Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases

To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surf...

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Bibliographic Details
Published in:Future oncology (London, England) Vol. 13; no. 10; pp. 875 - 882
Main Authors: Reyes, Adalgiza, Marti, Josep, Marfà, Santiago, Jiménez, Wladimiro, Reichenbach, Vedrana, Pelegrina, Amalia, Fondevila, Constantino, Garcia Valdecasas, Juan Carlos, Fuster, Josep
Format: Journal Article
Language:English
Published: England Future Medicine Ltd 01.04.2017
Subjects:
Adult
Aged
Aged, 80 and over
Arrays
biomarker
Biomarkers
Biomarkers, Tumor
Case-Control Studies
Chemotherapy
Colorectal cancer
Colorectal Neoplasms - mortality
Colorectal Neoplasms - pathology
Female
Genomics
Humans
Laboratories
Liver
liver metastases
Liver Neoplasms - metabolism
Liver Neoplasms - mortality
Liver Neoplasms - secondary
Male
Medical prognosis
Metastasis
Middle Aged
outcomes research
Patients
Prognosis
Proteins
Proteome
proteomic analysis
Proteomics
Proteomics - methods
Surgery
colorectal cancer
outcomes research
biomarker
prognosis
liver metastases
proteomic analysis
ISSN:1479-6694, 1744-8301
Online Access:Get full text
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Summary:To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls). The protein peak 7371 showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 protein peaks (100% sensitivity, 90% specificity). Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
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ISSN:1479-6694
1744-8301
DOI:10.2217/fon-2016-0461