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Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy....

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Autores principales: Kim, Han Sang, Kim, Sang Cheol, Kim, Sun Jeong, Park, Chan Hee, Jeung, Hei-Cheul, Kim, Yong Bae, Ahn, Joong Bae, Chung, Hyun Cheol, Rha, Sun Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472294/
https://www.ncbi.nlm.nih.gov/pubmed/22846430
http://dx.doi.org/10.1186/1471-2164-13-348
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author Kim, Han Sang
Kim, Sang Cheol
Kim, Sun Jeong
Park, Chan Hee
Jeung, Hei-Cheul
Kim, Yong Bae
Ahn, Joong Bae
Chung, Hyun Cheol
Rha, Sun Young
author_facet Kim, Han Sang
Kim, Sang Cheol
Kim, Sun Jeong
Park, Chan Hee
Jeung, Hei-Cheul
Kim, Yong Bae
Ahn, Joong Bae
Chung, Hyun Cheol
Rha, Sun Young
author_sort Kim, Han Sang
collection PubMed
description BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. RESULTS: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. CONCLUSIONS: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.
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spelling pubmed-34722942012-10-17 Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells Kim, Han Sang Kim, Sang Cheol Kim, Sun Jeong Park, Chan Hee Jeung, Hei-Cheul Kim, Yong Bae Ahn, Joong Bae Chung, Hyun Cheol Rha, Sun Young BMC Genomics Research Article BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. RESULTS: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. CONCLUSIONS: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization. BioMed Central 2012-07-30 /pmc/articles/PMC3472294/ /pubmed/22846430 http://dx.doi.org/10.1186/1471-2164-13-348 Text en Copyright ©2012 Kim 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
Kim, Han Sang
Kim, Sang Cheol
Kim, Sun Jeong
Park, Chan Hee
Jeung, Hei-Cheul
Kim, Yong Bae
Ahn, Joong Bae
Chung, Hyun Cheol
Rha, Sun Young
Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title_full Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title_fullStr Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title_full_unstemmed Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title_short Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
title_sort identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for nci-60 cancer cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472294/
https://www.ncbi.nlm.nih.gov/pubmed/22846430
http://dx.doi.org/10.1186/1471-2164-13-348
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