Year: 2006
Type: article
Study design: Observational Study
Abstract: Background and Aims: Hepatocellular carcinoma (HCC) is the one of the most common cancer worldwide.Proteomic pattern discrimination of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression.The objective of our study was to assess the use of proteomic pattern discrimination to identify multiple serum protein biomarkers for detection of liver disease progression to HCC.Methods: We developed and used a bioinformatics tool to identify proteomic patterns in serum of patients with HCC from those without HCC.A cohort of 140 serum samples obtained from subjects with cirrhosis (n 70), or HCC (n 70) were enrolled.A preliminary "training" set of spectra derived from the serum analysis from the 20 patients with HCC and 20 age-matched cirrhotic patients without HCC were analyzed by an iterative searching algorithm.The algorithm identified a proteomic pattern and discriminated sera of patients with HCC from those without HCC completely.The discovered pattern was then used to classify an independent set of 100 masked serum samples.Results: This proteomic pattern analysis yielded a sensitivity of 99% and a specificity of 94%, respectively to differentiate sera of patients with HCC from those without HCC.Under the conditions tested, 9 novel biomarkers were identified.Six of markers showed increased expression in patients with HCC, whereas three of markers showed decreased expression in patients with HCC.Conclusions: Our bioinformatics tool with protein pattern on 2-D gels is able to distinguish the serum of patients with HCC from those without HCC.Further studies may help to improve the outcome for patients with HCC by enabling the diagnosis to be made at an earlier stage of the disease when curative resection and/or transplantation treatment is possible.
Source: Journal of Hepatology
Authors: E.R. Lemmer, Y. Chen, S. Yea, E. Wurmbach, M. Schwartz, A. Villanueva, G. Narla, V. Mazzaferro, J. Bruix, S. Waxman, S.L. Friedman, J.M. Llovet Sinai
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,500
OpenAlex ID: W2001853979
