Dataset Information


Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma

ABSTRACT: 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.

ORGANISM(S): Homo sapiens  

SUBMITTER: Swan Thung   Sara Toffanin  Beatriz Mínguez  Todd R Golub  Jordi Bruix  Augusto Villanueva  Craig April  John Mandeli  Josep M Llovet  Radoslav Savic  Anja Lachenmayer  Scott L Friedman  Yujin Hoshida  Jian-Bing Fan  Myron Schwartz  Laia Cabellos  Vincenzo Mazzaferro  Sasan Roayaie 

PROVIDER: E-GEOD-20017 | ArrayExpress | 2011-07-30



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