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Chinese Researchers Report HCC-specific Diagnostic Biomarkers

Hepatocellular Carcinoma (HCC) is one of the most common and lethal diseases in the world. Most of the patients are diagnosed at advanced stages, hence early diagnosis is the key to improve survival time of HCC patients.

Previous studies showed that DNA methylation alteration occurred in tumor genesis and progression, and early-stage cancers could be diagnosed by detecting methylation changes in the blood.

Recently, research team led by Prof. LI Yixue at the CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences reported HCC-specific diagnostic biomarkers.

This study systematically investigated HCC-specific diagnostic biomarkers by comparing HCC with other cancers, and provided a new solution for HCC early diagnosis.

LI Hong, CHENG Jinming and some researchers in Professor Li’s team screened HCC-specific hypermethylated CpG sites by comparing the methylation profiles of 375 HCC samples, 50 normal liver samples, 184 normal blood samples, and 3780 tumor samples from patients with other cancers.

A logistic regression model was constructed to distinguish HCC patients from normal controls based on these hypermethylated sites. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies. This model achieved ~ 92% sensitivity in predicting HCC, ~ 98% specificity in excluding normal livers, and ~ 98% specificity in excluding other cancers.

Compared with previously published methylation markers, the markers reported are the only ones that can distinguish HCC from other cancers.

The research findings entitled “Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers” was published online in Genome Medicine on May 30, 2018. This study was supported by the grants from Ministry of Science and Technology and National Natural Science Foundation of China.


 Performance of HCC prediction model (Image by Prof. LI Yixue’s team)


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