Screening,Identification And Function Analysis Of Prostate Cancer Detection Marker

Aims:Prostate cancer (PCa) is one of the main forms of cancer affecting oldmen worldwide. In the USA, in 2006, PCa had the highest cancer incidencewith 234,460 new cases and the second-highest cancer-related mortality rate,with 27000 deaths. The retrospective research of Gu et al. showed that PCaincidence in Beijing, in the 1990s was five times higher than that in the1950s. We retrospectively investigated all PCa cases from eight hospitals inChangchun, China from 1986 to 2001, and found the number of cases hadincreased 4.6-fold in the 1999–2001 period vs. the 1986–1989 period. Wealso organized a mass screening in Changchun for PCa in men aged 50 yearsor older with routine serum prostate-specific antigen (PSA) tests. Thedetection rate of PSA was greater than 1.7 %, and 18.8 % of the casesinvolved osseous metastasis.Currently, the serum PSA test for mass screening for PCa appears to becontroversial. Recently, Stamey et al.announced that the serum PSA test asthe standard detection test for PCa was out of date in the USA, based on 20years of experience; in our experience, the biomarker still plays an importantrole in the mass screening of PCa in China. However, with the rapidemergence of proteomics, it is appealing to many investigators to explorenew and more effective specific proteins to accurately detecting PCa. Theuses of surface-enhanced laser desorption/ionization time-of-flight massspectrometry (SELDI-TOF MS), for example, requires less labor, and has ahigh throughput and excellent reproducibility. Initial researches on the identification of biomarkers for cancers using this technique have been verypromising, which has encouraged us to verify the availability of MS. Thistechnique has been successfully applied for exploring new protein markersfor early detection of PCa in the USA. However, the proteomic approach hasnot yet been used to identify the protein markers of PCa in a cohort ofChinese men.In this study, we analyzed serum samples using SELDI-TOF MS toexplore the marker proteins for the detection of PCa for the first time inChina.Methods:All the serum samples were randomly taken from a serum bank in the ResearchCenter of Prostate Diseases at Jilin University (Changchun, China) and were kept at–70oC. Immobilized metal affinity capture array (IMAC)-Cu metal binding chips(Ciphergen Biosystems, Fremont, CA, USA) were used and put into abiopROCessor (Ciphergen Biosystems, Fremont, CA, USA), a device thatholds eight chips and allows for the application of large volume of serum toeach chip array. The chips were then placed in the PBS-II SELDI-TOF MS(Ciphergen Biosystems, Fremont, CA, USA) operated in the positive ionmode. Time-of-flight spectra were generated by averaging 90 laser shotscollected on each spot with a laser intensity setting of 195, detectorsensitivity setting of 9, and a lag time focusing of 900 ns. The spectra werecalibrated using the All-in-1 protein molecular mass standard (CiphergenBiosystems, Fremont, CA, USA). To compensate for slight spot-tospotvariations, if any, the spectra were also normalized using the total ion currentmethod in the mass to charge (m/z) range of 1 500–30 000 (IMAC-Cusurface) with subtracted baseline. The reproducibility of the SELDI-TOFsystem was determined using the pooled normal serum quality controlsample. The QC spectra were very reproducible with intra- and inter-assayCVs for peak location of 0.05%, and CVs of 15% and 20%, respectively, for peak intensity.ResultsThe SELDI-TOF technology is particularly effective in resolving low molecularweight (< 10 kDa). The SELDI-TOF Biomarker Wizard program detected 308 peaksper spectrum for each corresponding sample. Eighteen serum differential proteins wereclearly identified in the PCa group compared with the control group (P < 0.01).However, the use of each single peak from the 18 proteins could not completelydifferentiate the PCa group from the control group. The 18 proteins from the PCa groupincluded 14 proteins in low abundance and 4 proteins in high abundance. theclassification tree that was reflected through eight masses (M1 669, M4 300, M5 923,M6 250, M6 652, M7 782, M15 868 and M145 875) calculated by the BiomarkerPattern program generated nine terminal nodes. The diagnosis sensitivity and specifityfor PCa were 92.0 % and 96.7 %, respectively.In the early stage of prostate cancer samples, SELDI TOF also has accuracydiagnosis on cancer. The 85 peaks in the 2–40 kDa range were used to construct thedecision tree calculated by the Biomarker Pattern software. The classification algorithmused six masses between 2 and 40 kDa (15868, 3978, 8959, 11531, 6324, and 4670) togenerate 7 terminal nodes. Mass 15868 and mass 11531 were expressed highlyredundant in PCA group; others were expressed highly redundant in CONT group.Based on the stochastic nature of reality, misclassification of a new sample cannot beruled out even for a pure node that contains only one sample type. Then we evaluatedthe expected probability of each terminal node. The most exciting result is that allterminal nodes showed highest probability of both samples. The classification algorithmcorrectly predicted 100% of the samples for either of the two groups in the training set.The algorithm correctly predicted 93.3 % (28 of 30) samples from prostate cancer, 90%(27 of 30) samples of control being correctly classified.To explore the use of Protein-Expression Profiling to Identify PrognosticSubclasses in Prostate Cancer is important. The classification algorithm used six massesbetween 2 and 40 kDa (8141, 11241, 2041, 7179, 7481, and 8944) to generate 7terminal nodes. Mass 8944 were richer in advanced stage group than early group; otherswere expressed highly redundant in early stage group. With these markers, thealgorithm correctly predicted 93.3 % (14 of 15) samples from EPC, 100% (15 of 15) samples of APC being correctly classified. The PPV and NPV for the decision tree were93.8% and 100%, superior to the decision tree algorithm and PSA. It can clearlydistinguish the different prognosis group of prostate cancer.One of the discriminating proteins (15868) that were consistently present in higheramounts in cancerous tissues was identified. We subsequently showed that theidentified marker would be useful to the early detection of prostate cancer. 15868 issignificantly differentiated between psa less than 4 and normal control. The first tenN-terminal amino acids were sequenced by EDMAN. The 10 amino acid was blastedusing the NCBI Protein-Protein BLAST. The result from BLAST showed the sequenceof the peptide matched with the fragment of Leucine Zipper Transcription Regulator2(LZTR2, reeducacion gene promotor region related protein, RGPR-p117, NP₁49118).It matched the middle of the protein. Moderately strong immunohistochemicalstaining for LZTR2 was noted in the cytoplasm of an endometrioid adenocarcinoma, ascompared to adjacent stroma and benign endometrial gland. In PSA staining negativecases of PCa patients whose PSA level were lower than 4.0ng/mL were moderatelystrong immunohistochemical staining of LZTR2.We previously found that monoclonal antibody (mAb) RM2 reacted to prostatecancer tissue reflecting the malignant potential, i.e., the Gleason grading systemwhereas RM2 reactivity to benign glands was weak to negative. In the current studywe further explored the biological significance of RM2 reactivity through thephenotypic changes of prostate cancer cells.RM2 reactivity and expression of gpx-chain was reduced concomitantly withdecreased proliferation in prostate cancer cells treated by the epigenetic drugs. Amongthe various molecules involved in the anoikis of prostate cancer cells induced by theepigenetic drugs, expression of FAK, Rho A, ERK-2 and galectin-3 was reduced. Wefound that galectin-3 bound prostate cancer cells in an anti-apoptotic manner and thisbinding was significantly inhibited by mAb RM2, indicating that galectin-3 bind to theglycosyl epitope (GalNAcDSLc4) RM2 recognizes. We found the inhibitory effect onexpression of gpx- chain and galectin-3 by 5-Aza and PBA, i.e., expression of thesemolecules were associated with anti-anoikis pathway. Moreover, 5-Aza and PBAinhibited the motility activity of the prostate cancer cells concomitant with decreasedexpression of gpx chain and galectin-3. Conclusionrom these markers, we constructed with 8 nodes decision tree model, the blindtest results showed that the model could accurately predict about 90% prostate cancer.Among these markers, the mass 15868 showed greatest promosing biomarker in earlydetetion of prostae cancer. After purifying the marker with IMAC column, the proteinwas characterized as LZTR2 by EDMAN sequencing technology.Immunohistochemistry staining antibody with LZTR2 showed it highly expressed in theprostate cancer cells and in clinical cases, it showed complement to PSA in thediagnosis of the prostate cancer.This study represents the first demonstration that the SELDI-TOF-basedserum proteomic array technique is effective for the diagnosis of PCa inChinese men. Such an approach is useful for the research of other cancersrequiring biomarker analysis.