Project description:During embryogenesis, Hepatocyte Growth Factor (HGF) elicits a distinctive morphogenetic program, the invasive growth, by the activation of MET, whose aberrant activation in cancer drives metastatic progression. Aim of this work is to define and characterize the transcriptional signature of invasive growth, and to verify its activation in human cancers. Global expression profiling was carried out on mouse liver stem/progenitor cells (MLP-29) stimulated for different times, one, six and twenty-four hours, in vitro with HGF to define the invasive growth signature. Meta-analysis of human cancer microarray data was carried out to dissect the transcriptional modules of the invasive growth that are aberrantly activated during carcinogenesis of hepatocellular carcinoma. Differential expression analysis identified 2643 regulated genes by HGF, the invasive growth signature, subdivided in 11 gene expression clusters revealing waves of time coded transcriptional regulation. Those waves have been in-silico associated with the regulative role of the transcriptional unit of Rela/Nfkbia and Fos/Jun and biological features recapitulating the physiological invasive growth phenotype observed in cell line, such as cell motility and scattering, cellular proliferation and protection from apoptosis, cytoskeletal rearangement. Genomic meta-analysis on hepatocellular carcinoma identified of a core genes set (323 gene symbols), consistently regulated between MLP-29 and human tumors and significantly associated with cancer aggressiveness and metastasis p.val < 1*10-6, HR=5.404 CI= 2.570-11.365. The invasive growth signature recapitulates the physiopatological program driven by the stimulation of HGF in normal embryonic liver cells and its activity is observed in HCC as well as in several other tumors. This signature is associated with neoplastic progression and reliably predicts human HCC disease outcome, suggesting the involvement of the invasive growth and cancer in cancer progression. These results prompt the future application of anti-met target therapies in HCC and the application of the signature for both prognostic and predictive purposes.

Project description:An integrative transcriptomics analysis was performed to evaluate the clinical relevance of genes associated with hepatocyte differentiation in human hepatocellular carcinoma (HCC). The well-established 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 7 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 is correlated with a dedifferentiation of hepatocytes, the main parenchymal cells in the liver. Here, we hypothesized that the level of expression of genes reflecting the differentiation status of tumor hepatocytes could be clinically relevant in defining subsets of patients with variable clinical outcomes. To test this hypothesis, an integrative transcriptomic approach was used to stratify a cohort of 139 HCC patients based on a gene expression signature established using a well-controlled in vitro model of tumor hepatocyte differentiation in HepaRG cell line. First, we validated the HepaRG model by identifying a robust gene expression signature associated with hepatocyte differentiation and liver metabolism. This 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), a minimal subset of 7 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 demonstrates that genes reflecting the differentiation status of tumor hepatocytes are clinically relevant for predicting the prognosis of HCC patients.

Project description:Gene-expression profiles of hepatitis C-related, early-stage liver cirrhosis Background & Aims: Liver cirrhosis affects 1%M-bM-^HM-^R2% of population and is the major risk factor of hepatocellular carcinoma (HCC). Hepatitis C cirrhosis-related HCC is the most rapidly increasing cause of cancer death in the US. Non-invasive methods have been developed to identify patients with asymptomatic, early-stage cirrhosis, increasing the burden of HCC surveillance, but biomarkers are needed to identify patients with cirrhosis who are most in need of surveillance. We investigated whether a liver-derived 186-gene signature previously associated with outcomes of patients with HCC is prognostic for patients newly diagnosed with cirrhosis but without HCC. Methods: We performed gene expression profile analysis of formalin-fixed needle biopsies from the livers of 216 patients with hepatitis C-related early-stage (Child-Pugh class A) cirrhosis who were prospectively followed for a median of 10 years at an Italian center. We evaluated whether the 186-gene signature was associated with death, progression of cirrhosis, and development of HCC. Results: Fifty-five (25%), 101 (47%), and 60 (28%) patients were classified as having poor-, intermediate-, and good-prognosis signatures, respectively. In multivariable Cox regression modeling, the poor-prognosis signature was significantly associated with death (P=.004), progression to advanced cirrhosis (P<.001), and development of HCC (P=.009). The 10-year rates of survival were 63%, 74%, and 85% and the annual incidences of HCC were 5.8%, 2.2%, and 1.5% for patients with poor-, intermediate-, and good-prognosis signatures, respectively. Conclusions: A 186-gene signature used to predict outcomes of patients with HCC is also associated with outcomes of patients with hepatitis C-related early-stage cirrhosis. This signature might be used to identify patients with cirrhosis in most need of surveillance and strategies to prevent their development of HCC. 216 liver biopsy specimens

Dataset Information

Mus musculus

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets