ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2017
CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 2 of them syntheses that pooled it.
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96 citing papers in PubMed, 2 syntheses or guidelines pooled it, 186 citations in OpenAlex.
- Preoperative prediction power of radiomics and non-radiomics methods based on MRI for early recurrence in hepatocellular carcinoma: a systemic review and meta-analysis.Abdominal radiology (New York) · 2024Pooled it
- Progressive and Prognosis Value of Notch Receptors and Ligands in Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.Scientific reports · 2017Pooled it
- A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.Annals of surgical oncology · 2021Trial
- Novel physiologic nomogram discriminates symptom outcome in patients with erosive esophagitis.Esophagus : official journal of the Japan Esophageal Society · 2021Trial
- Identification of key ferroptosis-related targets in colorectal cancer: A transcriptomics-driven study via machine learning and AUcell analysis of single-cell RNA-sequencing.Journal of Cancer · 2026Article
- The recurrence or metastasis related gene predicts the prognosis of extremity and trunk soft tissue sarcoma.Precision clinical medicine · 2025Article
- DNA methylation profiles predicting response to anti-PD-1-based treatment in patients with advanced gastric cancer.BMC medicine · 2025Article
- Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study.JMIR bioinformatics and biotechnology · 2025Article
- Identification of marker genes associated with oxidative stress in hypertrophic cardiomyopathy using bioinformatics analysis and experimental validation.Scientific reports · 2025Article
- Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.Biology direct · 2025Article
- Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review.World journal of surgical oncology · 2025Review
- Exploring methylation signatures for high de novo recurrence risk in hepatocellular carcinoma.Clinical and molecular hepatology · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Bioinformatics revealed biomarkers for diagnosis in kidney stones.Frontiers in genetics · 2025Article
- A Nomogram for Predicting Recurrence in Stage I Non-Small Cell Lung Cancer.The clinical respiratory journal · 2024Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation.Journal of cellular and molecular medicine · 2024Article
- Prognostic effect of DNA methylation ofHeliyon · 2024Article
- Exploring personalized treatment for cardiac graft rejection based on a four-archetype analysis model and bioinformatics analysis.Scientific reports · 2024Article
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36 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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