Basic Study
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 28, 2018; 24(28): 3145-3154
Published online Jul 28, 2018. doi: 10.3748/wjg.v24.i28.3145
Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas
Yan-Zhou Song, Xu Li, Wei Li, Zhong Wang, Kai Li, Fang-Liang Xie, Feng Zhang
Yan-Zhou Song, Department of General Surgery, Lianyungang Clinical Medical College of Nanjing Medical University/The First People’s Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China
Xu Li, Feng Zhang, Department of Liver Surgery/Liver Transplantation Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Wei Li, Zhong Wang, Kai Li, Fang-Liang Xie, Department of General Surgery, The First People’s Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China
Author contributions: Song YZ and Li X contributed equally to this work; Song YZ wrote the paper and performed the bioinformatic analysis; Li X performed the bioinformatic analysis and summarized the results; Li W, Wang Z, Li K collected and formatted genomic data; Xie FL revised the paper; Zhang F designed the research.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Nanjing Medical University.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: The data in this manuscript was accessible through https://portal.gdc.cancer.gov/ or http://gdac.broadinstitute.org/
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Feng Zhang, MD, Professor, Department of Liver Surgery/Liver Transplantation Center, The First Affiliated Hospital of Nanjing Medical University; 300 Guangzhou Rd, Nanjing 210029, Jiangsu Province, China. doctorzhangfeng@njmu.edu.cn
Telephone: +86-13505145722 Fax: +86-25-83672106
Received: March 21, 2018
Peer-review started: March 22, 2018
First decision: April 24, 2018
Revised: June 13, 2018
Accepted: June 25, 2018
Article in press: June 25, 2018
Published online: July 28, 2018
Processing time: 127 Days and 17.9 Hours
ARTICLE HIGHLIGHTS
Research background

Liver cancer is the fourth most common digestive cancer worldwide. Prognostic markers can help to make better clinical decision by selecting patients who respond well to some specific treatment. Besides traditional clinical markers, genetic biomarkers are emerging as novel indicators in cancer diagnosis and prognosis. The Cancer Genome Atlas (TCGA) is funded by the National Institute of Health (NIH) to describe the genomic alterations across cancer types. It provides tremendous amount of “omics” data, including mRNA sequencing, miRNA sequencing, reverse phase protein arrays, copy number change and DNA sequencing.

Research motivation

Although there seems to be great potential value of the clinical and genetic markers, there is no consensus on the predictive power of these indicators, especially the molecular markers.

Research objectives

By utilizing the TCGA data, we aimed to evaluate the prognostic power of liver cancer by molecular markers, and also to assess the predictive power of liver cancer by combining molecular markers and clinical data.

Research methods

Cox regression screen and least absolute shrinkage and selection operator (LASSO) were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then the heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.

Research results

The mRNA data was the most informative prognostic variables in all kinds of omics data in liver cancer. In the copy number variation (CNV), methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone. On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself. Based on the significant prognostic variables, several prognostic models were built. For the CNV data, score = 10p15.1 - 15q26.3. For the methylation data, score = - REL - MCM2. For the miRNA data, score = miR-3690 + miR-561 - miR-621. For the mRNA data, score = CCDC21 + GTF3C2 + DBF4.

Research conclusions

In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variables.

Research perspectives

The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.