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Pan-cancer analysis of TCGA data reveals notable signaling pathways

BACKGROUND: A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is impor...

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Autores principales: Neapolitan, Richard, Horvath, Curt M., Jiang, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501083/
https://www.ncbi.nlm.nih.gov/pubmed/26169172
http://dx.doi.org/10.1186/s12885-015-1484-6
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author Neapolitan, Richard
Horvath, Curt M.
Jiang, Xia
author_facet Neapolitan, Richard
Horvath, Curt M.
Jiang, Xia
author_sort Neapolitan, Richard
collection PubMed
description BACKGROUND: A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition. METHODS: We present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types. RESULTS: In each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring. CONCLUSIONS: We obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-1484-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-45010832015-07-15 Pan-cancer analysis of TCGA data reveals notable signaling pathways Neapolitan, Richard Horvath, Curt M. Jiang, Xia BMC Cancer Research Article BACKGROUND: A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition. METHODS: We present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types. RESULTS: In each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring. CONCLUSIONS: We obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-1484-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-07-14 /pmc/articles/PMC4501083/ /pubmed/26169172 http://dx.doi.org/10.1186/s12885-015-1484-6 Text en © Neapolitan et al. 2015 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/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
Neapolitan, Richard
Horvath, Curt M.
Jiang, Xia
Pan-cancer analysis of TCGA data reveals notable signaling pathways
title Pan-cancer analysis of TCGA data reveals notable signaling pathways
title_full Pan-cancer analysis of TCGA data reveals notable signaling pathways
title_fullStr Pan-cancer analysis of TCGA data reveals notable signaling pathways
title_full_unstemmed Pan-cancer analysis of TCGA data reveals notable signaling pathways
title_short Pan-cancer analysis of TCGA data reveals notable signaling pathways
title_sort pan-cancer analysis of tcga data reveals notable signaling pathways
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501083/
https://www.ncbi.nlm.nih.gov/pubmed/26169172
http://dx.doi.org/10.1186/s12885-015-1484-6
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