Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors
BACKGROUND: Metastasis is the number one cause of cancer deaths. Expression microarrays have been widely used to study metastasis in various types of cancer. We hypothesize that a meta-analysis of publicly available gene expression datasets in various tumor types can identify a signature of metastas...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3212952/ https://www.ncbi.nlm.nih.gov/pubmed/21736749 http://dx.doi.org/10.1186/1755-8794-4-56 |
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author | Daves, Marla H Hilsenbeck, Susan G Lau, Ching C Man, Tsz-Kwong |
author_facet | Daves, Marla H Hilsenbeck, Susan G Lau, Ching C Man, Tsz-Kwong |
author_sort | Daves, Marla H |
collection | PubMed |
description | BACKGROUND: Metastasis is the number one cause of cancer deaths. Expression microarrays have been widely used to study metastasis in various types of cancer. We hypothesize that a meta-analysis of publicly available gene expression datasets in various tumor types can identify a signature of metastasis that is common to multiple tumor types. This common signature of metastasis may help us to understand the shared steps in the metastatic process and identify useful biomarkers that could predict metastatic risk. METHODS: We identified 18 publicly available gene expression datasets in the Oncomine database comparing distant metastases to primary tumors in various solid tumors which met our eligibility criteria. We performed a meta-analysis using a modified permutation counting method in order to obtain a common gene signature of metastasis. We then validated this signature in independent datasets using gene set expression comparison analysis with the LS-statistic. RESULTS: A common metastatic signature of 79 genes was identified in the metastatic lesions compared with primaries with a False Discovery Proportion of less than 0.1. Interestingly, all the genes in the signature, except one, were significantly down-regulated, suggesting that overcoming metastatic suppression may be a key feature common to all metastatic tumors. Pathway analysis of the significant genes showed that the genes were involved in known metastasis-associated pathways, such as integrin signaling, calcium signaling, and VEGF signaling. To validate the signature, we used an additional six expression datasets that were not used in the discovery study. Our results showed that the signature was significantly enriched in four validation sets with p-values less than 0.05. CONCLUSIONS: We have modified a previously published meta-analysis method and identified a common metastatic signature by comparing primary tumors versus metastases in various tumor types. This approach, as well as the gene signature identified, provides important insights to the common metastatic process and a foundation for future discoveries that could have broad application, such as drug discovery, metastasis prediction, and mechanistic studies. |
format | Online Article Text |
id | pubmed-3212952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32129522011-11-11 Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors Daves, Marla H Hilsenbeck, Susan G Lau, Ching C Man, Tsz-Kwong BMC Med Genomics Research Article BACKGROUND: Metastasis is the number one cause of cancer deaths. Expression microarrays have been widely used to study metastasis in various types of cancer. We hypothesize that a meta-analysis of publicly available gene expression datasets in various tumor types can identify a signature of metastasis that is common to multiple tumor types. This common signature of metastasis may help us to understand the shared steps in the metastatic process and identify useful biomarkers that could predict metastatic risk. METHODS: We identified 18 publicly available gene expression datasets in the Oncomine database comparing distant metastases to primary tumors in various solid tumors which met our eligibility criteria. We performed a meta-analysis using a modified permutation counting method in order to obtain a common gene signature of metastasis. We then validated this signature in independent datasets using gene set expression comparison analysis with the LS-statistic. RESULTS: A common metastatic signature of 79 genes was identified in the metastatic lesions compared with primaries with a False Discovery Proportion of less than 0.1. Interestingly, all the genes in the signature, except one, were significantly down-regulated, suggesting that overcoming metastatic suppression may be a key feature common to all metastatic tumors. Pathway analysis of the significant genes showed that the genes were involved in known metastasis-associated pathways, such as integrin signaling, calcium signaling, and VEGF signaling. To validate the signature, we used an additional six expression datasets that were not used in the discovery study. Our results showed that the signature was significantly enriched in four validation sets with p-values less than 0.05. CONCLUSIONS: We have modified a previously published meta-analysis method and identified a common metastatic signature by comparing primary tumors versus metastases in various tumor types. This approach, as well as the gene signature identified, provides important insights to the common metastatic process and a foundation for future discoveries that could have broad application, such as drug discovery, metastasis prediction, and mechanistic studies. BioMed Central 2011-07-07 /pmc/articles/PMC3212952/ /pubmed/21736749 http://dx.doi.org/10.1186/1755-8794-4-56 Text en Copyright ©2011 Daves 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 Daves, Marla H Hilsenbeck, Susan G Lau, Ching C Man, Tsz-Kwong Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title | Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title_full | Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title_fullStr | Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title_full_unstemmed | Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title_short | Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
title_sort | meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3212952/ https://www.ncbi.nlm.nih.gov/pubmed/21736749 http://dx.doi.org/10.1186/1755-8794-4-56 |
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