- Open Access
Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer
© Pressinotti et al; licensee BioMed Central Ltd. 2009
Received: 17 July 2009
Accepted: 27 December 2009
Published: 27 December 2009
Despite recent progress in the identification of genetic and molecular alterations in prostate cancer, markers associated with tumor progression are scarce. Therefore precise diagnosis of patients and prognosis of the disease remain difficult. This study investigated novel molecular markers discriminating between low and highly aggressive types of prostate cancer.
Using 52 microdissected cell populations of low- and high-risk prostate tumors, we identified via global cDNA microarrays analysis almost 1200 genes being differentially expressed among these groups. These genes were analyzed by statistical, pathway and gene enrichment methods. Twenty selected candidate genes were verified by quantitative real time PCR and immunohistochemistry. In concordance with the mRNA levels, two genes MAP3K5 and PDIA3 exposed differential protein expression. Functional characterization of PDIA3 revealed a pro-apoptotic role of this gene in PC3 prostate cancer cells.
Our analyses provide deeper insights into the molecular changes occurring during prostate cancer progression. The genes MAP3K5 and PDIA3 are associated with malignant stages of prostate cancer and therefore provide novel potential biomarkers.
Prostate cancer is the most frequent cancer diagnosed in men (20.3% of the total), followed by lung (17.2%) and colorectal cancer (12.8%) . Measuring prostate specific antigen (PSA) has been a matter of routine to detect prostate cancer, but is insufficient to distinguish between different tumor grades. The Gleason Grading System is commonly used for histology-based grading of prostate cancer tissue . Since prostate tumors are often multifocal, the Gleason Score (GS) is the sum of the two most prevalent tumor patterns, which are graded 1 (CA1) as the most differentiated and 5 (CA5) as the least differentiated pattern of cancerous glands. Other methods for sub-classification have been described in recent reports . These indicate that translocations fusing the strong androgen-responsive gene TMPRSS2 with ERG or other oncogenic ETS factors may facilitate prostate cancer development. It has been proposed that the presence or absence of this genetic rearrangement may be used, much like the Gleason grading system, as a diagnostic tool to extract prognostically relevant sub-classifications of this cancer .
The discrimination between different tumor grades is important with respect to treatment decisions: Currently, many men who are diagnosed with GS 6 prostate cancer are often "over"-treated and risk suffering from urinary and sexual dysfunction . Therefore, it is important to develop a sensitive and specific diagnostic tool to distinguish between different tumor grades. To address this problem, many groups have recently started to profile gene expression levels in prostate tumor tissues to identify deregulated genes during disease progression. However, although many of these have addressed the question of molecular differences between normal, tumor, benign prostatic hyperplasia (BPH), and the putative precursor lesion prostatic intraepithelial neoplasia (PIN), little is still known about molecular changes between low- and high-risk tumors [6–9].
In the present study, we performed microarray-based gene expression profile analysis of 65 microdissected tissues comprising 25 samples of GS 6, 27 of GS 8-10 and 13 non cancerous samples. We sought to identify biological markers of distinct functional groups for the discrimination between low- and high-risk tumors. Overall, we found 20 genes with a significant alteration in expression between high-risk compared to low-risk tumors. Two of these genes exhibited Gleason grade associated protein expression in tumor tissues, which could serve as a valuable diagnostic tool in the future.
mRNA expression analysis revealed large expression differences between GS 6 and GS 8-10 tumors
Characteristics of study population
Number of differentially expressed genes (FDR < 5%)
Comparison (SAM test)
Normal ↔ Tumor
Normal ↔ Tumor (GS6)
Normal ↔ Tumor (GS8-10)
Tumor (GS6) ↔ Tumor (GS8-10)
For the identification of grade-discriminating genes, we compared the expression levels of GS 6 with GS 8-10 tumors. SAM analysis revealed 1141 up-regulated and 54 down-regulated non-redundant genes in advanced tumors (FDR 5%; see additional file 1). For validation, we compared our data with an independent study from True and coworkers, who reported 86 genes as deregulated during tumor progression from low to high GS . Of these, we identified 24 genes (28%) which all displayed the same tendency as in the original report (see additional file 3). Another comparison to the study of Lapointe and coworkers , who described 41 genes to be associated to a higher Gleason score revealed an overlap of six genes (BGN, COL1A2, COL3A1, PLA2G2A, SPARC, VCAN).
To identify biological processes associated with tumor progression, we performed gene ontology analysis with genes differentially regulated between low- and high-risk tumors. In order to extract highly significant canonical pathways, each gene symbol was mapped to its corresponding gene object in the IPA Knowledge Base, and networks were generated. Significant canonical pathways were related to actin-mediated processes, e.g. regulation of actin-based motility mediated by Rho-family GTPases (11 of 92 annotated genes, p = 5.23E-03) and actin cytoskeleton signaling (19 of 221 annotated genes, p = 1.4E-02). In addition, various metabolic processes, including oxidative phosphorylation (22 of 158 annotated genes; p = 8.95E-06) and protein ubiquitination (19 of 205 annotated genes; p = 5.6E-03) were deregulated in high-risk tumors. In order to extract as many genes as possible involved in apoptotic processes we used two further GO analysis tools (FatiGO  and GOstat ) and identified a set of 46 genes associated with apoptosis (GOstat p = 0.00033; see additional file 4).
Additionally, we compared gene signatures between normal and tumor tissue, which lead to the identification of > 2500 deregulated genes (FDR 5%) of which 2390 genes were down and 243 up-regulated (see additional file 2). We performed separate SAM analyses between normal and GS 6 or GS 8-10 and revealed 2016 and 2001 down-regulated as well as 463 and 454 up-regulated non redundant genes (Table 2). Of these, 1197 genes were deregulated with the same tendency in both tumor groups. Interestingly, three genes (VCAN, CLK1 and TMEM16G) revealed opposite expression levels in these comparisons. VCAN and CLK1 were found to be significantly down-regulated in GS 6, but up-regulated in high-risk tumors in comparison to normal tissue. In agreement with these results, over-expression of VCAN and CLK1 between primary prostate cancer and metastatic cancer has recently been described . In contrast TMEM16G, for which the microarray findings were supported by qRT-PCR data, showed the opposite trend. TMEM16G was found up-regulated between normal and GS 6 tissue, but down-regulated between normal and GS 8 as well as between GS 6 and GS 8 tissues. In a recent study, TMEM16G was described as a prostate-specific plasma membrane protein promoting cell-cell contact in the prostate cancer cell line LNCaP .
Validation of selected genes by qRT-PCR
qRT-PCR verified differentially expressed genes between GS 6 and GS 8-10
fatty acid beta-oxidation
multicellular organismal development
response to stress
ubiquitin-dependent protein catabolic process
phospholipid metabolic process
Immunohistochemistry demonstrates Gleason-grade associated protein expression of MAP3K5 and PDIA3
Decreased apoptotic activity upon knockdown of PDIA3 in prostate cancer cell lines
Recent studies showed that it is important to include different tumor stages of prostate cancer in gene expression analyses to be able to find new diagnostic and prognostic markers [6, 8, 9]. Here, we generated gene expression profiles of tissues from low-risk (GS 6) and high-risk prostate tumors (GS 8-10) tissues. In contrast to most other published studies, all tissue samples were carefully microdissected before RNA isolation. The comparison of these profiles revealed that both tumor subgroups differ by a large number of genes, most of which are up-regulated in high GS tumors. A comparison with another published data set  suggested that these results reflect a general trend in transcriptional activation in advanced prostate tumors. However, it cannot be fully ruled out that systematic changes introduced by different extents of stromal cells  in the microdissected material as well as two rounds of RNA amplification contribute to this bias.
Among the detected genes many are involved in pathways, which are known to be altered in tumor progression such as apoptosis, morphologic changes, metabolism, and ubiquitin-mediated protein degradation. Twenty representatives of these processes were verified via qRT-PCR, and a set of genes discriminating between less and more aggressive tumor forms was identified. One of the hallmarks of aggressive cancer is the imbalance between cell survival and apoptosis. Our gene ontology analysis revealed that a pronounced number of apoptosis-related genes exhibited expression changes between low- and high-risk prostate tumors. Thus, for validation, we focused our analysis on up-regulated anti-apoptotic genes and key players of apoptotic signaling and verified the expression level of 11 selected genes. These data were supported by the analysis of protein expression levels for MAP3K5 and PDIA3 by IHC. MAP3K5 and PDIA3 were also identified in other prostate cancer profiling studies [6, 7, 9, 21]. MAP3K5 (also known as apoptosis signal-regulating kinase 1; ASK1) has been widely accepted as one of the key components regulating reactive oxygen species (ROS) - induced JNK and p38 activation leading to differentially regulated apoptosis . ROS - dependent activation of MAP3K5 also plays a critical role in innate immune responses through production of proinflammatory cytokines . There is considerable evidence suggesting that oxidative stress contributes to the pathogenesis of prostate cancer [24, 25]. Given that mitochondria are a major source of reactive oxygen species (ROS), altered mitochondrial bioenergetics might induce MAP3K5 over-expression and contribute to the malignant progression of prostate tumors. In concordance with this hypothesis, we also found a significant number of deregulated genes involved in oxidative phosphorylation and mitochondrial dysfunction.
Like MAP3K5, PDIA3 (protein disulfide isomerase A3) is a member of the endoplasmatic reticulum stress signaling pathway also known as unfolded protein response (UPR), and its expression level increases in response to cellular stress due to its function as a chaperone . Recently published data connected PDIA3 to the apoptotic process and demonstrated an anti-apoptotic effect of PDIA3 in the melanoma cell line A375 after induction of ER stress . In contrast, our study suggested a decrease of caspase activity due to down regulation of PDIA3 in prostate cancer cell lines. This result suggests that the observed increase of PDIA3 in this study is most likely due to elevated cellular stress. But besides the role as a chaperone, PDIA3 might function as a pro-apoptotic protein in the prostate. Taking our IHC data of PDIA3 into account, PDIA3 protein concentration decreases significantly in CA5 compared to CA4 tissues and expression data comparing localized with metastatic prostate cancer showed a down-regulation of PDIA3 . These findings support the idea that down regulation of PDIA3 might play a role in late onset of prostate cancer progression. A lack of PDIA3 expression also correlates with increased tumor invasion and advanced stage of gastric cancer and has therefore been proposed to be a negative prognostic marker .
In addition to its role in the ER stress pathway, PDIA3 has recently gained attention due to its function as a component of the peptide-loading complex of the major histocompatibility complex (MHC) class I pathway [29, 30]. In PDIA3 deficient mice this complex is impaired and negatively influences presentation of antigenic peptides. This may help tumors to escape from immune surveillance by cytotoxic T cells .
The results of our IHC analysis point to a potential use of PDIA3 as a diagnostic marker: PDIA3 expression of Gleason pattern 3 tumors is higher in the presence of a Gleason pattern 5 tumor than in presence of another Gleason pattern 3 tumor. Additionally, PDIA3 and MAP3K5 have been found to be significantly (FDR 5%) up-regulated in tumors harboring a TMPRSS2 - fusion protein (data not shown). This underlines the opportunity to use PDIA3 and MAP3K5 as discriminating biomarkers in respect to histological grading system and gene arrangement classification.
In summary, this study validated a set of 20 genes, which discriminated between low and high Gleason grade prostate tumors. These genes comprise important functional processes well known to be involved in tumor progression such as apoptosis, morphological changes, metabolism and others. In addition, we show a grade-associated protein expression of MAP3K5 and PDIA3. In particular, high PDIA3 protein levels in Gleason pattern 3 cancers may indicate the presence of more aggressive tumor foci in the same tissue and could be of diagnostic value, possibly as part of a larger molecular signature.
Materials and methods
Frozen and paraffin-embedded prostate tissue samples were obtained from previously untreated patients who had undergone radical prostatectomy after tumor diagnosis in a PSA based screening program performed in Tyrol by the Department of Urology, Medical University of Innsbruck . The study was approved by the ethics committee at the Medical University of Innsbruck. Immediately after surgery, the prostate specimens were cooled in ice/water and brought to the pathologist who performed a rapid section and isolated a prostate slice that was embedded in Tissue-Tek OCT Compound (Sakura, Tokyo, Japan), snap frozen in liquid nitrogen and stored at -80°C until use. The rest of the prostate was fixed and paraffin-embedded according to standard procedures.
For isolation of total RNA, frozen sections were stained with hematoxylin and eosin for pathological analysis and exact localization of the tumors. Parallel unstained slides were used for microdissection. These were pre-treated for 1 min in each of the following pre-cooled solutions: 75% ethanol, RNase-free water, 100% ethanol (twice) and xylene (twice), and air dried. Laser-capture microdissection was performed on a Pix Cell II microdissection microscope (Arcturus, Sunnyvale, CA, USA) using 2,000-5,000 laser impulses corresponding to approximately 15 000 - 30 000 cells for each sample. Tumor samples were isolated from a cohort of Gleason score 6 tumors (Gleason pattern 3) and a group of Gleason score 8 - 10 tumors (Gleason patterns 4 and 5). Benign epithelial cell samples were microdissected apart from tumor foci from histopathologically normal regions of the same specimens. After microdissection, total RNA was isolated using the PicoPure isolation kit (MDC, Sunnyvale, USA) according to the protocol of the supplier. Quality control was done employing the Agilent Bioanalyzer 2100 system (Agilent Technologies, Waldbronn, Germany).
20 ng of RNA isolated from laser-capture microdissected epithelial prostate cells of tissues from patients who had undergone radical prostatectomy were subjected to a two-round amplification using the MessageAmpTM II aRNA Amplification Kit (Applied Biosystems/Ambion, Austin, USA). The quality of amplified RNA (aRNA) fragments was assessed by microcapillary electrophoresis using the Agilent Bioanalyzer 2100 system. Two micrograms of aRNA were subjected to microarray hybridization as described in . Briefly, aRNA was reverse transcribed using SuperScript II reverse transcriptase (Invitrogen, San Diego, CA, USA) and labeled with Cy5-dUTP. Each sample was compared to a common reference (Universal Human Reference RNA; Stratagene, La Jolla, CA, USA) labeled with Cy3-dUTP. Hybridizations were done using the platform Human Unigene3.1 cDNA Array 37.5K v1.0 (NCBI, GEO, GPL3050) representing estimated 22,000 transcripts. Data were analyzed using the GenePix 4.0 software (Axon Instruments, Foster City, USA). Low quality measurements were excluded from further analysis. Raw expression values were pre-processed using ArrayMagic  and thereby normalized using the VSN method .
Data analysis and data mining
Significance Analysis of Microarrays (SAM) was applied to identify genes differentially regulated between normal tissue, tumor tissue GS 6 and GS ≥ 8 . A two class unpaired SAM test with 1000 permutations was used. The False Discovery Rate (FDR) was set below 5%. Results from the SAM analysis were imported into FatiGO  and Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, Redwood City, CA, USA) to identify gene ontologies that were significantly over- or under-represented. MatchMiner software  was used to match gene entries between different microarray studies.
Quantitative real-time RT-PCR validation
Verification of expression of selected genes was performed via quantitative real-time RT-PCR using the ABI Prism 7900 HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA) and the Universal Probe Library System (Roche, Basel, Switzerland). Ct values were extracted by using the SDS-software (Applied Biosystems). The expression level of the housekeeping gene β-2-microglobulin was used for normalization, calculated with the 2-ΔΔCt method . Gene expression differences between GS 6 and GS 8-10 tumors were analyzed using t-test. A list of examined genes including mean Ct values of each analyzed group and corresponding primers is given in additional file 5.
For validation of expression at the protein level, we used immunohistochemistry (IHC) on corresponding paraffin-embedded tissue specimens from the same patient cohort. Immunohistochemistry was performed with 5 μm paraffin tissue sections employing the Ventana Discovery - XT staining automat (Roche). Standard CC1 pre-treatment and antigen retrieval was followed by incubation with antibody solution for 1 hr, choice of amplification kit, universal antibody solution for 60 min, staining with DAP map kit and counter stain for 4 min with haematoxylin II bluing reagent (all from Roche). For expression analysis of MAP3K5 (ASK1) protein the mouse monoclonal antibody EP553Y (Abcam Limited, Cambridge, MA, USA) was used at a dilution of 1:40 for the PDIA3 protein the mouse monoclonal antibody MaPERp571 (Abcam) at a dilution of 1:10. Specificity of staining was controlled by including a control antibody (DAKO Cytomation, Glostrup, Denmark). Immunoreactivity was then scored by a uropathologist and stratified according to the histology and the Gleason pattern of the specimens using a 4 point scaling system: 0, no staining, 1, weak staining, 2, intermediate staining, 3, strong staining.
RNA interference and apoptosis assay
The human prostate cancer cell line PC3 and LNCaP were purchased from ATCC (Manassas, VA, USA) and cultured in RPMI 1640 or HAMs F12 medium, respectively. The medium was supplemented with 50 units/ml penicillin, 50 μg/ml streptomycin sulphate, 1% nonessential amino acids, and 10% FBS (all from GIBCO/BRL, Gaithersburg, MD, USA).
PDIA3 siRNA (target sequences, see additional file 6) was purchased from Dharmacon (Lafayette, CO, USA) and evaluated against a scrambled siRNA control (Qiagen, Hilden, Germany). The siRNA knockdown experiments were performed by plating 0.8 × 104 cells PC3 cells in a 96-well plate (NUNC, Roskilde, Denmark) overnight. For transfection, siRNA and Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) were diluted separately and incubated for 5 min at room temperature. The two solutions were mixed and incubated for 20 min at room temperature. siRNA-Lipofectamine 2000 mixture was then added to the cells, and the plate was mixed by gentle rocking. Transfected cells were incubated at 37°C and 5% CO2 for 48 h. Knockdown efficiency was verified by qRT PCR.
Induction of apoptosis was performed by adding the indicated amounts of Staurosporine (Roche, Mannheim, Germany), Fenretinide (Sigma, Munich, Germany) or Tapsigargin (Sigma) for 6 and 24 hours, respectively. Control cells were left untreated. Activation of apoptosis was determined by measuring caspase 3 and 7 activities using Caspase-Glo 3/7® Assay (Promega, Madison, WI, USA) following the manufacturer's protocol.
The microarray data sets reported in this paper have been deposited MIAME compliant to NCBI Gene Expression Omnibus (GEO) database (accession no. GSE15484).
This article is dedicated to the memory of Professor Annemarie Poustka, who was the founder and head of the Division Molecular Genome Analysis at the DKFZ. She was an inspiring scientist and a wonderful person.
This study was supported by a grant of the Austrian Nationalstiftung and the Austria Wirtschaftsservice GmbH in the framework of the IMGuS research program (Institute for Medical Genome Research and Systems Biology, Wien). We thank Sabrina Balaguer Marcello Schifani, Christian Pohl, Christof Seifarth and Heidi Huebl for excellent technical assistance, Holger Fröhlich for statistical analysis and Sabine Elowe for critical reading of the manuscript.
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