Case Control Study
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 21, 2018; 24(3): 371-378
Published online Jan 21, 2018. doi: 10.3748/wjg.v24.i3.371
Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma
Hui Xie, Yao-Qin Xue, Peng Liu, Peng-Jun Zhang, Sheng-Tao Tian, Zhao Yang, Zhi Guo, Hua-Ming Wang
Hui Xie, Sheng-Tao Tian, Zhao Yang, Hua-Ming Wang, Department of Interventional Therapy, 302 Hospital of People’s Liberation Army, Beijing 100039, China
Yao-Qin Xue, Zhi Guo, Department of Interventional Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300070, China
Yao-Qin Xue, Department of Interventional Therapy, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
Peng Liu, Peng-Jun Zhang, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
Author contributions: Xie H, Xue YQ, Liu P, Guo Z and Wang HM designed the study; Xie H, Xue YQ and Liu P performed the research; Xie H, Zhang PJ, Tian ST and Zhao Y analyzed the data; Xie H, Xue YQ and Liu P wrote the paper; Guo Z and Wang HM revised the manuscript for final submission; Xie H, Xue YQ and Liu P contributed equally to this study; Guo Z and Wang HM are the co-corresponding authors.
Supported by National Key R&D Program of China, No. 2016YFC0106604; and National Natural Science Foundation of China, No. 81471761 and No. 81501568.
Institutional review board statement: The study was reviewed and approved by the 302 Hospital of People’s Liberation Army Institutional Review Board.
Informed consent statement: All study participants or their legal guardian provided written informed consent prior to study enrollment.
Conflict-of-interest statement: We declare that we have no financial or personal relationships with other individuals or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript.
Data sharing statement: The study participants provided informed consent for data sharing. No additional data are available.
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: Hua-Ming Wang, BCPS, MD, Chief Doctor, Professor, Department of Interventional Therapy, 302 Hospital of People’s Liberation Army, 100 Middle West 4th Ring Road, Fengtai District, Beijing 100039, China. hmwang302@126.com
Telephone: +86-10-66933263 Fax: +86-10-66933263
Received: August 9, 2017
Peer-review started: August 9, 2017
First decision: August 29, 2017
Revised: October 16, 2017
Accepted: November 21, 2017
Article in press: November 21, 2017
Published online: January 21, 2018
Processing time: 163 Days and 7.7 Hours
Abstract
AIM

In our previous study, we have built a nine-gene (GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1B, CXCR4, PFN1, and CALR) expression detection system based on the GeXP system. Based on peripheral blood and GeXP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma (HCC) patients and healthy people.

METHODS

Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fifty-two patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.

RESULTS

Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.

CONCLUSION

Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.

Keywords: Hepatocellular carcinoma; Peripheral blood; Early detection; Multi-parameter; Diagnostic value

Core tip: We aimed to analyze the results of expression of nine genes, which we identified previously, by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma patients and healthy people. Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis.