Case Control Study
Copyright ©The Author(s) 2016. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 28, 2016; 22(16): 4168-4182
Published online Apr 28, 2016. doi: 10.3748/wjg.v22.i16.4168
Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model
Abdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Mohamed Abouelhoda, Abeer A Bahnassy
Abdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt
Mohamed Abouelhoda, Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza 16453, Egypt
Mohamed Abouelhoda, Center for Informatics Sciences, Nile University, Sheikh Zayed City, Giza 16453, Egypt
Abeer A Bahnassy, Pathology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt
Author contributions: Zekri AR designed the study, edited the manuscript and helped in creating the model; Youssef AS participated in sample collection and manuscript writing and editing; Bakr YM assisted with the practical work and statistical analysis; Gabr RM and Ahmed OS assisted with the practical work; Elberry MH, Mayla AM and Bahnassy AA assisted with editing the manuscript; Abouelhoda M assited with creating the model.
Supported by National Cancer Institute, Cairo University, Cairo, Egypt.
Institutional review board statement: The study was approved by the Ethics Committee of National Cancer Institute, Cairo University, Cairo, Egypt (IRB No. 00004025, IORG. 0003381).
Informed consent statement: All patients gave informed consent.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
Data sharing statement: The technical appendix, statistical code, and dataset are available from the corresponding author at ncizekri@yahoo.com.
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: Abdel-Rahman Nabawy Zekri, PhD, Head, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Kasr Al-Aini street, Fom El-Khaleeg, Cairo 11976, Egypt. ncizekri@yahoo.com
Telephone: +20-2-22742607 Fax: +20-2-23644720
Received: November 30, 2015
Peer-review started: December 2, 2015
First decision: January 13, 2016
Revised: February 16, 2016
Accepted: March 1, 2016
Article in press: March 2, 2016
Published online: April 28, 2016
Abstract

AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).

METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups.

RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model (-11.3 + 7.38 × Prot + 0.00108 × sICAM + 0.2574 ×β-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × sICAM-1 + (4.251 × 10-4) × sTNF + 0.02567 ×β-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status.

CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.

Keywords: Mathematical model, Hepatocellular carcinoma, α-fetoprotein, Soluble intercellular adhesion molecule-1, β-catenin, Interleukin-8, Soluble tumor necrosis factor receptor II, Proteasome

Core tip: Hepatocellular carcinoma is one of the most common liver malignancies. We sought to create a mathematical model from a panel of serum proteins (intercellular adhesion molecules, beta catenin, interleukin-8, the proteasome, and soluble tumor necrosis factor receptor II) in combination with α-fetoprotein to aid in early detection of hepatocellular carcinoma. This panel was measured in 384 subjects infected with hepatitis C virus (HCV) as well as 95 healthy control subjects negative for HCV. Finally, we created mathematical models that may be valuable tools for the early detection of different statuses of liver disease co-occurring with HCV infection.