Proteomic identification and profiling of canine lymphoma patients.

Ratcliffe, L, Mian, S, Slater, K, King, H, Napolitano, M, Aucoin, D and Mobasheri, A (2009) Proteomic identification and profiling of canine lymphoma patients. Vet Comp Oncol, 7 (2). pp. 92-105.

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Abstract

This study employed proteomic and bioinformatic approaches to identify serum biomarkers in canine lymphoma patients. Chilled serum samples derived from non-lymphoma (n = 92) and lymphoma (n = 87) patients were shipped from first opinion veterinary practices, subjected to ion exchange chromatography and analysed by surface-enhanced laser desorption ionization mass spectrometry. Nineteen serum protein peaks were identified between the two groups as being significantly different (P < 0.05) based upon their normalized ion intensities. Two biomarkers were identified that were capable of differentiating lymphoma and non-lymphoma patients. Analysis of the test data provided a positive predictive value (PPV) of 82%. A clinical follow-up study was carried out on 96 canine patients suspected of having lymphoma. Evaluation of this data gave a specificity value of 91%, sensitivity of 75%, PPV of 80% and negative predictive value of 88%. In conclusion, the expression pattern of two serum biomarkers has enabled serum samples to be classified into either lymphoma or non-lymphoma categories.

Item Type:	Article
Authors :	Name Email ORCID Ratcliffe, L UNSPECIFIED UNSPECIFIED Mian, S UNSPECIFIED UNSPECIFIED Slater, K UNSPECIFIED UNSPECIFIED King, H UNSPECIFIED UNSPECIFIED Napolitano, M UNSPECIFIED UNSPECIFIED Aucoin, D UNSPECIFIED UNSPECIFIED Mobasheri, A a.mobasheri@surrey.ac.uk UNSPECIFIED
Date :	June 2009
Identification Number :	https://doi.org/10.1111/j.1476-5829.2009.00165.x
Uncontrolled Keywords :	Age Factors, Animals, Biomarkers, Tumor, Blood Proteins, Case-Control Studies, Diagnosis, Differential, Dog Diseases, Dogs, Lymphoma, Predictive Value of Tests, Proteomics, Sensitivity and Specificity, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Related URLs :	Author
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 10:12
Last Modified :	17 May 2017 14:47
URI:	http://epubs.surrey.ac.uk/id/eprint/826797

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