Dataset Information

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

ABSTRACT:

Background and aim

Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers.

Methods

A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used.

Results

We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group.

Conclusions

In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

SUBMITTER: Hao Y

PROVIDER: S-EPMC9720918 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

Hao Yijie Y Yang Qingxia Q He Qiye Q Hu Huanjing H Weng Zongpeng Z Su Zhixi Z Chen Shuling S Peng Sui S Kuang Ming M Chen Zhihang Z Xu Lixia L

European journal of medical research 20221205 1

<h4>Background and aim</h4>Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers.<h4>Methods</h4>A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide m ...[more]

PMID: 36464701

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Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Project description:Background and aimsAs a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.MethodsA total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.ResultsCombined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson-Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.ConclusionsThe nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.

Project description:Liver cancer is the main reason of cancer deaths globally, with an unfavorable prognosis. DNA methylation is one of the epigenetic modifications and maintains the right adjustment of gene expression and steady gene silencing. We aim to explore the novel signatures for prognosis by using DNA methylation-driven genes. To acquire the DNA methylation-driven genes, we perform the difference analysis from the gene expression data and DNA methylation data in TCGA or GEO databases. And we obtain the 31 DNA methylation-driven genes. Subsequently, consensus clustering analysis was utilized to identify the molecular subtypes based on the 31 DNA methylation-driven genes. So, two molecular subtypes were identified to perform those analyses: Survival, immune cell infiltration, and tumor mutation. Results showed that two subtypes were clustered with distinct prognoses, tumor-infiltrating immune cell and tumor mutation burden. Furthermore, the 31 DNA methylation-driven genes were applied to perform the survival analysis to select the 14 survival-related genes. Immediately, a five methylation-driven genes risk model was built, and the patients were divided into high and low-risk groups. The model was established with TCGA as the training cohort and GSE14520 as the validation cohort. According to the risk model, we perform the systematical analysis, including survival, clinical feature, immune cell infiltration, somatic mutation status, underlying mechanisms, and drug sensitivity. Results showed that the high and low groups possessed statistical significance. In addition, the ROC curve was utilized to measure the accuracy of the risk model. AUCs at 1-year, 3-years, and 5-years were respectively 0.770, 0.698, 0.676 in training cohort and 0.717, 0.649, 0.621 in validation cohort. Nomogram was used to provide a better prediction for patients' survival. Risk score increase the accuracy of survival prediction in HCC patients. In conclusion, this study developed a novel risk model of five methylation-driven genes based on the comprehensive bioinformatics analysis, which accurately predicts the survival of HCC patients and reflects the immune and mutation features of HCC. This study provides novel insights for immunotherapy of HCC patients and promotes medical progress.

Dataset Information

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

Background and aim

Methods

Results

Conclusions

Publications

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

Similar Datasets

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