# Identification of Novel Markers of Prostate Cancer Progression, Potentially Modulated by Vitamin D
## 2 Materials and Methods
### Data Processing  
To process and identify significant differentially expressed genes (SDEG) in data sets, we used the MultiExperiment Viewer (MeV) software (The Institute for Genomic Research (TIGR), J. Craig Venter Institute, Rockville, MD, USA). In cases where multiple genes probes have insisted on the same GeneID NCBI, we have used those with the highest variance. The significance threshold level for all data sets was \(p<0.05\). The genes with \(p<0.05\) were selected for further analysis. For all datasets we performed a statistical analysis with GEO2R, applying a Benjamini & Hochberg (False discovery rate) to adjust \(p\) values for multiple comparisons [27; 28; 29]. From GSE70770, we identified, respectively, 2279 and 9785 genes that were significantly up-regulated and down-regulated, comparing the prostate biopsies samples of healthy vs. tumor patients. Regarding GSE62872, we obtained a total of 2312 and 1060, respectively, of significantly up-regulated and down-regulated genes, comparing the prostate biopsies samples of healthy vs. tumor patients. The analysis of GSE6919 produced 1747 and 1060 genes, respectively, that were significantly up-regulated and down-regulated in prostate biopsies samples of healthy vs. tumor patients. Furthermore, the analysis of GSE64657 produced 995 up-regulated and 2196 down-regulated and significantly expressed genes, comparing the LNCaP cells line treated with vehicle (ethanol) and 1,25D\({}_{3}\) (100 nM) for 24 h. All data analysis are available in Table S1. In order to identify genes commonly modulated between the four GSEs, we performed a Venn diagram analysis, using the web-based utility Venn Diagram Generator ([[http://bioinformatics.psb.ugent.be/webtools/Venn/](http://bioinformatics.psb.ugent.be/webtools/Venn/)]([http://bioinformatics.psb.ugent.be/webtools/Venn/](http://bioinformatics.psb.ugent.be/webtools/Venn/))). To analyze a \(2\times 2\) contingency table, a chi-square with Yates correction was performed by GraphPad Prism software ([[http://graphpad.com/quickcals/contingency1.cfm](http://graphpad.com/quickcals/contingency1.cfm)]([http://graphpad.com/quickcals/contingency1.cfm](http://graphpad.com/quickcals/contingency1.cfm))). The association between rows (groups) and columns (outcomes) with \(p<0.01\) was considered to be statistically significant. The genes Ontology analysis was performed using the web utility GeneMANIA ([[http://genemania.org/](http://genemania.org/)]([http://genemania.org/](http://genemania.org/)), [[http://genemania.org/](http://genemania.org/)]([http://genemania.org/](http://genemania.org/))) [30] and the GATHER (Gene Annotation Tool to Help Explain Relationships) ([[http://changlab.uth.tmc.edu/gather/](http://changlab.uth.tmc.edu/gather/)]([http://changlab.uth.tmc.edu/gather/](http://changlab.uth.tmc.edu/gather/))) [31]. The GeneMANIA was also used for built the weighted gene networks commonly modulated. GeneMANIA searches publicly-available genomics and proteomics data, including data from gene and protein expression profiling studies and primary and curated molecular interaction networks and pathways, to find related genes. The network weighting method is 'Gene-Ontology (GO)-based weighting, Biological Process-based'. This weighting method assumes the input gene list is related in terms of biological processes (as defined by GO). GHATER (Gene Annotation Tool to Help Explain Relationships) ([[http://changlab.uth.tmc.edu/](http://changlab.uth.tmc.edu/)]([http://changlab.uth.tmc.edu/](http://changlab.uth.tmc.edu/)) gather/) is an online tool that explains the function of a group of genes, such as a cluster of co-regulated genes from microarrays. The tool is a comprehensive system that combines gene function, ontology, pathways and statistical tools that enable researchers to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The analysis of gene ontology is based on the Bayes factor. This is a measure of the strength of the evidence supporting an association of an annotation with your gene list.