| Background and objectives:Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are two commom diseases for the elder men on the urology. There are about13.6%of the elder men suffered from BPH in China. Geography and race difference are obvious in the incidence of prostate cancer. PCa is the most common male malignancy in the developed countries in Europe and America. About1/3of male malignancy is Prostate cancer. It is the second leading cause of cancer-related death in men, after lung cancer. In America, the incidence rate is about200,000every years, the death rate is near20%. The incidences of PCa in Asia appear to be rapidly increasing, though lower than western countries. In China, as people’s life become longer, diet structure and living habit changed diagnosis methods be improved, the incidence of PCa is increasing year by year.It become the major cause of death in men. urinary surgery research center of Beijing University had analyzed the constituent ratio of urinary tumor. They found PCa patient quantity was increasing. So PCa will be the major disease of threatening male health in China.In clinical, prostate-specific antigen (PSA) is the major marker to screen and diagnose PCa. The Gleason score is used for grade prostate cancer tissue. The clinical staging of PCa is defined by TNM staging. But PSA is not specificity limited in prostate cancer.Its values can be high in men with BPH, prostatitis and breast cancer. It is hard to identify PCa or BPH when PSA value is in4-10μg/1which called grey zone. Because Gleason score is subjectively determined by a pathologist, it is difficult to assess the malignant degree of PCa objectively and accurately. Thus there is an urgent need to find key carcinogenesis-associated molecules for PCa diagnosis. The molecules must be more specific and sensitive which can reflect the invasiveness of PCa and help to decide the prognosis of PCa.Transcriptome analysis, enabled by technology such as oligonucleotide microarray, is a simultaneous interrogation of gene expression by measuring the transcriptional activity on a global scale. The human genome project has lead to the identification of some32,000genes in human cells. The expression levels for this complete set of genes can now be assessed using microarray technology. This advancement has fundamentally changed how investigators approach biomedical questions and provides unparalleled opportunities for marker discovery. Two main types of microarrays are being utilized for gene expression profiling studies. The most popular one is the short oligonucleotidechips produced by Affymetrix in1991. The second major platform consists of printed cDNA fragments or long oligonucleotide on glass slides or other types of solid support. Other than the fact that microarray analysis suffers from inherently noisy information, the other problem is the sheer volume of information obtained from this type of experiment. Dissection of global changes in gene expression during pre-disease states, disease progression, and following clinical treatment can provide great insight into disease mechanism and treatment management.Proteome analysis is the most widely based on methods using differential expression on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) gels or, more recently, two dimensional chromatography followed by mass spectrometry protein identification. It is considered as a powerful tool for global evaluation of protein expression, and has been widely applied in analysis of diseases, especially in fields of cancer research. Two dimensional difference gel electrophoresis (2D-DIGE) technology, using a mixed-sample internal standard, is now recognized as an accurate method to determine and quantify human proteins, reducing inter-gel variability and simplifying gel analysis. Using the2D-DIGE approach, different samples prelabeled with mass-and charge-matched fluorescent cyanine dyes are co separated in the same2D gel, and an internal standard is used in every gel that has negated the problem of intergel variation. It has been reported that the correlation between quantitation by2D-DIGE and metabolic stable isotope labeling is exceptionally good. In addition, this method reduces the number of gels needed for one experiment. Given these advantages over traditional2-DE, more accurate quantitative and qualitative analyses of2D-DIGE have been applied to proteomic studies in several human cancers, including hepatocellular carcinoma, colorectal cancer, esophageal squamous cell carcinoma, breast cancer, ovarian cancer, bladder Cancer, lymph node metastatic prostate cancer and pancreatic cancer.To our knowledge, a number of studies in cancer have employed gene expression microarrays to profile tumor tissues and validated the effectiveness of the microarray technique, allowing identification of tumor subclasses and marker genes for diagnosis and treatment of the disease. However, much less work has been done at the protein level. Proteins, as opposed to nucleic acids, represent the functional effectors of cancer progression and thus serve as therapeutic targets as well as tumor markers. Based on this notion, in the present study, using an integrative approach, we analyzed the proteomic profiles with mRNA transcript data from our recent microarray gene expression profiling study. Then, the differentially expressed spots were identified, and the proteins of interest were further validated by Enzyme-linked Immunosorbent Assay (ELISA). The signal transduction pathway of differentially expressed proteins could be achieved by Bioinformatics, and get the message of its function in further. Analyzed the relationship between the candidate proteins and clinically pathological date could be helpful to find the tumor marker which help to diagnose and decide the prognosis of PCa.Materials:Four fresh PCa tissues and paired4adjacent benign tissues of prostate obtained from4PCa patients who underwent radical prostatectomy and transurethral prostatic resection (TURP) were provided from September2010to February2011by Guangzhou First Municipal People’s Hospital, Guangzhou, China. Fresh PCa and adjacent benign tissues of prostate were obtained immediately after the surgery and snap-frozen in liquid nitrogen and then stored at-80℃until use. The average age of PCa patients was70.5years old (54-80years old). The pathology malignancy grade and Gleason score detected from paraffin section were recorded as well as the clinical stage.At the same time, for protein validation by ELISA, pre-operative blood was obtained by venipuncture from84patients with PCa and35patients with benign prostatic hyperplasia (BPH) who were operated at the Guangzhou First Municipal People’s Hospital and Guangdong Provincial People’s Hospital, Guangzhou, China. The clinicopathological data of these patients are recorded. Twenty one control blood samples were donated (on a voluntary basis) by male, healthy, habitual, and controlled blood donors in the community of Guangzhou. The average age of84PCa patients was74.3years old (49-87years old).10patients’PSA value were less than10ng/mL,49patients’ Gleason score less than8,9patients have metastasis. The average age of35PCa patients was71.1years old (56-89years old).23patients’ PSA value was less than lOng/mL. The average age of21voluntary was30.42years old (18-43years old)Methods:Using integrative transcriptome and proteome to detect4fresh prostate cancer tissues and paired4adjacent benign tissues of prostate. After screened from the GEO database,6candidate tumor markers were detected with ELISA. Analyzed the relationship between the candidate proteins and clinically pathological parameters:(1)、Obtained the clinical sample tissues and made pathological section for HE staining, the pure tissues were taken by Laser capture microdissection (LCM).(2)、 Total RNA was extracted and purified from these samples using TRIzolTM reagent (Invitrogen, Carlsbad, CA, USA), following the anufacturer’s instructions.(3)、 cRNA samples were prepared and hybridized to the array (Agilent-014850Whole Human Genome Microarray4x44K G4112F).(4)、Bioconductor, a biological data analysis package based on R statistical programming language, was used for array data analysis and integration with other gene annotations.(5)、Protein was extracted and purified from these samples using clean up kits.(6)、After the preliminary experiment of protein electrophoresis with silver staining, the protein extracts were labeled with Cy2、Cy3and Cy5fluorescent dye according to the manufacturer instructions (GE Healthcare).(7)、The labeled protein was carried out with2D-DIGE.(8)、The proteins were visualized by using a fluorescence scanner at appropriate wavelengths for Cy2, Cy3, and Cy5dyes (Typhoon9400; GE Healthcare).(9)、Image analysis was carried out with the DeCyder5.01software (GE Healthcare). The DeCyder differential ingel analysis (DIA) module was used for pairwise comparisons of each adjacent benign tissue of prostate and PCa sample to the mixed standard present in each gel and for the calculation of normalized spot volumes/protein abundance.(10)、 MALDI-TOF mass spectrometry and tandem TOF/TOF mass spectrometry were carried out on a4800Proteomics Analyzer (Applied Biosystems).(11)、After index on GEO data base，6candidate proteins were screened from14differentially expressed proteins.(12)、The candidate protein concentrations of84cases of PCA,35cases of BPH and21cases of normal people were detected by Enzyme-linked Immunosorbent Assay (ELISA).(13)、Analyzed the relationship of the candidate protein level and clinical pathological date.Statistical treatment:The software of SPSS version13.0for windows was used for statistical analysis. Continuous variables were expressed as x±s. Pair t-test was performed for analyzing the different expression of mRNA between prostate cancer tissue and the adjacent tissue. Pair T-test was also performed for analyzing the different expression of protein between prostate cancer tissue and the adjacent tissue. One-way ANOVA test was performed for analyzing the plasma concentration of candidate protein in different experimental groups, pairwise comparisons using the Bonferroni method. Independent-samples t test analysis the relationship between the plasma concentration of candidate protein and clinical pathological parameters. Differences were considered statistically significant when P was less than0.05.Results:1. We compared the gene expression profile of PCa tissues to adjacent benign tissues of prostate using gene expression microarray.1207genes that were consistently different from adjacent benign tissues of prostate (paired t test, P<0.05) were selected as differentially expressed genes (DEGs). Among them,652DEGs were upregulated in PCa, whereas555DEGs were downregulated in PCa2. After2D-DIGE, the Cy2, Cy3, and Cy5channels of each gel were individually imaged, and the images were analyzed using DeCyder5.0software. Among2566matched protein spots,89were significantly up-regulated in the tumor group (ratiotumor/nontumor≥2, P<0.01), whereas66spots were down-regulated (rationontumor/tumor≤-2, P<0.01).3. A total of60differentially expressed proteins, including37that were upregulated and23that were down-regulated in the PCa tissues, were successfully identified. The identified proteins were located in the cytoplasm (16proteins,26.7%), mitochondrion (15proteins,25.0%), nucleus (12proteins,20.0%), and extracellular space (4proteins,6.7%)(base on Swiss-Prot database annotation). The subcellular localizations of13(21.7%) indentified proteins are unknown. Additionally, among60PCa significant proteins,25(41.7%),18(30.0%),8(13.3%),6(10.0%) and3(5.0%) proteins are involved in cellular metabolic process, regulation of biological process, gene expression, cell motility and establishment of localization in cell, respectively (based on Swiss-Prot database annotation). Moreover, these proteins were grouped into different functional classes:(1) enzymes (30proteins,50.0%, including oxidoreductase, hydrolase, transferases, isomerase and lyases),(2) binding proteins (28proteins,46.7%),(3) signal transduction proteins (6proteins,10.0%),(5) metastasis-related proteins (4proteins,6.7%),(6) carrier proteins (2proteins,3.3%),(7) transcription factors (2protein,3.3%), and (8) other proteins of unknown function (10proteins,16.7%).4. Based on the results of gene expression microarray and2DDIGE,14genes and their protein products (ACLY, CAPG, GSTM3, GSTP1, HNRNPL, IMPDH2, KRT15, MCCC2, MSN, MYL9, PYGB, SERPINB5, TRAP1and VCL) were identified to be differentially expressed in PCa tissues.5. After index on GEO data base,6candidate proteins were screened from14differentially expressed proteins. The protein concentrations were detected with ELISA.6candidate proteins:IMPDH2、MCCC2、TRAP1、CAPG、KRT15and MYL9.6.6proteins were chosen for validation and analysis by ELISA with the serum of84PCa patients,35BPH patients and13healthy controls. Compared with BPH patients and healthy controls, the t test showed that MCCC2, Tumor Necrosis Factor Receptor-associated Protein1(TRAP1) and Inosine monophosphate dehydrogenase II (IMPDH2) levels were significantly elevated in PCa, which correlated with the2D-DIGE results.7. Independent-samples T test analysis the relationship between the plasma concentration of candidate protein and clinical pathological parameters. The serum IMPDH2level did not correlate with the age, prostate gland volume and the serum PSA levels of the PCa patients (P=0.551,0.315and0.507, respectively). Additionally, the serum IMPDH2levels were significantly higher in tumors with high Gleason scores (≥7) than those with low Gleason scores (<7, P=0.030). Higher serum IMPDH2levels were also associated with the presence of metastasis (P=0.023). The serum MCCC2levels were significantly higher in tumors with high Gleason scores (≥7) than those with low Gleason scores (<7, P=0.018). Higher serum MCCC2levels were also associated with the presence of metastasis (P<0.001). Higher serum TRAP1levels were also associated with the volume of prostate (P=0.032).Conclusions:1. Integrative gene expression microarray and2D-DIGE coupled with MS analysis t o the PCa tissue specimens would be a better way to discover novel and effective cancer markers.2. The expression of IMPDH2and MCCC2were positively correlated with the Gleason Score and metastasis of prostate cancer.3. The expression of TRAP1was positively correlated with the volum of prostate gland.4. It may help to improve the early diagnosis rate of prostate cancer by detecting the expression of IMPDH2and MCCC2, which are hoped to be the new serum tumor marker to reflect the malignancy of prostate cancer and judge prognosis of prostate cancer. |
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