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Prognostic values of a novel multi-mRNA signature for predicting relapse of cholangiocarcinoma.


ABSTRACT: Cholangiocarcinoma (CCA) is an epithelial cancer and has high death and recurrence rates, current methods cannot satisfy the need for predicting cancer relapse effectively. So, we aimed at conducting a multi-mRNA signature to improve the relapse prediction of CCA. We analyzed mRNA expression profiling in large CCA cohorts from the Gene Expression Omnibus (GEO) database (GSE76297, GSE32879, GSE26566, GSE31370, and GSE45001) and The Cancer Genome Atlas (TCGA) database. The Least absolute shrinkage and selection operator (LASSO) regression model was used to establish a 7-mRNA-based signature that was significantly related to the recurrence-free survival (RFS) in two test series. Based on the 7-mRNA signature, the cohort TCGA patients could be divided into high-risk or low-risk subgroups with significantly different RFS [p < 0.001, hazard ratio (HR): 48.886, 95% confidence interval (CI): 6.226-3.837E+02]. Simultaneously, the prognostic value of the 7-mRNA signature was confirmed in clinical samples of Ren Ji hospital (p < 0.001, HR: 4.558, 95% CI: 1.829-11.357). Further analysis including multivariable and sub-group analyses revealed that the 7-mRNA signature was an independent prognostic value for recurrence of patients with CCA. In conclusion, our results might provide an efficient tool for relapse prediction and were beneficial to individualized management for CCA patients.

SUBMITTER: Guo H 

PROVIDER: S-EPMC7019144 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic values of a novel multi-mRNA signature for predicting relapse of cholangiocarcinoma.

Guo Han H   Cai Jie J   Wang Xuan X   Wang Bingrui B   Wang Fang F   Li Xiang X   Qu Xiaoye X   Kong Xianming X   Gao Yueqiu Y   Wu Hailong H   Sun Xuehua X   Xia Qiang Q   Kong Xiaoni X  

International journal of biological sciences 20200116 5


Cholangiocarcinoma (CCA) is an epithelial cancer and has high death and recurrence rates, current methods cannot satisfy the need for predicting cancer relapse effectively. So, we aimed at conducting a multi-mRNA signature to improve the relapse prediction of CCA. We analyzed mRNA expression profiling in large CCA cohorts from the Gene Expression Omnibus (GEO) database (GSE76297, GSE32879, GSE26566, GSE31370, and GSE45001) and The Cancer Genome Atlas (TCGA) database. The Least absolute shrinkage  ...[more]

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