Oncotarget

Research Papers:

An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients

Peng Lin, Rong-Quan He, Yi-Wu Dang, Dong-Yue Wen, Jie Ma, Yun He, Gang Chen _ and Hong Yang

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Oncotarget. 2018; 9:17368-17395. https://doi.org/10.18632/oncotarget.24089

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Abstract

Peng Lin1,*, Rong-Quan He2,*, Yi-Wu Dang3, Dong-Yue Wen1, Jie Ma2, Yun He1, Gang Chen3 and Hong Yang1

1Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P. R. China

2Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P. R. China

3Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P. R. China

*These authors contributed equally to this work

Correspondence to:

Gang Chen, email: [email protected]

Hong Yang, email: [email protected]

Keywords: prognosis index; HCC; survival; autophagy

Received: October 05, 2017     Accepted: January 03, 2018     Epub: January 09, 2018     Published: April 03, 2018

ABSTRACT

Prognostic signatures have been proposed as clinical tools to estimate prognosis in hepatocellular carcinoma (HCC), which is the second most common contributor to cancer-related death at present globally. Autophagy-related genes play a dynamic and fundamental role in HCC, but knowledge of their utility as prognostic markers is limited. Here, we facilitated univariate and multivariate Cox proportional hazards regression analyses to reveal that 3 autophagy-related genes (BIRC5, FOXO1 and SQSTM1) were closely related to the survival of HCC. Then, we generated a prognosis index (PI) for predicting overall survival (OS) based on the three genes, which was an independent prognostic indicator for the OS of HCC (HR = 1.930, 95% CI: 1.200–3.104, P = 0.007). The PI showed moderate performance for predicting the survival of HCC patients and its efficacy was validated by data from three microarrays (GSE10143, GSE10186 and GSE17856). Furthermore, we deeply mined the integrated large-scale datasets from public microarrays and immunohistochemistry to validate the overexpression of BIRC5 and SQSTM1 while down-regulated FOXO1 expression in HCC. Bioinformatic analysis offered the hypothesis that proliferative signals in high-risk HCC patients were disturbing and thereby facilitated inferior clinical outcomes. Collectively, the prognostic signature we proposed is a promising biomarker for monitoring outcome of HCC. Nevertheless, prospective experimental studies are needed to validate the clinical utility.


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