Published online Jun 8, 2015. doi: 10.4254/wjh.v7.i10.1312
Peer-review started: August 15, 2014
First decision: September 28, 2014
Revised: February 14, 2015
Accepted: March 5, 2015
Article in press: March 9, 2015
Published online: June 8, 2015
Processing time: 292 Days and 11.6 Hours
Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.
Core tip: The projected rise in hepatocellular carcinoma (HCC) is largely attributed to hepatitis C virus infection with onset of HCC being a latent consequence occurring decades after the original infection. However, other environmental risk factors including alcohol, tobacco, and diet-derived insults that cause liver injury increase the incidence of HCC. The poor prognosis associated with late stage diagnosis renders successful intervention difficult. The methodology described in this review article shows the feasibility of a highly multiplexed manner using multiple reaction monitoring using internal standard peptides to more easily quantify proteins, which narrows the time between discovery and validation in the biomarker pipeline in general.