Project description:This SuperSeries is composed of the following subset Series: GSE9843: Gene expression profiling of 91 hepatocellular carcinomas with hepatitis C virus etiology, Samples with "vascular invasion: Yes/No" were included in the study. GSE20017: Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma Refer to individual Series
Project description:Background: The presence of vascular invasion (VI) in pathology specimens has been widely described as closely linked to poor outcome in hepatocellular carcinoma (HCC) patients after tumor resection. Previous attempts have been conducted to achieve molecular markers or signatures to predict HCC recurrence in HCC. Here, we aim to develop a diagnostic model combining clinical and genomic variables able to detect the presence of VI prior to surgery and link it to survival estimation. Methods: Seventy-nine HCV related HCC samples from patients that underwent surgical resection as a treatment for HCC were subjected to Genome-wide gene expression profiling and a predictive model of vascular invasion was constructed. The model was tested in an independent-validation set of 153 fixed tissue samples of resected HCC. Quantitative RTPCR and inmunohistochemistry were performed in HCC samples to test a potential biomarker. Results: A 39-gene signature was able to accurately (72%) identify vascular invasion in HCC patients treated with resection. A model including tumor size and the signature is able to predict presence of VI with 85% accuracy in HCV-related HCC patients, and is able to exclude VI in up to 87% cases in HCC from all etiologies. Conclusions: Using the VI gene signature together with tumor size, VI can be successfully detected in HCC patients. The diagnostic model, integrated in a previously reported survival chart is able to provide an estimated survival for selected cases. Clinical implications of this fact are relevant to provide objective data to further apply expanded indication of curative treatments in HCC. Gene-expression profiling was performed using formalin-fixed, paraffin-embedded hepatocellular carcinoma tissues obtained at the time of surgical resection.
Project description:Background: The presence of vascular invasion (VI) in pathology specimens has been widely described as closely linked to poor outcome in hepatocellular carcinoma (HCC) patients after tumor resection. Previous attempts have been conducted to achieve molecular markers or signatures to predict HCC recurrence in HCC. Here, we aim to develop a diagnostic model combining clinical and genomic variables able to detect the presence of VI prior to surgery and link it to survival estimation. Methods: Seventy-nine HCV related HCC samples from patients that underwent surgical resection as a treatment for HCC were subjected to Genome-wide gene expression profiling and a predictive model of vascular invasion was constructed. The model was tested in an independent-validation set of 153 fixed tissue samples of resected HCC. Quantitative RTPCR and inmunohistochemistry were performed in HCC samples to test a potential biomarker. Results: A 39-gene signature was able to accurately (72%) identify vascular invasion in HCC patients treated with resection. A model including tumor size and the signature is able to predict presence of VI with 85% accuracy in HCV-related HCC patients, and is able to exclude VI in up to 87% cases in HCC from all etiologies. Conclusions: Using the VI gene signature together with tumor size, VI can be successfully detected in HCC patients. The diagnostic model, integrated in a previously reported survival chart is able to provide an estimated survival for selected cases. Clinical implications of this fact are relevant to provide objective data to further apply expanded indication of curative treatments in HCC.
Project description:Vascular invasion is considered as the critical risk factors for tumor recurrence of hepatocellular carcinoma (HCC). To reveal the molecular mechanisms underlying macrovascular invasion (MaVI) and metastasis in HCC, we performed an iTRAQ based proteomic study to identify notably dysregulated proteins in 53 HCC patients with differential vascular invasion. In patients with MaVI, 47 proteins were significantly down-regulated in HCC tumor tissue. More importantly, 30 of them were not changed in HCC without MaVI. Gene ontology analysis of these 47 proteins shows the top 3 enriched pathways are urea cycle, gluconeogenesis and arginine biosynthetic process. We validated 9 remarkably dysregulated candidates in HCC patients with MaVI by Western blot, including 8 down-regulated proteins (CPS1, ASS1, ASL, ARG1, BHMT, DMGDH, Annexin A6 and CES1) and 1 up-regulated protein (CKAP4). Furthermore, dysregulation of CPS1, ASL and ARG1, key enzymes involved in urea cycle, together with Annexin A6 and CES1, major proteins in regulating cholesterol homeostasis and fatty acid ester metabolism were verified using immunohistochemical staining. The significant down-regulation of urea cycle generates clinically relevant proteomic signature in HCC patients with macrovascular invasion, which may provide possible insights into the molecular mechanisms of metastasis and new therapeutic targets of HCC.
Project description:Progression of hepatocellular carcinoma (HCC) often leads to vascular invasion and intrahepatic metastasis, which correlate with recurrence after surgical treatment and poor prognosis. It is crucial to identify patients with a high risk of recurrence and develop more intensified or targeted treatment strategy to improve disease outcome.
Project description:Progression of hepatocellular carcinoma (HCC) often leads to vascular invasion and intrahepatic metastasis, which correlate with recurrence after surgical treatment and poor prognosis. It is crucial to identify patients with a high risk of recurrence and develop more intensified or targeted treatment strategy to improve disease outcome. In the training set, tumor and non-tumor liver were profiled separately, and each was used to generate a prediction model which was validated with the use of independent validation set.