Abstract
BackgroundIn 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.ResultsRadiosensitivity 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.ConclusionsIntegration 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.
Highlights
In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy
Four published microarray experiments were reanalyzed to identify genes whose expression correlated with radiosensitivity in NCI60 cancer cell lines
The scatter plots showing relationships between survival fraction at 2 Gy radiation (SF2) and gene expression of the 31 radiosensitivity signature genes in the four microarrays are in Additional files 2, 3, 4, and 5
Summary
A prediction of response to treatment could lead to better dose selection for patients in radiotherapy. Predicting tumor response to radiotherapy is one of the major issues in cancer treatment. The cancer cell line panel of the National Cancer Institute (NCI) has been widely used for drug screening based on relevant gene expression [8]. Promising, these studies are confined to a single platform microarray and further validation and a larger dataset are needed. Individually identifying every gene with a statistically significant response is not sufficient as a biological explanation For this reason, gene set analysis is necessary, along with defining the biological processes or pathways in expression analysis
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