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A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer

BACKGROUND: Colon cancer occurrence is increasing worldwide, making it the third most frequent cancer. Although many therapeutic options are available and quite efficient at the early stages, survival is strongly decreased when the disease has spread to other organs. The identification of molecular...

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Autores principales: Durand, Stéphanie, Trillet, Killian, Uguen, Arnaud, Saint-Pierre, Aude, Le Jossic-Corcos, Catherine, Corcos, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622428/
https://www.ncbi.nlm.nih.gov/pubmed/28962550
http://dx.doi.org/10.1186/s12864-017-4139-y
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author Durand, Stéphanie
Trillet, Killian
Uguen, Arnaud
Saint-Pierre, Aude
Le Jossic-Corcos, Catherine
Corcos, Laurent
author_facet Durand, Stéphanie
Trillet, Killian
Uguen, Arnaud
Saint-Pierre, Aude
Le Jossic-Corcos, Catherine
Corcos, Laurent
author_sort Durand, Stéphanie
collection PubMed
description BACKGROUND: Colon cancer occurrence is increasing worldwide, making it the third most frequent cancer. Although many therapeutic options are available and quite efficient at the early stages, survival is strongly decreased when the disease has spread to other organs. The identification of molecular markers of colon cancer is likely to help understanding its course and, eventually, to uncover novel genes to be targeted by drugs. In this study, we compared gene expression in a set of 95 human colon cancer samples to that in 19 normal colon mucosae, focusing on 401 genes from 5 selected pathways (Apoptosis, Cancer, Cholesterol metabolism and lipoprotein signaling, Drug metabolism, Wnt/beta-catenin). Deregulation of mRNA levels largely matched that of proteins, leading us to build in silico protein networks, starting from mRNA levels, to identify key proteins central to network activity. RESULTS: Among the analyzed genes, 10.5% (42) had no reported link with colon cancer, including the SFRP1, IGF1 and ADH1B (down), and MYC and IL8 (up), whose encoded proteins were most interacting with other proteins from the same or even distinct networks. Analyzing all pathways globally led us to uncover novel functional links between a priori unrelated or rather remotely connected pathways, such as the Drug metabolism and the Cancer pathways or, even more strikingly, between the Cholesterol metabolism and lipoprotein signaling and the Cancer pathways. In addition, we analyzed the responsiveness of some of the deregulated genes essential to network activities, to chemotherapeutic agents used alone or in presence of Lovastatin, a lipid-lowering drug. Some of these treatments could oppose the deregulations occurring in cancer samples, including those of the CHECK2, CYP51A1, HMGCS1, ITGA2, NME1 or VEGFA genes. CONCLUSIONS: Our network-based approach allowed discovering genes not previously known to play regulatory roles in colon cancer. Our results also showed that selected drug treatments might revert the cancer-specific deregulation of genes playing prominent roles within the networks operating to maintain colon homeostasis. Among those genes, some could constitute novel testable targets to eliminate colon cancer cells, either directly or, potentially, through the use of lipid-lowering drugs such as statins, in association with selected anticancer drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-017-4139-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-56224282017-10-11 A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer Durand, Stéphanie Trillet, Killian Uguen, Arnaud Saint-Pierre, Aude Le Jossic-Corcos, Catherine Corcos, Laurent BMC Genomics Research Article BACKGROUND: Colon cancer occurrence is increasing worldwide, making it the third most frequent cancer. Although many therapeutic options are available and quite efficient at the early stages, survival is strongly decreased when the disease has spread to other organs. The identification of molecular markers of colon cancer is likely to help understanding its course and, eventually, to uncover novel genes to be targeted by drugs. In this study, we compared gene expression in a set of 95 human colon cancer samples to that in 19 normal colon mucosae, focusing on 401 genes from 5 selected pathways (Apoptosis, Cancer, Cholesterol metabolism and lipoprotein signaling, Drug metabolism, Wnt/beta-catenin). Deregulation of mRNA levels largely matched that of proteins, leading us to build in silico protein networks, starting from mRNA levels, to identify key proteins central to network activity. RESULTS: Among the analyzed genes, 10.5% (42) had no reported link with colon cancer, including the SFRP1, IGF1 and ADH1B (down), and MYC and IL8 (up), whose encoded proteins were most interacting with other proteins from the same or even distinct networks. Analyzing all pathways globally led us to uncover novel functional links between a priori unrelated or rather remotely connected pathways, such as the Drug metabolism and the Cancer pathways or, even more strikingly, between the Cholesterol metabolism and lipoprotein signaling and the Cancer pathways. In addition, we analyzed the responsiveness of some of the deregulated genes essential to network activities, to chemotherapeutic agents used alone or in presence of Lovastatin, a lipid-lowering drug. Some of these treatments could oppose the deregulations occurring in cancer samples, including those of the CHECK2, CYP51A1, HMGCS1, ITGA2, NME1 or VEGFA genes. CONCLUSIONS: Our network-based approach allowed discovering genes not previously known to play regulatory roles in colon cancer. Our results also showed that selected drug treatments might revert the cancer-specific deregulation of genes playing prominent roles within the networks operating to maintain colon homeostasis. Among those genes, some could constitute novel testable targets to eliminate colon cancer cells, either directly or, potentially, through the use of lipid-lowering drugs such as statins, in association with selected anticancer drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-017-4139-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-30 /pmc/articles/PMC5622428/ /pubmed/28962550 http://dx.doi.org/10.1186/s12864-017-4139-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Durand, Stéphanie
Trillet, Killian
Uguen, Arnaud
Saint-Pierre, Aude
Le Jossic-Corcos, Catherine
Corcos, Laurent
A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title_full A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title_fullStr A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title_full_unstemmed A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title_short A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
title_sort transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5622428/
https://www.ncbi.nlm.nih.gov/pubmed/28962550
http://dx.doi.org/10.1186/s12864-017-4139-y
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