Reverse transcription PCR was performed by using 1 ml of cDNA from each of the cell lines, 10 pmol of each forward/ reverse primer, and 0.5 ml of AccuPrime Taq DNA polymerase (Invitrogen, UK), in 20 ml volumes. Thermocycling was performed under the following conditions: Initial denaturation at 94uC for 5 minutes; 30 PCR cycles of 94uC for 1 min, 58uC for 1 min, and 72uC for 1 min, and a final extension of 72uC for 7 minutes. Amplified PCR products (10 ml) were separated on a 2.5% agarose gel containing ethidium bromide and imaged using the GelDoc XR+ Molecular Imager (Bio-Rad). Band intensities were measured using the Quantity One software (Bio-Rad). PCR products were sequenced at the Genetics core facility, University of Sheffield (http://www.shef.ac.uk/medicine/research/ corefacilities/genetics.html). DNA sequences were visualised using the Chromas Lite version 2.01 software, freely downloaded from http://www.technelysium.com.au/chromas_lite.html. Results Hierarchical cluster analysis of protein profiles Analysis of the 4 pooled groups of patients identified 122 proteins with associated information on their relative expression levels (Table S2). The false discovery rate was 1.4% which is within the acceptable limit of 5% [25]. For the iTRAQ dataset, 75 unique proteins were identified and relatively quantified with $2 unique peptides, and these data were used for statistical analyses to determine alterations in protein levels between groups. Hierarchi- cal clustering was performed to group the data based on the degree of similarity between the BPH and cancer groups. Agglomerative clustering using the squared Euclidean distance between log10 value of iTRAQ ratios and smallest intercluster dissimilarity linkage procedure was performed (Mathematica 7.0.0 for Mac), to generate the dendrogram shown in Figure 1. A key feature of the dendrogram is separation of the patient groups according to the stage of their disease. Patients with metastasis separated the furthest and are thus considered to be the most different from patients with BPH. The patients with BPH clustered more closely with patients in the non-progressing group compared with the progressing and metastatic groups. Gene ontology annotation To assess the range of proteins identified, gene ontology (GO) annotations for biological processes were assigned using the Protein ANalysis THrough Evolutionary Relationships (PAN- THER) software, which links protein accession codes to the Figure 1. Hierarchical cluster analysis of the 4 patient groups studied. Samples were clustered based on the similarity of their protein expression profiles observed in log10 of the iTRAQ ratios and a dendrogram generated to indicate the relationship between the samples. Squared Euclidean distance between clusters (single linkage) is shown. Varying lengths of the branch points indicate the degree of similarity; the shorter the branch the higher the degree of similarity. doi:10.1371/journal.pone.0030885.g001 Serum Biomarkers for Prostate Cancer Metastasis corresponding entries in the gene ontology database [14,27]. The PANTHER analysis revealed the presence of many common plasma proteins such as those associated with complement mediated immunity (14%), immunity and defence (27%), prote- olysis (21%), blood clotting (7%), and protein metabolism and modification (22%). For the biological network analysis, the Metacore platform (GeneGo, Inc., St. Joseph, MI), was employed as previously described [14], which revealed that many of the differentially expressed proteins such as C4, C4a, C5, C5b, C9 and C6 mapped to the classical immune response pathway (Figure S1). Diagnostic biomarker leads Differences in protein levels are reported following a t-test analysis as previously described [23]. The p-values were calculated based on the number of peptides used for the quantification and the variance of the iTRAQ reporter ratios derived from these peptides. A p- value#0.01 was used to assign significant changes in protein levels between sample sets. Importantly, the protein changes reported as significant differential expression were selected based upon statistical significance rather than fold change [28]. Some of these differences are expected to be potentially larger due to the known under estimation associated with iTRAQ based quantification [24]. During our analysis we were interested in proteins showing both increased and decreased expression levels, as previous studies have reported that both significantly up- and down-regulated proteins may be of clinical relevance [15,29]. The identification of proteins differentially expressed in non- progressing, progressing and metastatic patient groups relative to the BPH group were of interest as these could provide leads for potentially useful diagnostic and prognostic biomarkers (Table S3). Thus, a comparison between the non-progressing cancer group versus the BPH group showed a significant differential expression of 22 proteins; 7 of which were up-regulated and 15 down- regulated (Table S3a). Similarly, a comparison between the progressing patient group versus the BPH group identified the differential expression of 19 proteins; 11 of which showed significant over-expression and 8 showed down-regulation (Table S3b). Comparisons of the metastatic patient group versus the BPH group identified the differential expression of 35 proteins, with 19 proteins showing significant over-expression and 16 showing significant down-regulation (Table S3c). Additionally, C-reactive protein (CRP) was found to be elevated 41.1 fold in the serum of patients with metastatic disease compared to patients with BPH (Table S2). Prognostic biomarker leads Once a diagnosis of cancer has been made the next steps are to establish the extent (stage) of disease, in an attempt to predict those patients in which the disease is likely to progress from the patients in which the disease is likely to remain localized, and to obtain prognostic information. Currently, pre-treatment PSA levels, biopsy Gleason grade and clinical staging are used to provide prognostic information; however, these parameters are associated with a number of limitations. Thus, a comparison of patients with progressing versus non-progressing disease identified the signifi- cant differential expression of 25 proteins; 13 up-regulated and 12 down-regulated (Table S4). Differential protein levels associated with disease progression In addition to the comparisons above, protein differences were mapped according to the stage of prostate cancer development PLoS ONE | www.plosone.org 4 February 2012 | Volume 7 | Issue 2 | e30885