Cargando…

A systems biology approach to the global analysis of transcription factors in colorectal cancer

BACKGROUND: Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only...

Descripción completa

Detalles Bibliográficos
Autores principales: Pradhan, Meeta P, Prasad, Nagendra KA, Palakal, Mathew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539921/
https://www.ncbi.nlm.nih.gov/pubmed/22852817
http://dx.doi.org/10.1186/1471-2407-12-331
_version_ 1782255165676453888
author Pradhan, Meeta P
Prasad, Nagendra KA
Palakal, Mathew J
author_facet Pradhan, Meeta P
Prasad, Nagendra KA
Palakal, Mathew J
author_sort Pradhan, Meeta P
collection PubMed
description BACKGROUND: Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity’s own significance but also uncover relationships with novel biological entities. To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). METHODS: We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. RESULTS: The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. CONCLUSIONS: Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.
format Online
Article
Text
id pubmed-3539921
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-35399212013-01-10 A systems biology approach to the global analysis of transcription factors in colorectal cancer Pradhan, Meeta P Prasad, Nagendra KA Palakal, Mathew J BMC Cancer Research Article BACKGROUND: Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity’s own significance but also uncover relationships with novel biological entities. To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). METHODS: We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. RESULTS: The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. CONCLUSIONS: Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC. BioMed Central 2012-08-01 /pmc/articles/PMC3539921/ /pubmed/22852817 http://dx.doi.org/10.1186/1471-2407-12-331 Text en Copyright ©2012 Pradhan et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Pradhan, Meeta P
Prasad, Nagendra KA
Palakal, Mathew J
A systems biology approach to the global analysis of transcription factors in colorectal cancer
title A systems biology approach to the global analysis of transcription factors in colorectal cancer
title_full A systems biology approach to the global analysis of transcription factors in colorectal cancer
title_fullStr A systems biology approach to the global analysis of transcription factors in colorectal cancer
title_full_unstemmed A systems biology approach to the global analysis of transcription factors in colorectal cancer
title_short A systems biology approach to the global analysis of transcription factors in colorectal cancer
title_sort systems biology approach to the global analysis of transcription factors in colorectal cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539921/
https://www.ncbi.nlm.nih.gov/pubmed/22852817
http://dx.doi.org/10.1186/1471-2407-12-331
work_keys_str_mv AT pradhanmeetap asystemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer
AT prasadnagendraka asystemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer
AT palakalmathewj asystemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer
AT pradhanmeetap systemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer
AT prasadnagendraka systemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer
AT palakalmathewj systemsbiologyapproachtotheglobalanalysisoftranscriptionfactorsincolorectalcancer