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연세대학교 의과대학
BMC Genomics, Jul 30 2012, 13 | https://doi.org/10.1186/1471-2164-13-348
Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells
Han Sang Kim 1,2,#, Sang Cheol Kim 3,#, Sun Jeong Kim 1, Chan Hee Park 1, Hei-Cheul Jeung 1,2, Yong Bae Kim 4, Joong Bae Ahn 1,2, Hyun Cheol Chung 1,2,5, Sun Young Rha 1,2,5,*
1Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Korea
2Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
3Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea
4Department of Radiation Oncology, Yonsei Cancer Center, Seoul, Korea
5Brain Korea 21 Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea
2Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
3Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea
4Department of Radiation Oncology, Yonsei Cancer Center, Seoul, Korea
5Brain Korea 21 Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea
#These authors contributed equally
*Corresponding author: correspondence to Sun Young Rha
Abstract
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.
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.
논문정보
- Research article
- 2012년 07월 (BRIC 등록일 2026-04-27)
- 국내 연구진
- 의약학 > 종양의학
- 120회 이상 인용된 논문
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