Jiliang Qiu, Muyan Cai, Wei He, Yun Zheng, Dan Xie, Binkui Li, and Yunfei Yuan, Sun Yat-sen University Cancer Center; Baogang Peng, Junhang Luo, Bin Chen, and Zunfu Ke, First Affiliated Hospital of Sun Yat-sen University; Pi Guo and Mengfeng Li, Sun Yat-sen University; Jiliang Qiu, Muyan Cai, Wei He, Yun Zheng, Dan Xie, Binkui Li, and Yunfei Yuan, State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine; Jiliang Qiu, Yunqiang Tang, and Hui Tang, Guangzhou Medical University Cancer Center, Guangzhou; and Yeben Qian and Canliang Lu, First Affiliated Hospital of Anhui Medical University, Hefei, China.
J Clin Oncol. 2017 Mar;35(7):734-742. doi: 10.1200/JCO.2016.68.2153. Epub 2017 Jan 9.
Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG-based signature, is useful in predicting recurrence for patients with E-HCC.
早期肝细胞癌（E-HCC）的诊断率正在逐渐提高，在诊断出的患者中，有一半会出现复发。因此，迫切需要确定与复发相关的标志物。我们研究了 CpG 甲基化在预测 E-HCC 患者复发中的效果。
共有来自四个独立中心的 576 名 E-HCC 患者被分为三个阶段。在发现阶段，使用 Illumina Methylation 450k Beadchip 分析了 66 个肿瘤样本。使用两种算法，最小绝对值收缩和选择操作和支持向量机递归特征消除，来选择显著的 CpG。在训练阶段，使用惩罚性 Cox 回归将 CpG 进一步缩小到 140 个样本。在验证阶段，使用内部队列（n=141）和两个外部队列（n=191 和 n=104）验证候选 CpG。
结合最小绝对值收缩和选择操作以及支持向量机递归特征消除算法选择的 46 个 CpG 后，在训练阶段，三个 CpG 分别对应于 SCAN 结构域包含 3、Src 同源 3 结构域生长因子受体结合 2 样相互作用蛋白 1 和肽酶抑制剂 3，被突出为候选预测因子。基于这三个 CpG，开发了一个用于 E-HCC 的甲基化特征（MSEH），以在训练队列中对患者进行高复发风险和低复发风险的分组（P<.001）。MSEH 在内部队列（P<.001）和两个外部队列（P<.001；P=0.002）中得到验证。此外，生成了一个包含 MSEH、肿瘤分化、肝硬化、乙型肝炎病毒表面抗原和抗病毒治疗的列线图，用于预测训练队列的 5 年无复发生存率，并且在三个验证队列中表现良好（一致性指数：分别为 0.725、0.697 和 0.693）。
MSEH 是一种基于三个 CpG 的特征，可用于预测 E-HCC 患者的复发。
