Abstract

Simple SummaryLiver cancer is one of the most commonly diagnosed cancers worldwide and the fourth leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) accounts for at least 80% of all malignant liver primary tumors. A better characterization of molecular mechanisms underlying HCC onset and progression may lead to discover new therapeutic targets and biomarkers. In this study, we performed an integrative transcriptomics analysis to evaluate the clinical relevance of genes associated with hepatocyte differentiation in human HCC. The HepaRG cell line model was used to define a gene expression signature reflecting the status of tumor hepatocyte differentiation. This signature was able to stratify HCC patients into clinically relevant molecular subtypes. Then, a minimal subset of seven differentiation-associated genes was identified to predict a poor prognosis in several cancer datasets.Hepatocellular carcinoma (HCC) is a deadly cancer worldwide as a result of a frequent late diagnosis which limits the therapeutic options. Tumor progression in HCC is closely correlated with the dedifferentiation of hepatocytes, the main parenchymal cells in the liver. Here, we hypothesized that the expression level of genes reflecting the differentiation status of tumor hepatocytes could be clinically relevant in defining subsets of patients with different clinical outcomes. To test this hypothesis, an integrative transcriptomics approach was used to stratify a cohort of 139 HCC patients based on a gene expression signature established in vitro in the HepaRG cell line using well-controlled culture conditions recapitulating tumor hepatocyte differentiation. The HepaRG model was first validated by identifying a robust gene expression signature associated with hepatocyte differentiation and liver metabolism. In addition, the signature was able to distinguish specific developmental stages in mice. More importantly, the signature identified a subset of human HCC associated with a poor prognosis and cancer stem cell features. By using an independent HCC dataset (TCGA consortium), a minimal subset of seven differentiation-related genes was shown to predict a reduced overall survival, not only in patients with HCC but also in other types of cancers (e.g., kidney, pancreas, skin). In conclusion, the study identified a minimal subset of seven genes reflecting the differentiation status of tumor hepatocytes and clinically relevant for predicting the prognosis of HCC patients.

Highlights

  • Liver cancer is one of the most commonly diagnosed cancers worldwide (841,000 cases) and the fourth leading cause of cancer-related deaths (782,000 cases) [1]

  • An integrative transcriptomics analysis was conducted using publicly available human Hepatocellular carcinoma (HCC) gene expression datasets downloaded from Gene Expression Omnibus (GEO) to evaluate the clinical relevance of the hepatocyte differentiation signature derived from HepaRG, as previously described [18]

  • By applying a functional and integrative transcriptomics approach based on the well-characterized HepaRG model of tumor hepatocyte differentiation, we identified a minimal subset of seven genes (HMGCS2, BDH1, ALDH2, PIPOX, HAO1, AQP9 and PAH) predicting the survival of patients with cancer

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Summary

Introduction

Liver cancer is one of the most commonly diagnosed cancers worldwide (841,000 cases) and the fourth leading cause of cancer-related deaths (782,000 cases) [1]. Both the incidence and the mortality of liver cancer have increased over the last two decades. Hepatocellular carcinoma (HCC) accounts for at least 80% of all malignant liver primary tumors. HCC frequently occurs in a background of fibrotic and/or cirrhotic liver and is usually asymptomatic during the early phases of carcinogenesis. Most of the patients are diagnosed with advanced stage HCC, which limits the therapeutic options and their efficacy [2]. Tumor heterogeneity in advanced HCC impedes the development of effective treatments. Mutations in the TERT promoter and in TP53, and CTNNB1 genes were identified as the most frequent driver mutations in HCC [3]

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