A Unique Metastasis Gene Signature Enables Prediction of Tumor Relapse in Early Stage Hepatocellular Carcinoma Patients
By S. Roessler, H. Jia, A. Budhu, M. Forgues, Q. Ye, Ju-Seog Lee, Snorri Thorgeirsson, Zhong-Yi Sun, Zhao-You Tang, L. Qin and 1 more. Cancer Research, 2010
931 citations recorded by the source corpus.
Metastasis-related recurrence often occurs in hepatocellular carcinoma (HCC) patients who receive curative therapies. At present, it is challenging to identify patients with high risk of recurrence, which would warrant additional therapies. In this study, we sought to analyze a recently developed metastasis-related gene signature for its utility in predicting HCC survival using two independent cohorts consisting of a total of 386 patients who received radical resection. Cohort-1 contained 247 predominantly HBV-positive cases analyzed with an Affymetrix platform, while cohort-2 contained 139 cases with mixed etiology analyzed with the NCI Oligo Set microarray platform. We employed a survival risk prediction algorithm with training, test, and independent cross-validation strategies and found that the gene signature is predictive of overall and disease-free survival. Importantly, risk was significantly predicted independently of clinical characteristics and microarray platform. In addition, survival prediction was successful in patients with early disease, such as small (<5 cm in diameter) and solitary tumors, and the signature predicted particularly well for early recurrence risk (<2 years), especially when combined with serum alpha fetoprotein or tumor staging. In conclusion, we have demonstrated in two independent cohorts with mixed etiologies and ethnicity that the metastasis gene signature is a useful tool to predict HCC outcome, suggesting the general utility of this classifier. We recommend the use of this classifier as a molecular diagnostic test to assess the risk that an HCC patient will develop tumor relaps within 2 years after surgical resection, particularly for those with early stage tumors and solitary presentation.
Subfields: Hepatology, Pulmonary and Respiratory Medicine, Surgery
View Paper: https://www.semanticscholar.org/paper/05eb39c793293c1ebed1f4989f38d56b359df178
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Citations in the corpus
- Circular RNA cSMARCA5 inhibits growth and metastasis in hepatocellular carcinoma. (2018, methodology): “from a Chinese cohort (GSE14520, GPL3921)[37] using GEO2R[38].”
- Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, neutral): “(B) An external cohort of 242 HCC patients from Fudan, China were compared for survival of patients with high versus low p53 signatures as described for panel a. (C) A 100 HCC patient cohort from M.D. Anderson Cancer Center was compared by p53 low and high signature quartiles for overall survival.”
- Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, methodology): “We then performed survival prediction of iCCA or HCC cohort using 10-fold cross validation and 1,000 random permutations based on 15 surrogate gene signatures (Figures S4A and S4B), an approach used successfully in previous studies (Dang et al., 2017; Roessler et al., 2010).”
- Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, methodology): “A total of 221 samples with survival information were chosen from GSE14520 Affymetrix high-throughput GeneChip HG-U133A microarray dataset from an earlier study of patients with HCC (32).”