Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 14, 2020; 26(2): 134-153
Published online Jan 14, 2020. doi: 10.3748/wjg.v26.i2.134
Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment
Fa-Peng Zhang, Yi-Pei Huang, Wei-Xin Luo, Wan-Yu Deng, Chao-Qun Liu, Lei-Bo Xu, Chao Liu
Fa-Peng Zhang, Yi-Pei Huang, Wei-Xin Luo, Wan-Yu Deng, Chao-Qun Liu, Lei-Bo Xu, Chao Liu, Department of Biliary Pancreatic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, Guangdong Province, China
Fa-Peng Zhang, Yi-Pei Huang, Wei-Xin Luo, Wan-Yu Deng, Chao-Qun Liu, Lei-Bo Xu, Chao Liu, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, Guangdong Province, China
Wan-Yu Deng, College of Life Science, Shangrao Normal University, Shangrao 334001, Jiangxi Province, China
Author contributions: Zhang FP, Xu LB and Liu C designed the research; Zhang FP, Huang YP and Luo WX collected and analyzed data; Deng WY and Liu CQ prepared the figures; Zhang FP, Xu LB and Liu C wrote and revised the manuscript.
Supported by National Natural Science Foundation of China, No. 81972255, No. 81772597 and No. 81672412; Guangdong Natural Science Foundation, No. 2017A030311002; Guangdong Science and Technology Foundation, No. 2017A020215196; Fundamental Research Funds for the Central Universities of Sun Yat-Sen University, No. 17ykpy44; Science Foundation of Jiangxi, No. 20181BAB214002; Education Department Science and Technology Foundation of Jiangxi, No. GJJ170936; and Grant from Guangdong Science and Technology Department, No. 2017B030314026.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Sun Yat-Sen Memorial Hospital, Guangzhou, China.
Conflict-of-interest statement: All authors declare no conflict-of-interest related to this article.
Data sharing statement: The data used in this manuscript are accessible at https://cancergenome.nih.gov/, https://icgc.org/, and https://www.ncbi.nlm.nih.gov/geo/.
ARRIVE guidelines statement: The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
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/
Corresponding author: Chao Liu, MD, PhD, Chairman, Director, Professor, Department of Hepato-Pancreato-Biliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, Guangdong Province, China. liuchao3@mail.sysu.edu.cn
Received: September 30, 2019
Peer-review started: September 30, 2019
First decision: November 10, 2019
Revised: November 23, 2019
Accepted: December 7, 2019
Article in press: January 7, 2020
Published online: January 14, 2020
Processing time: 105 Days and 1.8 Hours
ARTICLE HIGHLIGHTS
Research background

Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. In recent years, immunotherapy has emerged as a novel and effective therapy and is being applied in various tumors including HCC. However, the influence of genes involved in the tumor microenvironment on the prognosis of HCC patients remains unclear. And the high-throughput studies that investigated the potential prognostic role of immune prognostic models in HCC are still lacking.

Research motivation

So far, only a small number of HCC patients receiving immunotherapy treatment exhibited responses due to the immunosuppressive microenvironment. Hence, it is necessary to investigate the HCC microenvironment to identify prognostic genes that enable us to predict the benefit of immunotherapy, which may help in clinical decision making for individualized treatment.

Research objectives

To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction and effectiveness of immunotherapy of HCC, we analyzed the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) databases.

Research methods

We computed the immune/stromal scores of HCC patients obtained from TCGA based on the ESTIMATE algorithm. Univariate analysis, multivariate analysis and the least absolute shrinkage and selection operator, were utilized to construct our predictive model. This model was performed based on the significant differentially expressed genes screened established based on mRNA expression profiles from the TCGA database. The robustness of this model was validated using GEO and ICGC datasets.

Research results

The risk score model consisting of eight genes (Disabled homolog 2, Musculin, C-X-C motif chemokine ligand 8, Galectin 3, B-cell-activating transcription factor, Killer cell lectin like receptor B1, Endoglin, and Adenomatosis polyposis coli tumor suppressor) was constructed and validated based on HCC patients who were divided into high- or low-risk group. The receiver operating characteristic curve analysis confirmd the good potency of the risk score prognostic model. Moreover, we investigated the relationship between patient risk scores and the expression of common immune checkpoints, and the results showed that the risk score was significantly associated with the expression of Cytotoxic T-Lymphocyte associated protein 4, Programmed cell death 1, and T-cell immunoglobulin mucin receptor 3. To establish a clinically applicable method to assess the prognosis of HCC patients, a nomogram involving risk score and the pathologic stage was formulated.

Research conclusions

Our research established and validated a risk score model that is based on eight immune-related genes to predict the overall survival of HCC, which may help in clinical decision making for individualized treatment. The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.

Research perspectives

The risk score model provides an immunological viewpoint to clarify the mechanisms that determine the clinical outcome of HCC. Identifying effective molecular biomarkers and predictive markers of immunotherapy is a future direction for improving the effectiveness of immunotherapy.