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Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients

Authors :
Gui-Qi Zhu
Yi Yang
Er-Bao Chen
Biao Wang
Kun Xiao
Shi-Ming Shi
Zheng-Jun Zhou
Shao-Lai Zhou
Zheng Wang
Ying-Hong Shi
Jia Fan
Jian Zhou
Tian-Shu Liu
Zhi Dai
Source :
Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-13 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

Abstract Background Due to the phenotypic and molecular diversity of hepatocellular carcinomas (HCC), it is still a challenge to determine patients’ prognosis. We aim to identify new prognostic markers for resected HCC patients. Methods 274 patients were retrospectively identified and samples collected from Zhongshan hospital, Fudan University. We analyzed the gene expression patterns of tumors and compared expression patterns with patient survival times. We identified a “9-gene signature” associated with survival by using the coefficient and regression formula of multivariate Cox model. This molecular signature was then validated in three patients cohorts from internal cohort (n = 69), TCGA (n = 369) and GEO dataset (n = 80). Results We identified 9-gene signature consisting of ZC2HC1A, MARCKSL1, PTGS1, CDKN2B, CLEC10A, PRDX3, PRKCH, MPEG1 and LMO2. The 9-gene signature was used, combined with clinical parameters, to fit a multivariable Cox model to the training cohort (concordance index, ci = 0.85), which was successfully validated (ci = 0.86 for internal cohort; ci = 0.78 for in silico cohort). The signature showed improved performance compared with clinical parameters alone (ci = 0.70). Furthermore, the signature predicted patient prognosis than previous gene signatures more accurately. It was also used to stratify early-stage, HBV or HCV-infected patients into low and high-risk groups, leading to significant differences in survival in training and validation (P

Details

Language :
English
ISSN :
14795876
Volume :
17
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Translational Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.5d115c22976a4aba9fa3debf3537cd07
Document Type :
article
Full Text :
https://doi.org/10.1186/s12967-019-1946-8