Dataset Information

Predicting hepatocellular carcinoma development for cirrhosis patients via methylation detection of heparocarcinogenesis-related genes.

ABSTRACT: Background: Most hepatocellular carcinoma (HCC) patients have undergone a progression from chronic hepatitis, then liver cirrhosis (LC), and finally to carcinoma. The objective of this study was to elucidate risk factors to predict HCC development for cirrhosis patients. Methods: Multiple methylated specific PCR (MSP) was applied to determine methylation status of heparocarcinogenesis-related genes in 396 tissue and plasma specimens and multivariate cox model was used to analyze the relationship between risk variables and HCC development among cirrhosis patients, followed up in a median period of 30 months. Results: Among 105 LC cases, HCC incidence rate at 30 months was 30.48% (32/105), which were statistically associated with patients' age and aberrant methylation of p16, SFRP, and LINE1 (p<0.05). Receiver operating characteristic (ROC) curve showed the overall predictive accuracy reached the highest (90.7%) if the four risk variables were concurrent to predict HCC development. Moreover, along with the growth of age from 0-40, 40-55, to 55-70 years or the increased number of aberrantly-methylated gene from 0-1 to 2-3, the HCC incidence rate of cirrhosis patients rised from 10.00%, 12.28% to 82.14% and 17.44% to 89.47%, separately. Thus, based on combined analysis with diverse age and number of aberrantly-methylated gene, 105 cases were divided into five groups and computed their respective HCC incidecne rate to categorize them into different risk groups. Of note, A significant lifting of HCC incidence rate in the high-risk group (40-55 years coupled with 2-3 aberrantly-methylated genes, 55-70 years coupled with 0-1 aberrantly-methylated gene, 55-70 years coupled with 2-3 aberrantly-methylated genes; n=33) was observed compared with the low-risk group (0-40 years coupled with 0-1 aberrantly-methylated gene, 40-55 years coupled with 0-1 aberrantly-methylated gene; (n=72) (p<0.01). Conclusions: Ultimately, high-risk cirrhosis patients with 55-over years or 2-3 aberrantly-methylated genes should be paid more attention to be regularly screened with HCC development.

SUBMITTER: Huang Y

PROVIDER: S-EPMC6010674 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predicting hepatocellular carcinoma development for cirrhosis patients via methylation detection of heparocarcinogenesis-related genes.

Huang Yuan Y Wei Ling L Zhao Rong-Ce RC Liang Wei-Bo WB Zhang Jing J Ding Xue-Qin XQ Li Zhi-Long ZL Sun Cheng-Jun CJ Li Bo B Liu Qiu-Ying QY He Jing-Yang JY Yu Xiao-Qin XQ Gao Bo B Chen Ming-Mei MM Sun Ai-Min AM Qin Yang Y

Journal of Cancer 20180604 12

<b>Background:</b> Most hepatocellular carcinoma (HCC) patients have undergone a progression from chronic hepatitis, then liver cirrhosis (LC), and finally to carcinoma. The objective of this study was to elucidate risk factors to predict HCC development for cirrhosis patients. <b>Methods:</b> Multiple methylated specific PCR (MSP) was applied to determine methylation status of heparocarcinogenesis-related genes in 396 tissue and plasma specimens and multivariate cox model was used to analyze th ...[more]

PMID: 29937940

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Project description:Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

Dataset Information

Predicting hepatocellular carcinoma development for cirrhosis patients via methylation detection of heparocarcinogenesis-related genes.

Publications

Predicting hepatocellular carcinoma development for cirrhosis patients via methylation detection of heparocarcinogenesis-related genes.

Similar Datasets

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