new molecular approaches for early screening and diagnosis are urgently needed. Gene expression analysis was recently used to increase our knowledge about the biology of CaP [2]. Gene signatures at RNA level are determined and often used as predictors to model clinically relevant information (e.g. prognosis, survival time, sensitivity to drugs, etc.). To this aim, final conclusions on the classification power of the gene signature studied are entirely drawn on the basis of the molecular data obtained at transcrip- tional level by using methods such as DNA microarray or RT q- PCR. By Northern blot analysis, we have previously identified 8 informative genes whose expression changes on the basis of the presence of CaP malignancy in humans [3]. In a 5-year follow-up study, we also showed that the levels of expression of this panel of genes strongly correlate with differentiation and final outcome (prognosis) of CaP patients. This result was obtained by combining molecular data with standard clinical information [4]. But in our mind the real challenge was to detect CaP by means of molecular tools alone. Although the study of specifically altered gene expression during CaP tumourigenesis is a difficult task, the scientific information that will ultimately be obtained is an important reward, leading to a better understanding of the molecular basis of the disease which may lead to better methods for diagnosis and therapy. This is particularly important when considering the heterogeneity of CaP and the variability of clinical progression, with some patients presenting with slow growing indolent tumours and other patients having a disease that is rapidly progressive. The signature that we have identified is made of 4 metabolically related genes, ODC, OAZ, AdoMetDC, SSAT, the regulatory genes of the polyamine metabolism, 2 genes related to cell cycle progression, H3 and GAS1, specific markers of S- and G0-phases, respectively [5,6], GAPDH, an enzyme of the glycolitic pathway, and CLU an enigmatic protein whose biological role is still a matter of debate. CLU, also known as SGP-2, TRPM-2 or ApoJ, is highly over- expressed during prostate gland involution and remodelling [7,8]. Although there is a general consensus about the involvement of CLU in regulating cell death, its specific role in the apoptotic process is still controversial, as well as in cell transformation. More specifically, its level of expression and regulation during CaP onset and progression is debated [9,10]. A better understanding of this issue is of particular importance not only because of the above considerations, but also because a clinical trial aimed at silencing CLU gene by antisense oligonucleotides in CaP patients is currently ongoing [11]. As a matter of fact, we and others have found that CLU is down-regulated during CaP progression [12–15], suggesting that it might act as a tumour-suppressor factor [16,17]. Concerning the gene signature described here, all of the genes studied have previously shown strictly related shifts in co- expression during CaP progression [3]. Specific alteration of transcription of the regulatory enzymes of polyamine metabolism during CaP progression has also been validated by meta-analysis of microarrays [18]. In the TRAMP mice model of CaP progression, our gene signature alone as determined by RT- qPCR, besides discriminating CaP from benign tissue, also predicted individual response to treatment with Green Tea Catechins (GTCs) [19]. Other individual marker genes have been found of proven validity in this field, such as a-methylacyl coenzyme A racemase (AMACR) [20,21] or Prostate Cancer Antigen 3 (DD3/PCA3) [22], but at the moment no relationship between the expression level of PCA3 and tumour grade or staging was found yet. Our gene signature is made of informative genes some of which are well known to play important roles in the Gene Signature physiology of prostate cells, such as the genes coding for regulatory proteins of polyamine metabolism [23] and CLU [7]. The aim of our study was to show that our gene signature alone enhances the sensitivity and the specificity of molecular diagnosis of CaP when compared to single marker identification. We used histopathologic classification of fixed tissues specimens as final reference, as usually done in similar circumstances [24,25]. The 8- gene model was detected by RT-qPCR in frozen tissue specimens obtained at Radical Prostatectomy (RP). Classification of tissue specimens (i.e. presence or absence of tumour) was performed without other pathological or clinical data. Furthermore, molecular data were used for sub-classification of tissue specimens with regard to Gleason score, age and total serum PSA of the patient at RP. For specimen classification we used the Nearest Neighbour (NN) classifier, a statistical multi-factorial analysis tool known to perform well specifically for cancer classification when compared with other methods. For validation of classification performance, we used the 10-fold cross validation procedure, repeated 100 times with different sub-samplings in order to estimate the mean performance of the signature and the confidence interval (results are expressed as mean695% confidence interval, approximately corresponding to 2 standard deviations). Results The tissue specimens bio-bank The bio-bank consisted of a collection of 66 human specimens, see Table 1, matching our eligibility criteria for the study: i) pathological evidence of presence or absence of CaP in the frozen pre- and post-RNA sections; ii) benign specimens free of prostate intraepithelial neoplasia (PIN) lesions or tumour invasion, taken very far away (i.e. in different areas of the prostate, ideally in the controlateral sextant) from the neoplastic lesion; iii) CaP specimens having a cancer cell content covering at least 5% of the whole section area; iv) good yield and high quality of RNA preparation. Among these, 44 specimens were CaP-benign paired specimens obtained from the same patient. Thirthy-five specimens were classified as benign by the pathologist, while 31 were CaP. How to get molecular and morphological data from the same tissue specimen: the ‘‘sandwich’’ method To obtain a direct comparison between molecular data and pathological classification we developed a ‘‘sandwich’’ method (see Figure 1 and Methods: Prostate Tissue Specimens Collection and Handling). RNA extraction yielded an average of 50–60 mg of total RNA from 20 mg of human prostate tissue. The amount of total RNA obtained was high enough to directly check the quality of the preparation by spectrophotometry, followed by convention- al electrophoresis. Only good quality RNA was used for RT- qPCR analysis. Eight informative genes plus 2 housekeepers were analysed starting from the same RNA sample. RT q-PCR and data analysis Relative quantification of the target genes was performed with the well known REST software tool [26]. Changes in the level of expression of 7 out of 8 informative genes in CaP Versus benign tissue did not reach statistical significance (not shown), while CLU was significantly down-regulated in CaP (p,0.05). In Figure 2, the relative gene expression of the signature obtained by the 2‘2DDCT method is reported as a function of RP final Gleason score. Interestingly, CLU is significantly and reversely related (p,0.01) to the Gleason score of the tumour: i.e. lower expression levels of CLU were found in higher Gleason score specimens. In Table 2 are shown the final results of classification using the Nearest Neighbour (NN) PLoS ONE | www.plosone.org 2 October 2008 | Volume 3 | Issue 10 | e3617