CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study

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.

Integrated Analysis Identifies a Nine-microRNA Signature Biomarker for Diagnosis and Prognosis in Colorectal Cancer

“…The candidate miRNAs were determined by a LASSO algorithm with penalty parameter tuning with 10-fold cross-validation and an SVM-Recursive Feature Elimination (RFE) algorithm (Qiu et al, 2017). In total, nine common candidate miRNAs were further selected for the training cohort.…”

Molecular signatures in hepatocellular carcinoma: A step toward rationally designed cancer therapy

“…More recently, Qiu et al recently demonstrated that aberrant DNA methylation also might be used to distinguish the risk of recurrence among patients with resected, early stage HCC . Those authors analyzed 66 tumors samples with an Illumina Methylation 450‐k bead chip, from which they isolated 46 CpG sites that were associated with either a high or low risk of recurrence.…”

“…More recently, Qiu et al recently demonstrated that aberrant DNA methylation also might be used to distinguish the risk of recurrence among patients with resected, early stage HCC. 80 Those authors analyzed 66 tumors samples with an Illumina Methylation 450-k bead chip, from which they isolated 46 CpG sites that were associated with either a high or low risk of recurrence. They narrowed this set to 3 CpG sites near 3 genes (SCAN domain-containing protein 3 [SCAND3], SH3-conaining GRB2-like protein-3 interacting protein 1 [SGIP1], and peptidase inhibitor 3 [PI3]), which were then used to create a risk score.…”

“…Over the last 2 decades, a plethora of signatures based on unique gene expression profiles and modifications of biologic macromolecules has been proposed, as summarized in Table 2. 10,12,13,15,24,68,70,71,75,80,93,99,101,102,104,118,120,132,[135][136][137] There is considerable overlap among these signatures, revealing common mechanisms of hepatocarcinogenesis and disease progression (Fig. 2).…”

Predicting overall survival of patients with hepatocellular carcinoma using a three‐category method based on DNA methylation and machine learning

“…6 An effective two-category classification model was generated for predicting early HCC recurrence based on at least three CpG methylation sites; this model was developed through analysis of 450K methylation chip data from 576 publicly available samples. 7 Analysis of 450K chip data also led to the identification of DNA methylation sites in the genome of peripheral blood mononuclear cells and T cells that were associated with HCC progression. 8 Bisulfite sequencing analysis in the Huh2 HCC cell line showed an association between abnormal DNA methylation and abnormal DLL3 expression.…”