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Updated: Mar 9, 2026
Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter
Jiliang Qiu1, Baogang Peng1, Yunqiang Tang1
1Jiliang 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.
Abstract:
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.
