Hu Ji, Zhao Fu-Ying, Huang Bin, Ran Jing, Chen Mei-Yuan, Liu Hai-Lin, Deng You-Song, Zhao Xia, Han Xiao-Fan
Department of General Surgery, The First People's Hospital of Chongqing Liang Jiang New Area, Chongqing, China.
Department of Medical Laboratory, The First People's Hospital of Chongqing Liang Jiang New Area, Chongqing, China.
Front Genet. 2021 Jan 13;11:614160. doi: 10.3389/fgene.2020.614160. eCollection 2020.
To develop and validate a CpG-based classifier for preoperative discrimination of early and advanced-late stage colorectal cancer (CRC).
We identified an epigenetic signature based on methylation status of multiple CpG sites (CpGs) from 372 subjects in The Cancer Genome Atlas (TCGA) CRC cohort, and an external cohort (GSE48684) with 64 subjects by LASSO regression algorithm. A classifier derived from the methylation signature was used to establish a multivariable logistic regression model to predict the advanced-late stage of CRC. A nomogram was further developed by incorporating the classifier and some independent clinical risk factors, and its performance was evaluated by discrimination and calibration analysis. The prognostic value of the classifier was determined by survival analysis. Furthermore, the diagnostic performance of several CpGs in the methylation signature was evaluated.
The eight-CpG-based methylation signature discriminated early stage from advanced-late stage CRC, with a satisfactory AUC of more than 0.700 in both the training and validation sets. This methylation classifier was identified as an independent predictor for CRC staging. The nomogram showed favorable predictive power for preoperative staging, and the C-index reached 0.817 (95% CI: 0.753-0.881) and 0.817 (95% CI: 0.721-0.913) in another training set and validation set respectively, with good calibration. The patients stratified in the high-risk group by the methylation classifier had significantly worse survival outcome than those in the low-risk group. Combination diagnosis utilizing only four of the eight specific CpGs performed well, even in CRC patients with low CEA level or at early stage.
Our classifier is a valuable predictive indicator that can supplement established methods for more accurate preoperative staging and also provides prognostic information for CRC patients. Besides, the combination of multiple CpGs has a high value in the diagnosis of CRC.
开发并验证一种基于CpG的分类器,用于术前鉴别早期和晚期结直肠癌(CRC)。
我们基于来自癌症基因组图谱(TCGA)CRC队列中372名受试者以及一个包含64名受试者的外部队列(GSE48684)中多个CpG位点(CpGs)的甲基化状态,通过LASSO回归算法确定了一种表观遗传特征。从甲基化特征衍生出的分类器用于建立多变量逻辑回归模型,以预测CRC的晚期阶段。通过纳入该分类器和一些独立的临床风险因素进一步构建列线图,并通过判别分析和校准分析评估其性能。通过生存分析确定该分类器的预后价值。此外,还评估了甲基化特征中几个CpGs的诊断性能。
基于八个CpG的甲基化特征能够区分早期和晚期CRC,在训练集和验证集中的曲线下面积(AUC)均超过0.700,令人满意。该甲基化分类器被确定为CRC分期的独立预测指标。列线图对术前分期显示出良好的预测能力,在另一个训练集和验证集中的C指数分别达到0.817(95%可信区间:0.753 - 0.881)和0.817(95%可信区间:0.721 - 0.913),校准良好。通过甲基化分类器分层为高风险组的患者生存结局明显比低风险组差。即使在癌胚抗原(CEA)水平低或处于早期的CRC患者中,仅利用八个特定CpGs中的四个进行联合诊断也表现良好。
我们的分类器是一个有价值的预测指标,可补充现有方法以实现更准确的术前分期,同时也为CRC患者提供预后信息。此外,多个CpGs的联合在CRC诊断中具有很高的价值。