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Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification

Authors Ke K, Chen G, Cai Z, Huang Y, Zhao B, Wang Y, Liao N, Liu X, Li Z, Liu J

Received 28 June 2018

Accepted for publication 9 September 2018

Published 5 November 2018 Volume 2018:10 Pages 5291—5302

DOI https://doi.org/10.2147/CMAR.S178579

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Ms Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Dr Xueqiong Zhu


Kun Ke,1–3,* Geng Chen,2–4,* Zhixiong Cai,2–4 Yanbing Huang,1–3 Bixing Zhao,2,3 Yingchao Wang,2,3 Naishun Liao,2,3 Xiaolong Liu,2,3 Zhenli Li,2,3 Jingfeng Liu1–3,5

1The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China; 2The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China; 3The Liver Center of Fujian Province, Fujian Medical University, Fuzhou 350025, China; 4School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China; 5Liver Disease Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China

*These authors contributed equally to this work

Purpose: Prediction of hepatocellular carcinoma (HCC) prognosis faced great difficulty due to tumor heterogeneity. We aimed to identify the prognosis-associated molecular subtypes existing in HCC patients and construct an evaluation model based on identified molecular classification.
Materials and methods: Non-negative matrix factorization consensus clustering was performed using 371 HCC patients from The Cancer Genome Atlas (TCGA) to identify molecular subtypes, based on the expression profile of the survival-associated genes. Signature genes for different subtypes were identified by Significance Analysis of Microarray and Prediction Analysis for Microarrays . Model for subtype discrimination and prognosis evaluation was established using binary logistic regression. The model and its clinical implications were further validated in GSE5436 cohort and Fujian cohort.
Results: Based on TCGA data, we observed two molecular subtypes with distinct clinical outcomes including significantly different overall survival, tumor differentiation, TNM stage, and vascular invasion (all P<0.05). The existence of these two molecular subtypes was further validated in five other Gene Expression Omnibus datasets. Furthermore, we constructed an evaluation model based on six subtype signature genes, which can discriminate different subtypes with the cutoff of 0.385. Meanwhile, both Cox regression analysis and stratification analysis showed that the calculated continuous prognostic value could also effectively indicate HCC prognosis, regardless of patients’ clinical conditions. The prognostic evaluation model was successfully validated in GSE54236 cohort and Fujian cohort.
Conclusion: Two prognostic molecular subtypes existed among HCC patients, which provided promising strategies for overcoming HCC heterogeneity and could be utilized in future clinical application for predicting HCC prognosis.

Keywords: hepatocellular carcinoma, transcriptome, molecular classification, prognosis evaluation, HCC heterogeneity

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