Among the 31 signature genes, several selleck Ganetespib were enriched, including ACTN1, CAPNS1, ITGB5, RALB, which were downre gulated, and WAS, which was upregulated in radiosen sitive cell lines. Genetic network interaction showed that adhesion related molecules in Table 3 were involved in the integrin signaling pathway, and that interaction existed with other signaling pathways such as the PI3K, Wnt, and MAPK signaling pathways, which were enriched, as shown in Table 2B. Discussion The discovery of potential biomarkers and the elucida tion of the mechanisms of radiosensitivity are import ant to developing radiosensitizers as well for predicting kinase, p53, and Wnt signaling pathways.

Adhesion related molecules as major components in a radiosensitivty signature To integrate the top down and bottom up approach, 31 radiosensitivity signature genes found through SAM analysis were compared with the gene sets found in the global test. Eight genes were functionally annotated in the global test, and their major function was examined according to KEGG pathways. The common function was related to cell junctions and adhesion, sug gesting that adhesion related molecules might have a major role in the mechanism of radiosensitivity. tumor response in radiation oncology. We rea nalyzed four published microarray studies to identify a common radiosensitivity signature regardless of plat form. This strengthened the reliability of our analysis. Using SAM, we examined each gene individually to show that the correlation with SF2 was significant.

Next, we performed a gene set analysis using a global test based on a linear regression model with a well defined gene set from KEGG pathways. A combination of both analyses found that adhesion related molecules and several cancer related molecular pathways were significantly enriched for radiosensitivity and these molecules were linked via the integrin signaling path way. Using both a top down and bottom up approach increases the ability to determine genes and signaling pathways that are biologically explainable and statisti cally acceptable. Several studies have reported possible radiosensitivity predictive genes. However, no gene is com mon among the previous reports. Therefore, we used four microarrays to find genes commonly identified as significant in radiosensitivity. We identified 31 common genes as well as 179 genes that were selected in more than three studies. Of these 179 genes, 8 were previously reported. Comparing the 179 genes with previ ous reports, the cell cycle genes CCNA2 and CDK6 in esophageal cancer, and the ras related gene RAC2 in rectal cancer were common. Other genes that were reported previously Batimastat could also be possible drug tar gets.