Unknown,Transcriptomics,Genomics,Proteomics

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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: Yujin Hoshida

PROVIDER: E-GEOD-20017 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy. 43 tumor (JT) and 44 non-tumor (JNT) liver tissues surgically resected from patients with HCV-associated hepatocellular carcinoma; 8 non-tumor liver tissues (control samples, JC) surgically resected from HCV- or HBV-free patients with metastatic liver tumor. Inter-batch normalization was carried out using Distance Weighted Discrimination procedure. The supplementary file 'GSE17856_Readme.txt' contains a description of the replicates used for normalization. The 'GSE17856_US14702406_2514850*' files are the raw data files for the replicates.

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

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Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma

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