Noerholm et al. BMC Cancer 2012, 12:22 http://www.biomedcentral.com/1471-2407/12/22 sample set in the current study is too small to pass for- mal power calculation criteria [19]. With the observation of differences between GBM and control RNA expression patterns, we sought to identify the genes that best separated the two groups by con- ducting gene-by-gene t-tests between the groups on all genes in the full 44 K data set and corrected the result- ing p-values for False Discovery Rate (FDR) by applying the Benjamini and Hochberg algorithm [20]. The 25 most down- and up-regulated genes are listed in Table 1 and more extensive lists of the 200 most down- and up-regulated genes are available in Additional file 2: Table S2 and Table S3, respectively. From the top den- drogram in Figure 1A, which was prepared without the use of p-values, it would appear that the GBM samples separate into two distinct sub-groups. However, when we performed a gene-by-gene t-test between these two apparent groups of GBM samples no significant genes were left that met the p < 0.05 criteria for statistical sig- nificance after application of Benjamini and Hochberg correction for FDR. Similarly, when the genes in Addi- tional file 2: Table S2 and Table S3 were used together for cluster analysis, there was a clear separation of GBMs from controls as seen in Figure 2, but the two distinct groups of GBM samples were no longer appar- ent. From Figure 3, which shows a volcano plot of the FDR corrected p-values against the level of differential expression for each gene, it is evident that substantially more genes were found to be significantly down-regu- lated (121 genes) than up-regulated (24 genes) in the GBM samples compared to controls. Gene ontology analysis Normalization of array data can be done in a number of different ways, each having its own advantages and dis- advantages. To reduce the risk that the normalization procedure introduced unintended biases or artifacts, we normalized the data in three different ways, as described in Methods. For gene ontology analysis we used the top two hundred genes that passed the criteria p < 0.05 after FDR-correction in all three normalization proce- dures mentioned above (Additional file 2: Table S2 and Table S3). All genes listed in the tables of this paper met the same selection criteria. Down-regulated genes in serum microvesicles from GBM patients We analyzed the up- and down-regulated genes inde- pendently to try to identify brain- or GBM-specific genes up-regulated in the GBM patients due to the microvesicle shedding activity of the tumour, as well as genes down-regulated in normal cells, such as platelets, lymphocytes and endothelial cells [21] that also shed microvesicles into the blood. Gene ontology analysis of Page 4 of 11 the 200 most down-regulated genes (from -4.21 to -1.92-fold, Additional file 2: Table S2) using the online tool, DAVID [22] revealed that an overwhelming major- ity of the resulting GO terms are related to ribosome functions, e.g. “Ribosome”, “Translational Elongation” and “RNA Binding”. The most significant cluster had an enrichment score of 54.82 and contained 115 of the 200 input genes, most of which were mRNAs coding for ribosomal proteins in both the large and small subunits of the ribosome (e.g. RPL11, RPS29, RPLP1, RPS27A, etc.). These genes have previously been shown to be very highly expressed in lymphocytes relative to other blood cells [23,24]. Evaluation of the percentage of lym- phocytes in the white blood cell (WBC) counts in the GBM patients used in this study, showed that 7 out of the 9 patients used for array analysis and 9 out of the 10 patients used for qPCR validation had values of 3- 19% lymphocytes, which is well below the reference interval for normal healthy individuals of 22-28% [25] and is consistent with immunosuppression observed in most GBM patients [26]. Unfortunately, the de-identi- fied normal control samples used for this study were obtained through a blood bank so no detailed informa- tion is available on their blood cell counts. Up-regulated genes in microvesicles from GBM patients When we left out the down-regulated genes and attempted unsupervised clustering using only genes that were up-regulated in microvesicles from GBM patients (Table 1/Additional file 2: Table S3), no clear separation of the GBMs from the normal controls was evident. Further, gene ontology analysis of the up-regulated genes in Table 1/Additional file 2: Table S3 resulted in clusters and GO terms with enrichment scores substan- tially lower (< 3) than for the down-regulated genes in Table 1/Additional file 2: Table S2 (> 50). Validation analysis by qRT-PCR We moved on to see if some of the dysregulated genes from the microarray could be validated as dysregulated by qRT-PCR in an independent set of 10 patients and 10 controls. We selected individual up-regulated genes from the lists in Table 1/Additional file 2: Table S3 in search of markers associated with GBM tumours. We sought to identify mRNAs up-regulated in the microve- sicles in peripheral blood from GBM patients, which were expressed at low to undetectable levels in blood cells, and elevated in either GBM or normal brain cells. We reasoned that since the majority of microvesicles in blood are believed to arise from platelets [27] and other blood cells, the RNA expression of these cells could serve as an approximation of the normal blood microve- sicle RNA profile. We compared public datasets in Gene Expression Omnibus (GEO) for GBM (GSE15824) to