Cargando…
Thirty biologically interpretable clusters of transcription factors distinguish cancer type
BACKGROUND: Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of gene...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180590/ https://www.ncbi.nlm.nih.gov/pubmed/30305013 http://dx.doi.org/10.1186/s12864-018-5093-z |
_version_ | 1783362235490369536 |
---|---|
author | Abrams, Zachary B. Zucker, Mark Wang, Min Asiaee Taheri, Amir Abruzzo, Lynne V. Coombes, Kevin R. |
author_facet | Abrams, Zachary B. Zucker, Mark Wang, Min Asiaee Taheri, Amir Abruzzo, Lynne V. Coombes, Kevin R. |
author_sort | Abrams, Zachary B. |
collection | PubMed |
description | BACKGROUND: Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types. RESULTS: We recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes. CONCLUSIONS: Using our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5093-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6180590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61805902018-10-18 Thirty biologically interpretable clusters of transcription factors distinguish cancer type Abrams, Zachary B. Zucker, Mark Wang, Min Asiaee Taheri, Amir Abruzzo, Lynne V. Coombes, Kevin R. BMC Genomics Research Article BACKGROUND: Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types. RESULTS: We recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes. CONCLUSIONS: Using our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5093-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-11 /pmc/articles/PMC6180590/ /pubmed/30305013 http://dx.doi.org/10.1186/s12864-018-5093-z Text en © The Author(s) 2018 Open Access This 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 Abrams, Zachary B. Zucker, Mark Wang, Min Asiaee Taheri, Amir Abruzzo, Lynne V. Coombes, Kevin R. Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title | Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title_full | Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title_fullStr | Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title_full_unstemmed | Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title_short | Thirty biologically interpretable clusters of transcription factors distinguish cancer type |
title_sort | thirty biologically interpretable clusters of transcription factors distinguish cancer type |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180590/ https://www.ncbi.nlm.nih.gov/pubmed/30305013 http://dx.doi.org/10.1186/s12864-018-5093-z |
work_keys_str_mv | AT abramszacharyb thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype AT zuckermark thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype AT wangmin thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype AT asiaeetaheriamir thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype AT abruzzolynnev thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype AT coombeskevinr thirtybiologicallyinterpretableclustersoftranscriptionfactorsdistinguishcancertype |