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Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases

BACKGROUND: Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in eluc...

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Detalles Bibliográficos
Autores principales: Kim, Sangwoo, Lee, Doheon
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2665050/
https://www.ncbi.nlm.nih.gov/pubmed/19344478
http://dx.doi.org/10.1186/1471-2105-10-S3-S2
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author Kim, Sangwoo
Lee, Doheon
author_facet Kim, Sangwoo
Lee, Doheon
author_sort Kim, Sangwoo
collection PubMed
description BACKGROUND: Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately. METHODS: We set four tumor type classes composed of various tumor characteristics such as tissue origin, cellular environment, and metastatic ability. We conducted a set of comparisons among the four tumor classes followed by searching for genes that are consistently up or down regulated through the whole comparisons. RESULTS: We identified four sets of genes that are consistently differently expressed in the comparisons, each of which denotes one of four cellular characteristics respectively – liver tissue, colon tissue, liver viability and metastasis characteristics. We found that our candidate genes for tissue specificity are consistent with the TiGER database. And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features. CONCLUSION: We suggested a new method for identifying metastasis related genes from a large-scale database. The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.
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spelling pubmed-26650502009-04-06 Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases Kim, Sangwoo Lee, Doheon BMC Bioinformatics Proceedings BACKGROUND: Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately. METHODS: We set four tumor type classes composed of various tumor characteristics such as tissue origin, cellular environment, and metastatic ability. We conducted a set of comparisons among the four tumor classes followed by searching for genes that are consistently up or down regulated through the whole comparisons. RESULTS: We identified four sets of genes that are consistently differently expressed in the comparisons, each of which denotes one of four cellular characteristics respectively – liver tissue, colon tissue, liver viability and metastasis characteristics. We found that our candidate genes for tissue specificity are consistent with the TiGER database. And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features. CONCLUSION: We suggested a new method for identifying metastasis related genes from a large-scale database. The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations. BioMed Central 2009-03-19 /pmc/articles/PMC2665050/ /pubmed/19344478 http://dx.doi.org/10.1186/1471-2105-10-S3-S2 Text en Copyright © 2009 Kim and Lee; 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 Proceedings
Kim, Sangwoo
Lee, Doheon
Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title_full Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title_fullStr Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title_full_unstemmed Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title_short Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
title_sort mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2665050/
https://www.ncbi.nlm.nih.gov/pubmed/19344478
http://dx.doi.org/10.1186/1471-2105-10-S3-S2
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