Liu XN, Cui DN, Li YF, Liu YH, Liu G, Liu L. Multiple “Omics” data-based biomarker screening for hepatocellular carcinoma diagnosis. World J Gastroenterol 2019; 25(30): 4199-4212 [PMID: 31435173 DOI: 10.3748/wjg.v25.i30.4199]
Corresponding Author of This Article
Lei Liu, PhD, Professor, Institutes of Biomedical Sciences, Fudan University, No. 131 Dongan Rd, Shanghai 200032, China. liulei_sibs@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
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/
World J Gastroenterol. Aug 14, 2019; 25(30): 4199-4212 Published online Aug 14, 2019. doi: 10.3748/wjg.v25.i30.4199
Multiple “Omics” data-based biomarker screening for hepatocellular carcinoma diagnosis
Xiao-Na Liu, Dan-Ni Cui, Yu-Fang Li, Yun-He Liu, Gang Liu, Lei Liu
Xiao-Na Liu, Dan-Ni Cui, Yu-Fang Li, Yun-He Liu, Gang Liu, Lei Liu, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
Author contributions: Liu XN, Cui DN, and Li YF contributed equally to the work; Liu G and Liu L contributed equally to the work; Liu XN, Cui DN, Li YF and Liu G drafted the initial manuscript; Liu YH produced the figures; Liu G conceptualized and designed the review; Liu G and Liu L reviewed the manuscript.
Supported bythe National Key Research and Development Program of China, No. 2016YFC0901903.
Conflict-of-interest statement: The authors have no conflict of interest to declare.
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: Lei Liu, PhD, Professor, Institutes of Biomedical Sciences, Fudan University, No. 131 Dongan Rd, Shanghai 200032, China. liulei_sibs@163.com
Telephone: +86-21-54237325Fax: +86-21-54237325
Received: April 10, 2019 Peer-review started: April 10, 2019 First decision: May 9, 2019 Revised: May 28, 2019 Accepted: July 2, 2019 Article in press: July 3, 2019 Published online: August 14, 2019 Processing time: 128 Days and 7.6 Hours
Abstract
The huge prognostic difference between early and late stage hepatocellular carcinoma (HCC) is a challenging diagnostic problem. Alpha-fetoprotein is the mostly widely used biomarker for HCC used in the clinic, however it’s sensitivity and specificity of is not optimal. The development and application of multiple biotechnologies, including next generation sequencing, multiple “omics” data, that include genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics has been used for HCC diagnostic biomarker screening. Effective biomarkers/panels/models have been identified and validated at different clinical levels. A large proportion of these have a good diagnostic performance for HCC, especially for early HCC. In this article, we reviewed the various HCC biomarkers derived from “omics” data and discussed the advantages and disadvantages for diagnosis HCC.
Core tip: Compared to traditional biomarkers, high throughput technologies provide novel insights and mechanistic understanding of hepatocellular carcinoma (HCC). In this article, recent genomic, epigenomic, transcriptomic, proteomic, metabolomics, and metagenomics based HCC diagnostic biomarkers and their performance was evaluated. The advantages and disadvantages of these HCC diagnostic biomarkers are also discussed.
