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Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients

A positive family history is a strong and consistently reported risk factor for gastric cancer (GC). So far, it has been demonstrated that serum pepsinogens (PGs), and gastrin 17 (G17) are useful for screening individuals at elevated risk to develop atrophic gastritis but they are suboptimal biomark...

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Autores principales: Corona, Giuseppe, Cannizzaro, Renato, Miolo, Gianmaria, Caggiari, Laura, De Zorzi, Mariangela, Repetto, Ombretta, Steffan, Agostino, De Re, Valli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877611/
https://www.ncbi.nlm.nih.gov/pubmed/29518896
http://dx.doi.org/10.3390/ijms19030750
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author Corona, Giuseppe
Cannizzaro, Renato
Miolo, Gianmaria
Caggiari, Laura
De Zorzi, Mariangela
Repetto, Ombretta
Steffan, Agostino
De Re, Valli
author_facet Corona, Giuseppe
Cannizzaro, Renato
Miolo, Gianmaria
Caggiari, Laura
De Zorzi, Mariangela
Repetto, Ombretta
Steffan, Agostino
De Re, Valli
author_sort Corona, Giuseppe
collection PubMed
description A positive family history is a strong and consistently reported risk factor for gastric cancer (GC). So far, it has been demonstrated that serum pepsinogens (PGs), and gastrin 17 (G17) are useful for screening individuals at elevated risk to develop atrophic gastritis but they are suboptimal biomarkers to screen individuals for GC. The main purpose of this study was to investigate serum metabolomic profiles to find additional biomarkers that could be integrated with serum PGs and G17 to improve the diagnosis of GC and the selection of first-degree relatives (FDR) at higher risk of GC development. Serum metabolomic profiles included 188 serum metabolites, covering amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexoses. Serum metabolomic profiles were performed with tandem mass spectrometry using the Biocrates AbsoluteIDQ p180 kit. The initial cohort (training set) consisted of n = 49 GC patients and n = 37 FDR. Differential metabolomic signatures among the two groups were investigated by univariate and multivariate partial least square differential analysis. The most significant metabolites were further selected and validated in an independent group of n = 22 GC patients and n = 17 FDR (validation set). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic power and the optimal cut-off for each of the discriminant markers. Multivariate analysis was applied to associate the selected serum metabolites, PGs, G17 and risk factors such as age, gender and Helicobacter pylori (H. pylori) infection with the GC and FDR has been performed and an integrative risk prediction algorithm was developed. In the training set, 40 metabolites mainly belonging to phospholipids and acylcarnitines classes were differentially expressed between GC and FDR. Out of these 40 metabolites, 9 were further confirmed in the validation set. Compared with FDR, GC patients were characterized by lower levels of hydroxylated sphingomyelins (SM(OH)22:1, SM(OH)22:2, SM(OH)24:1) and phosphatidylcholines (PC ae 40:1, PC ae 42:2, PC ae 42:3) and by higher levels of acylcarnitines derivatives (C2, C16, C18:1). The specificity and sensitivity of the integrative risk prediction analysis of metabolites for GC was 73.47% and 83.78% respectively with an area under the curve of the ROC curve of 0.811 that improves to 0.90 when metabolites were integrated with the serum PGs. The predictive risk algorithm composed of the C16, SM(OH)22:1 and PG-II serum levels according to the age of individuals, could be used to stratify FDR at high risk of GC development, and then this can be addressed with diagnostic gastroscopy.
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spelling pubmed-58776112018-04-09 Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients Corona, Giuseppe Cannizzaro, Renato Miolo, Gianmaria Caggiari, Laura De Zorzi, Mariangela Repetto, Ombretta Steffan, Agostino De Re, Valli Int J Mol Sci Article A positive family history is a strong and consistently reported risk factor for gastric cancer (GC). So far, it has been demonstrated that serum pepsinogens (PGs), and gastrin 17 (G17) are useful for screening individuals at elevated risk to develop atrophic gastritis but they are suboptimal biomarkers to screen individuals for GC. The main purpose of this study was to investigate serum metabolomic profiles to find additional biomarkers that could be integrated with serum PGs and G17 to improve the diagnosis of GC and the selection of first-degree relatives (FDR) at higher risk of GC development. Serum metabolomic profiles included 188 serum metabolites, covering amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexoses. Serum metabolomic profiles were performed with tandem mass spectrometry using the Biocrates AbsoluteIDQ p180 kit. The initial cohort (training set) consisted of n = 49 GC patients and n = 37 FDR. Differential metabolomic signatures among the two groups were investigated by univariate and multivariate partial least square differential analysis. The most significant metabolites were further selected and validated in an independent group of n = 22 GC patients and n = 17 FDR (validation set). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic power and the optimal cut-off for each of the discriminant markers. Multivariate analysis was applied to associate the selected serum metabolites, PGs, G17 and risk factors such as age, gender and Helicobacter pylori (H. pylori) infection with the GC and FDR has been performed and an integrative risk prediction algorithm was developed. In the training set, 40 metabolites mainly belonging to phospholipids and acylcarnitines classes were differentially expressed between GC and FDR. Out of these 40 metabolites, 9 were further confirmed in the validation set. Compared with FDR, GC patients were characterized by lower levels of hydroxylated sphingomyelins (SM(OH)22:1, SM(OH)22:2, SM(OH)24:1) and phosphatidylcholines (PC ae 40:1, PC ae 42:2, PC ae 42:3) and by higher levels of acylcarnitines derivatives (C2, C16, C18:1). The specificity and sensitivity of the integrative risk prediction analysis of metabolites for GC was 73.47% and 83.78% respectively with an area under the curve of the ROC curve of 0.811 that improves to 0.90 when metabolites were integrated with the serum PGs. The predictive risk algorithm composed of the C16, SM(OH)22:1 and PG-II serum levels according to the age of individuals, could be used to stratify FDR at high risk of GC development, and then this can be addressed with diagnostic gastroscopy. MDPI 2018-03-07 /pmc/articles/PMC5877611/ /pubmed/29518896 http://dx.doi.org/10.3390/ijms19030750 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Corona, Giuseppe
Cannizzaro, Renato
Miolo, Gianmaria
Caggiari, Laura
De Zorzi, Mariangela
Repetto, Ombretta
Steffan, Agostino
De Re, Valli
Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title_full Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title_fullStr Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title_full_unstemmed Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title_short Use of Metabolomics as a Complementary Omic Approach to Implement Risk Criteria for First-Degree Relatives of Gastric Cancer Patients
title_sort use of metabolomics as a complementary omic approach to implement risk criteria for first-degree relatives of gastric cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877611/
https://www.ncbi.nlm.nih.gov/pubmed/29518896
http://dx.doi.org/10.3390/ijms19030750
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