The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma
Open accessAbstract
Background & Aims In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early-stage (BCLC 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n=287) and adjacent non-tumor, cirrhotic tissue (n=226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from non-tumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature “G3-proliferation” (hazard ratio [HR]=1.75, P=0.003) and an adjacent “poor-survival” signature (HR=1.74, P=0.004) were independent predictors of HCC recurrence, along with satellites (HR=1.66, P=0.04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.
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Sorafenib in advanced hepatocellular carcinoma
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Sorafenib in Advanced Hepatocellular Carcinoma
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Self-citationMore powerful procedures for multiple significance testing
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Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)
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Self-citationDesign and Endpoints of Clinical Trials in Hepatocellular Carcinoma
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EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma
Galle, P. R., Forner, A., Llovet, J. M., Mazzaferro, V., Piscaglia, F., Raoul, J.-L., Schirmacher, P., & Vilgrain, V. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.03.019 · Cited by 11,406
Self-citationEASL–EORTC Clinical Practice Guidelines: Management of hepatocellular carcinoma
European Association for the Study of the Liver, & European Organisation for Research and Treatment of Cancer (2012) · Journal of Hepatology · doi:10.1016/j.jhep.2011.12.001 · Cited by 5,218
“None of the biomarkers tested so far fulfil these criteria in HCC, but four just require external validation by independent groups: gene signatures or biomarkers from the tumor (EpCAM signature, G3-proliferation subclass, and miR-26a) [77,135,136] and adjacent tissue (poor-survival signature) [137].”
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Self-citationGenetic landscape and biomarkers of hepatocellular carcinoma
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Self-citationMechanisms of HBV-induced hepatocellular carcinoma
Levrero, M., & Zucman-Rossi, J. (2016) · Journal of Hepatology · doi:10.1016/j.jhep.2016.02.021 · Cited by 1,052
Metadata
- Authors
- Villanueva, A., Hoshida, Y., Battiston, C., Tovar, V., Sia, D., Alsinet, C., Cornella, H., Liberzon, A., Kobayashi, M., Kumada, H., Thung, S. N., Bruix, J., Newell, P., April, C., Fan, J., Roayaie, S., Mazzaferro, V., Schwartz, M. E., & Llovet, J. M.
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 140
- Issue
- 5
- Pages
- 1501-1512.e2
- ISSN
- 0016-5085 (ISSN-L)
- OpenAlex
- W2046257690
- Cited by
- 447
- References
- 42
- FWCI
- 21.58
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Villanueva, A., Hoshida, Y., Battiston, C., Tovar, V., Sia, D., Alsinet, C., Cornella, H., Liberzon, A., Kobayashi, M., Kumada, H., Thung, S. N., Bruix, J., Newell, P., April, C., Fan, J., Roayaie, S., Mazzaferro, V., Schwartz, M. E., & Llovet, J. M. (2011). Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma. Gastroenterology. https://doi.org/10.1053/j.gastro.2011.02.006
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
