Cargando…
Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages
BACKGROUND: The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. METHODS: Using c...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719936/ https://www.ncbi.nlm.nih.gov/pubmed/29212455 http://dx.doi.org/10.1186/s12885-017-3832-1 |
_version_ | 1783284583406501888 |
---|---|
author | Wang, Daifeng Haley, John D. Thompson, Patricia |
author_facet | Wang, Daifeng Haley, John D. Thompson, Patricia |
author_sort | Wang, Daifeng |
collection | PubMed |
description | BACKGROUND: The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. METHODS: Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. RESULTS: We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or ‘periods’ during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where immune-associated genes were up-regulated after middle EMT stages. The presence of EMT-dynamic gene expression patterns supports the presence of differential activation and repression timings at the transcriptional level for various pathways and functions during EMT that are not detected in pure E or M cells. Importantly, the cell line identified EMT-dynamic genes were found to be present in lung cancer patient tissues and associated with patient outcomes. CONCLUSIONS: Our study suggests that in vitro identified EMT-dynamic genes capture elements of gene EMT expression dynamics at the patient level. Measurement of EMT dynamic genes, as opposed to E or M only, is potentially useful in future efforts aimed at classifying patient’s responses to treatments based on the EMT dynamics in the tissue. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-017-3832-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5719936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57199362017-12-11 Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages Wang, Daifeng Haley, John D. Thompson, Patricia BMC Cancer Research Article BACKGROUND: The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. METHODS: Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. RESULTS: We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or ‘periods’ during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where immune-associated genes were up-regulated after middle EMT stages. The presence of EMT-dynamic gene expression patterns supports the presence of differential activation and repression timings at the transcriptional level for various pathways and functions during EMT that are not detected in pure E or M cells. Importantly, the cell line identified EMT-dynamic genes were found to be present in lung cancer patient tissues and associated with patient outcomes. CONCLUSIONS: Our study suggests that in vitro identified EMT-dynamic genes capture elements of gene EMT expression dynamics at the patient level. Measurement of EMT dynamic genes, as opposed to E or M only, is potentially useful in future efforts aimed at classifying patient’s responses to treatments based on the EMT dynamics in the tissue. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-017-3832-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-06 /pmc/articles/PMC5719936/ /pubmed/29212455 http://dx.doi.org/10.1186/s12885-017-3832-1 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 Wang, Daifeng Haley, John D. Thompson, Patricia Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title | Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title_full | Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title_fullStr | Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title_full_unstemmed | Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title_short | Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
title_sort | comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719936/ https://www.ncbi.nlm.nih.gov/pubmed/29212455 http://dx.doi.org/10.1186/s12885-017-3832-1 |
work_keys_str_mv | AT wangdaifeng comparativegenecoexpressionnetworkanalysisofepithelialtomesenchymaltransitionrevealslungcancerprogressionstages AT haleyjohnd comparativegenecoexpressionnetworkanalysisofepithelialtomesenchymaltransitionrevealslungcancerprogressionstages AT thompsonpatricia comparativegenecoexpressionnetworkanalysisofepithelialtomesenchymaltransitionrevealslungcancerprogressionstages |