1. Correlation between mRNA expression and SF2 was calculated using a quantitative method calculating the linear regression coefficient between gene expres sion and radiosensitivity from the linear regression model, and genes were identified under a false dis covery rate of 10% for all four selleckbio microarray platforms. We performed SAM for each microarray platform. A common radiosensitive gene signature were defined as genes commonly identified in all four microarray platforms. A Venn diagram was plotted using Venny. Principal components analysis was performed for data reduction, simplifying datasets to three dimen sions for plotting purposes. Principal component ana lysis was conducted using R statistical software, using princomp function and default options.

Gene set enrichment analysis using a global test To find a pathway of genes correlated with SF2, gene set analysis was performed using a global test with a defined gene set from the Kyoto encyclopedia of genes and genomes pathways. This test was based on the generalized linear model and tested the null hypoth esis in which all regression coefficients between SF2 and gene expression were zero. This was a score test based on random effect modeling of parameters corresponding to the coefficients of the individual genes in a pathway. It was used to determine whether the global expression pattern of a gene set was significantly related to SF2. If the global test was significant, the genes in the gene set were more associated with SF2 than expected under a null hypothesis. These associa tions could involve both upregulation and downregulation.

Typically, a significant gene set is a combination of positively and negatively related genes. P value was corrected for multiple comparisons using the Benjamini and Hochberg method. An adjusted p value under 0. 01 was considered as signifi cant. Analysis was done using R statistical software. We used function gt in the R package globaltest. Canonical pathway analysis, gene plot and genetic network representation In addition to gene set analysis, canonical pathway ana lysis was performed using 179 genes identified in more than three microarray platforms using SAM analysis. Canonical pathway analysis identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the 179 genes.

In this test, the p value was measured to decide the likeli hood that the association between 179 genes and a given pathway was due to random chance. The smaller the p value, the lower the likelihood of Batimastat random association and the more significant the association. The significance of association between the 179 genes and the canonical pathways was measured in two ways 1 the ratio of the number of molecules from the data set that mapped to the pathway divided by the total number of molecules that mapped to the canonical pathway.