Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study

World Journal of Surgical Oncology

Research

Anuja Mehta¹  John D Norton¹  Simon Marsh²  Tan Arulampalam²  Christopher CL Liao³  Nicholas J Ward³ 

[1] Department of Biological Sciences, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK;ICENI Centre, Department of Surgery, Colchester Hospital University NHS Foundation Trust, Turner Road, CO4 5JL, Colchester, UK;ICENI Centre, Department of Surgery, Colchester Hospital University NHS Foundation Trust, Turner Road, CO4 5JL, Colchester, UK;Department of Biological Sciences, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK;

关键词: Colorectal Cancer Patient;    Protein Expression Profile;    Weighted Vote;    Supervise Learning Algorithm;    Blood Seron;   

DOI  :  10.1186/1477-7819-8-33

 received in 2009-11-22, accepted in 2010-04-26,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMass spectrometry-based protein expression profiling of blood sera can be used to discriminate colorectal cancer (CRC) patients from unaffected individuals. In a pilot methodological study, we have evaluated the changes in protein expression profiles of sera from CRC patients that occur following surgery to establish the potential of this approach for monitoring post-surgical response and possible early prediction of disease recurrence.MethodsIn this initial pilot study, serum specimens from 11 cancer patients taken immediately prior to surgery and at approximately 6 weeks following surgery were analysed alongside 10 normal control sera by matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Using a two-sided t-test the top 20 ranked protein peaks that discriminate normal from pre-operative sera were identified. These were used to classify post-operative sera by hierarchical clustering analysis (Spearman's Rank correlation) and, as an independent 'test' dataset, by k-nearest neighbour and weighted voting supervised learning algorithms.ResultsHierarchical cluster analysis classified post-operative sera from all six early Dukes' stage (A and B) patients as normal. The remaining five post-operative sera from more advanced Dukes' stages (C1 and C2) were classified as cancer. Analysis by supervised learning algorithms similarly grouped all advanced Dukes' stages as cancer, with four of the six post-operative sera from early Dukes' stages being classified as normal (P = 0.045; Fisher's exact test).ConclusionsThe results of this pilot methodological study illustrate the proof-of-concept of using protein expression profiling of post-surgical blood sera from individual patients to monitor disease course. Further validation on a larger patient cohort and using an independent post-operative sera dataset would be required to evaluate the potential clinical relevance of this approach. Prospective data, including follow-up on patient survival, could in the future, then be evaluated to inform decisions on individualised treatment modalities.

【 授权许可】

Unknown   
© Liao et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.