甲基化的变化是HCC的关键事件。从癌症基因组Atlas（TCGA）下载HCC和17种其他类型癌症的甲基化数据集。鉴定了肿瘤组织和副癌组织之间甲基化差异的差异的CPG位点。我们使用从TCGA下载的HCC甲基化数据集作为训练集，并在所有癌症数据集中删除了重叠的站点，以确保只有特定于HCC的CPG站点仍然存在。执行逻辑回归分析以选择可用于诊断HCC的特定生物标志物，并且使用从GEO作为验证集下载的两个数据集-GSE157341和GSE54503用于验证我们的模型。我们还使用了COX回归模型来选择与患者预后相关的CPG网站。我们将6个HCC特异性甲基化CPG位点鉴定为生物标志物，用于HCC诊断。在训练集中，接收器操作特征（ROC）曲线（AUC）的区域，其中包含所有这些网站的模型为0.971。来自Geo数据库的两个验证集，AUC为0.8802和0.9711。此外，分析了3个其他CPG位点，并用于为患者预后和生存预测创造风险评分模型。通过从TCGA和基因表达的HCC甲基化数据集分析，确定了HCC诊断和预后评估的潜在生物标志物。

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