Cargando…
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....
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1782246574833795072 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3472294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kimhansang identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT kimsangcheol identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT kimsunjeong identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT parkchanhee identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT jeungheicheul identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT kimyongbae identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT ahnjoongbae identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT chunghyuncheol identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells AT rhasunyoung identificationofaradiosensitivitysignatureusingintegrativemetaanalysisofpublishedmicroarraydatafornci60cancercells |