We profiled the expression of genes in benign and untreated human prostate cancer tissues using oligonucleotide microarrays. We report here 50 genes with distinct expression patterns in metastatic and confined tumors (Gleason score 6 and 9; lymph node invasive and noninvasive). Validation of expression profiles of 6 genes by quantitative PCR revealed a strong inverse correlation in the expression of zinc finger protein 185 (ZNF185), bullous pemphigoid antigen gene (BPAG1), and prostate secretory protein (PSP94) with progression of prostate cancer. Treatment of prostate cancer cell lines with 5-aza-2′-deoxycytidine (5-Aza-CdR), an inhibitor of DNA methylation, restored ZNF185 expression levels. Moreover, methylation-specific PCR confirmed methylation of the 5′CpG islands of the ZNF185 gene in all of the metastatic tissues and 44% of the localized tumor tissues, as well as in the prostate cancer cell lines tested. Thus, transcriptional silencing of ZNF185 by methylation in prostate tumor tissues implicates the ZNF185 gene in prostate tumorigenesis.

Tumor stage, Gleason score, and preoperative serum prostate specific antigen are currently the only well-recognized predictors of prostate cancer progression. However, these markers cannot reliably identify men that ultimately fail therapy, and they give us no insight into prostate carcinogenesis or potential therapeutic targets for prostate cancer (1). Several groups recently implemented DNA microarrays to analyze differentially expressed genes between corresponding normal and cancer tissues to advance the understanding of the molecular basis of malignancy, and potentially serve as biomarkers or prognostic markers of malignancy (2, 3, 4, 5, 6). The altered expression of the genes characteristic of the specific stage of the cancer is now being considered as a supplementary approach to the histopathological work-up of precancerous and cancerous lesions of the prostate (7).

Inactivation of tumor suppressor genes is an important event contributing to the development of neoplastic malignancies. In addition to the classical genetic mechanisms involving deletion or inactivating point mutations, growth regulatory genes can be functionally inactivated by epigenetic alterations, i.e., alterations in the genome other than the DNA sequence itself, which include global genomic hypomethylations (8), promoter hypermethylation of CpG islands (9, 10), histone deacetylations, and chromatin modifications (11, 12). Molecular analysis of tumor-derived genetic and epigenetic alterations may have a profound impact on cancer diagnosis and monitoring for tumor recurrence (10).

The objective of this study was to identify biologically and clinically relevant clusters of genes characteristic of prostate cancer versus benign tissues and confined versus metastatic prostate cancer using oligonucleotide microarrays. The expression profiles were generated from 5 metastatic prostate tissues, and 23 confined tumors including 12 Gleason score 9 (high grade) and 11 Gleason score 6 (intermediate grade) tumors. In addition, 8 adjacent benign prostatic tissues were also studied. We have shown 50 genes with distinct expression patterns in prostate cancer compared with benign prostatic tissues. Expression levels of prostate secretory protein (PSP94), zinc finger protein (ZNF185), bullous pemphigoid antigen gene (BPAG1), prostate specific transglutaminase gene (TGM4), Erg isoform 2 (Erg-2), and Rho GDP dissociation inhibitor (RhoGDI-β) were validated by Taqman quantitative real-time PCR. Furthermore, analysis of the expression of ZNF185 in prostate cancer cell lines revealed an increase in the expression by treatment with an inhibitor of DNA methylation, 5-Aza-CdR. MSP indicated ZNF185 inactivation by CpG dinucleotide methylations in prostate cancer cell lines and cancer tissues. Our studies show that down-regulation of ZNF185, PSP94, and BPAG1 with epigenetic alteration of ZNF185 is highly associated with prostate cancer progression and potentially can be useful as a biomarker for predicting progression of the cancer.

Prostate Tissues.

Prostate cancer tissue specimens were obtained from patients who had undergone radical prostatectomy for prostate cancer at Mayo Clinic. The Institutional Review Board of Mayo Foundation approved collection of tissues and their use for this study. None of the patients included in this study had received preoperative hormonal therapy, chemotherapy, or radiotherapy. Harvested tissues were embedded in OCT and frozen at −80°C until use. A H&E-stained section was prepared to insure that tumor was present in the tissue used for the analyses. Of 340 tissues available in our tissue bank, we selected tissues that had >80% of the neoplastic cells by histological examination. To examine differential gene expression in intermediate- (Gleason score 6) and high-grade (Gleason score 9) prostatic adenocarcinoma and metastatic tumors, we studied 11 primary stage T2 Gleason score 6 cancers (6 with positive regional lymph nodes and 5 with negative lymph nodes), 12 primary stage T3 Gleason score 9 cancers (6 with positive regional lymph nodes and 6 with negative lymph nodes), and 5 metastatic tumors. Table 1 shows Gleason grade, age, preoperative serum prostate-specific antigen levels, and staging of all of the patients from whom prostate tissues were obtained for this study. Twelve separately collected prostatic tissue samples matched with the cancer tissues (obtained from the same patients) were used as normal controls.

Isolation of RNA and Gene Expression Profiling.

Thirty prostate tissue sections of 15-μm thickness were cut with a cryostat and used for RNA isolation. Total RNA was extracted from frozen tissue sections with TRIzol reagent (Life Technologies, Inc., Carlsbad, CA). DNA was removed by treatment of the samples with DNase I using DNA-free kit (Ambion, Austin, TX), and additional RNA cleanup was performed using RNeasy Mini kit (Qiagen, Valencia, CA) according to the manufacturer’s protocols. RNA quality was monitored by agarose gel electrophoresis and also on Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). High-density oligonucleotide microarrays HG-U95Av2 containing 12,625 sequences of human genes and expressed sequence tags (Affymetrix, Santa Clara, CA) were used in this study. Complementary RNA was prepared, labeled, and hybridized to oligonucleotide arrays as described previously (13). The arrays were scanned with gene array scanner (Agilent Technologies). All of the arrays were scaled to a target intensity of 1500. Raw data were collected and analyzed by using Affymetrix Microarray Suite 5.0 version.

Quantitative Real-Time Reverse Transcription-PCR.

To confirm the differential expression of genes from microarray data, we selected 4 down-regulated genes, ZNF185, PSP94, BPAG1, and TGM4, and 2 up-regulated genes, Erg-2 and RhoGDI-β, for validation by Taqman real-time reverse transcription-PCR in a total of 44 tissues including 36 samples used for microarrays with an additional 4 primary tumors and 4 adjacent benign tissues. One μg of the total RNA was used for first-strand cDNA synthesis. The PCR mix contained 1× reaction buffer [10 mm Tris and 50 mm KCl (pH 8.3)], MgCl2 (5 mm), PCR nucleotide mix (1 mm), random primers (0.08 A260 units), RNase inhibitor (50 units), and avian myeloblastosis virus reverse transcriptase (20 units) in a final volume of 20 μl.

For real-time PCR 1 μl of the cDNA was used in the PCR reactions. Taqman real-time primers and probes were designed using the software Primer Express version 1.5 (PE Applied Biosystems, Foster City, CA) and synthesized at Integrated DNA Technologies (Coralville, IA). The sequences of the primers and probes for each gene are provided in Fig. 2 A. Probes were labeled at 5′ end with the reporter dye 6-carboxyfluorescein and at 3′ end with a Black Hole Quencher. Probes were purified by reverse-phase high-performance liquid chromatography, and primers were PAGE purified. All of the PCR reactions were carried out in Taqman Universal PCR master mix (PE Applied Biosytems) with 300 nm of each primer and 200 nm of probe in a final volume of 50 μl. Thermal cycling conditions were as follows: 2 min at 50°C, with denaturation at 95°C for 10 min, 40 cycles of 15 s at 95°C (melting), and 1 min at 60°C (annealing and elongation). The reactions were performed in an ABI Prism 7700 Sequence Detection System (PE Applied Biosystems). To evaluate the validity and sensitivity of real-time quantitative PCR, serial dilutions of the oligonucleotide amplicon of the gene in a range of 1 to 1 × 109 copies were used as corresponding standard. Standard curves were generated using the Ct values determined in the real-time PCR to permit gene quantification using the supplied software according to the manufacturer’s instructions. In addition, a standard curve was generated for the housekeeping gene, glyceraldehyde-3-phosphate-dehydrogenase (Applied Biosystems; part number 402869) to enable normalization of each gene. Data were expressed as relative copy number of transcripts after normalization.

Cell Lines and 5-Aza-CdR Treatment.

The human prostate cancer cell lines LNCaP, PC3 (American Type Culture Collection, Rockville, MD), and LAPC4 (a gift from Dr. Charles L. Sawyers, University of California Los Angeles, Los Angeles, CA) were grown in RPMI 1640 supplemented with 5% fetal bovine serum at 37°C and 5% CO2 until reaching approximately 50–70% confluence. Cells were then treated with 5% fetal bovine serum RPMI 1640 containing 6 μm 5-Aza-CdR (Sigma Chemicals Co., St. Louis, MO) for 6 days, with medium changes on days 1, 3, and 5. Total RNA was isolated from the cell lines, and the expression of the ZNF185 was analyzed by Taqman real-time PCR as described above. The housekeeping gene GAPDH was used as an internal control to enable normalization.

DNA Isolation and Bisulfite Modification.

Genomic DNA was obtained from metastatic, primary, matched benign prostatic tissues and the above mentioned prostate cancer cell lines treated with 5-Aza-CdR, using Wizard genomic DNA purification kit according to the manufacturer’s protocol (Promega, Madison, WI). Genomic DNA (100 ng) was modified by sodium bisulfite treatment by converting unmethylated, but not methylated, cytosines to uracil as described previously (14). DNA samples were then purified using the spin columns (Qiagen) and eluted in 50 μl of distilled water. Modification was completed by treatment with NaOH (0.3 m final concentration) for 5 min at room temperature, followed by ethanol precipitation. DNA was resuspended in water and used for PCR amplification.

MSP.

DNA methylation patterns within the gene were determined by chemical modification of unmethylated cytosine to uracil and subsequent PCR as described previously (15) using primers specific for either methylated or the modified unmethylated sequences. The primers used for MSP were shown in Fig. 3 B. Two sets of primers were designed corresponding to the genomic positions around 210 and 335. Genomic position indicates the location of the 5′ nucleotide of the sense primer in relation to the major transcriptional start site defined in the GenBank accession no. Y09538. The PCR mixture contained 1× PCR buffer [50 mm KCl and 10 mm Tris-HCl (pH 8.3) with 0.01% w/v gelatin], deoxynucleoside triphosphates (0.2 mm each), primers (500 μm), and bisulfite-modified or -unmodified DNA (100 ng) in a final volume of 25 μl. Reactions were hot-started at 95°C for 10 min with the addition of 1.25 units of AmpliTaq Gold DNA polymerase (Perkin-Elmer). Amplifications were carried out in GeneAmp PCR systems 9700 (Applied Biosystems) for 35 cycles (30 s at 95°C, 30 s at 55°C, and 30 s at 72°C), followed by a final 7-min extension at 72°C. Appropriate negative and positive controls were included in each PCR reaction. One μl of the PCR product was loaded directly onto DNA 500 lab chip and analyzed on Agilent 2100 Bioanalyzer (Agilent Technologies).

We monitored gene expression profiles of 28 prostate cancer tissues using oligonucleotide microarrays. To identify genes differentially expressed between cancer and benign tissues, we performed a gene-by-gene analysis of the difference in mean log expression between the two groups. Genes were ranked according to intersample variability (SD), and 1850 genes with the most variable expression across all of the samples were median-centered and normalized with respect to other genes in the samples and corresponding genes in the other samples. Genes and samples were subjected to hierarchical clustering essentially as described previously (16). Differential expression of genes in benign and malignant prostate tissues was estimated using an algorithm (13) based on equally weighted contributions from the difference of hybridization intensities (μTumor-μNormal) or (μNormal-μTumor), the quotient of hybridization intensities (μTumor/μNormal) or (μNormal/μTumor), and the result of an unpaired t test between expression levels in tumor and normal tissues. We narrowed down the selection criteria to genes that showed a fold change of >2.35 between normal and cancer samples and a P < 0.001 by Student’s t test. We present a cluster of 25 up-regulated and 25 down-regulated genes, which discriminated between normal and cancer tissues (Fig. 1).

In general we did not observe good correlation in expression of genes between Gleason grades with or without lymph node metastasis, which might be because of the limitation that the expression profiles were not generated from homogeneous cell population. Among the 25 down-regulated genes we identified (Fig. 1), PSP94, BPAG1, WFDC2, KRT5, KRT15, TAGLN, ZFP 36, and the genes encoding LIM domain proteins FLH1, FLH2, and ENIGMA are consistent with the expression profiles of the previous studies (2, 7, 17, 18, 19). Up-regulation of hepsin, AMACR, STEAP, FOLH1, RAP2A, and the unknown gene DKFZP564B167 are consistent with the data published previously of microarray analysis (2, 3, 4, 5, 6, 7, 18, 20, 21). In addition our data also confirm up-regulation of the cell cycle regulated genes CCNB1, CCNB2, MAD2L1, DEEPEST, and BUB1B, cell adhesion regulator MACMARCKS, and unclassified genes KIAA0186 and KIAA0906(5, 7, 17, 21).

We selected PSP94, ZNF185, BPAG1, and TGM4 from the 25 down-regulated genes, and Erg-2 and RhoGDI-β from the 25 up-regulated genes for additional validation by Taqman quantitative PCR. These genes were selected because of their moderate- to high-level expression in prostate cancer. In addition, their potential functions as mentioned below may be highly relevant to prostate cancer biology. Furthermore, except for PSP94, their role in prostate cancer biology has not been described. PSP94 has been shown to be down-regulated in prostate cancer (22) and is the most down-regulated gene in our microarray data.

To validate the expression profiles, Taqman quantitative PCR was performed in duplicate for each sample. The standard curve slope values for all of the genes ranged between −3.58 and −3.20, corresponding to PCR efficiency of >0.9. The Kruskal-Wallis global test was done with the real-time quantitative analysis for all of the genes. A significant decrease in the expression of ZNF185, BPAG1, and PSP94 mRNA levels was observed in metastatic versus organ confined and localized tumors compared with benign tissues (P < 0.0001; Fig. 2 B). Moreover, the Wilcoxon test was used to compare each tissue type to the adjacent benign tissues. ZNF185, BPAG1, and PSP94 showed Ps < 0.0019 in each group compared with benign tissues.

PSP94 is a highly prostate-specific gene encoding a major prostate secretory protein. Earlier studies reported that both the secretion and synthesis of PSP94 were reduced in prostate cancer tissues (22). PSP94 is involved in inhibition of tumor growth by apoptosis (23), and the down-regulation in prostate tumor tissues may be the survival mechanism for cancer cells. Our study suggests that PSP94 may play a role in prostate cancer progression. BPAG1 is a 230-kDa hemidesmosomal component involved in adherence of epithelial cells to the basement membrane. Previous studies have shown a loss of BPAG1 in invasive breast cancer cells (24). The down-regulation of BPAG1 in our study (>14-fold in metastatic tissues) could be an indicative of an invasive phenotype and might predict the potential of invasive cells to metastasize (25). Erg-2 is a proto-oncogene known to play an important role in the development of cancer (26). We observed that Erg-2 expression levels were increased in 16 of 32 (50%) cancer tissues when stringently compared with the highest level of Erg-2 in 12 adjacent benign tissues. The increase in mRNA levels of Erg-2 in at least half of the cancer tissues examined indicates a role of Erg-2 in prostate cancer. These results warrant additional studies of PSP94, BPAG1, and Erg-2 in prostate cancer. Furthermore, TGM4 is a prostate tissue specific transglutaminase (type IV) that has been implicated in apoptosis and cell growth (27). RhoGDI-β may be involved in cellular transformation (28). Our Taqman PCR study shows that TGM4 and RhoGDI-β levels were not changed significantly in most of the prostate cancer tissues (data not shown).

We focused on ZNF185 for additional study because it is a novel LIM domain gene (29), and may play a role in prostate cancer development and progression. LIM domain proteins are known to play an important role in regulation of cellular proliferation and differentiation (30, 31, 32, 33, 34). ZNF185 is located on chromosome Xq28, a chromosomal region of interest as a result of the >20 hereditary diseases mapped to this region. The LIM is a cysteine-rich motif that coordinately binds two zinc atoms and mediates protein-protein interactions. Heiss et al.(29) cloned a full-length ZNF185 cDNA and showed that the transcript is expressed in a very limited number of human tissues with most abundant expression in the prostate. Our observation is the first to identify a relationship of ZNF185 regulation and cancer. Here we report a significant down-regulation in the expression of ZNF185 gene in all of the prostate cancer tissues compared with benign prostatic tissues (Fig. 1; Fig. 2 B). The decrease in ZNF185 expression in prostate tumors indicates that ZNF185 may play an important role in the development and progression of prostate cancer.

To study the transcriptional silencing of ZNF185 in prostate cancer, we treated LAPC4, LNCaP, and PC3 prostate cancer cell lines with 5-Aza-CdR, an inhibitor of DNA methyl transferase DNMT1 (34). Treatment with 5-Aza-CdR showed an ∼2-fold increase in mRNA levels of ZNF185 (Fig. 3,A) indicating that the gene might be partially silenced by methylation. To confirm the transcriptional inactivation, MSP was carried out to assess the methylation status of cytosine residues in the 5′ CpG dinucleotides of genomic DNA in prostate tumors, adjacent benign tissues, and in prostate cell lines with or without treatment with 5-Aza-CdR. Cytosine methylations within CpG dinucleotides were observed in the prostate cancer tissues and cell lines with two sets of primers used for PCR (Fig. 3,C). A reduction of the methylated band and increase of the unmethylated band in cell lines with 5-Aza-CdR treatment is consistent with the restoration of ZNF185 mRNA levels after demethylation. (Fig. 3,A). In most of the tissues samples, DNA not treated with bisulfite (unmodified) failed to amplify with either set of methylated- or unmethylated-specific primers but readily amplified with primers specific for the sequence before modification, suggesting an almost complete bisulfite reaction. Methylation of ZNF185 was accompanied by amplification of the unmethylated reaction as well. The presence of the unmethylated ZNF185 DNA could indicate the presence of normal tissues in these nonmicrodissected samples. However, heterogeneity in the patterns of methylation in the tumor itself might also be present. Fisher’s unordered test for methylation difference in metastatic, confined tumors and benign tissues was highly significant (P < 0.0003). The incidence of methylation in cancer tissues is shown in Fig. 3 D. Methylation status and down-regulation in the mRNA expression is correlated with higher tumor grade and metastasis. These results suggest that methylation of CpG dinucleotides may be the major factor causing transcriptional inactivation of ZNF185 and repressing its expression in the prostate cancer tissues.

In summary we show that mRNA expression analysis with oligonucleotide microarrays identified a set of genes that characterize prostate cancer and benign prostatic tissues. We confirmed that a decrease in the expression of genes PSP94, BPAG1, and ZNF185 highly correlates with prostate cancer progression. Increase of Erg-2 levels may suggest its role in the development of prostate cancer. Importantly, this is the first study to identify inactivation of the LIM domain gene ZNF185 in patients with prostate cancer and in prostate cancer cell lines. This gene may serve as a marker of prostate cancer aggressiveness. In addition, our findings warrant additional investigations of potential transcriptional silencing of PSP94 and BPAG1 as prognostic markers for prostate cancer progression, and as potential therapeutic targets for prostate cancer.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

Supported in part by NIH Grants CA91956 and CA70892.

3

The abbreviations used are: PSP94, prostate secretory protein 94; ZNF185, zinc finger protein 185; BPAG1, bullous pemphigoid antigen gene 1; MSP, methylation-specific PCR; 5-Aza-CdR, 5-aza-2′-deoxycytidine.

Fig. 1.

Expression of 50 significantly regulated genes in 36 prostate tissue samples. Cluster diagram depicting genes that distinguish metastatic (Met; n = 5) from confined tumors with Gleason score 9 lymph node-positive (9P; n = 6) or -negative (9N; n = 6), and Gleason score 6 lymph node-positive (6P; n = 6) or -negative (6N; n = 5) prostate cancer and adjacent benign tissues (ABT; n = 8; n represents the number of tissues). Each row represents a gene and each column a tissue sample. Red and green represent up-regulation and down-regulation, respectively, relative to the median of the reference pool. Gray represents technically inadequate or missing data, and black represents equal expression relative to the reference samples. Color saturation is proportional to the magnitude of the difference from the mean. Each gene is labeled by its gene name. Mean and SD of the fold change in the expression levels of genes compared with ABT is shown.

Fig. 1.

Expression of 50 significantly regulated genes in 36 prostate tissue samples. Cluster diagram depicting genes that distinguish metastatic (Met; n = 5) from confined tumors with Gleason score 9 lymph node-positive (9P; n = 6) or -negative (9N; n = 6), and Gleason score 6 lymph node-positive (6P; n = 6) or -negative (6N; n = 5) prostate cancer and adjacent benign tissues (ABT; n = 8; n represents the number of tissues). Each row represents a gene and each column a tissue sample. Red and green represent up-regulation and down-regulation, respectively, relative to the median of the reference pool. Gray represents technically inadequate or missing data, and black represents equal expression relative to the reference samples. Color saturation is proportional to the magnitude of the difference from the mean. Each gene is labeled by its gene name. Mean and SD of the fold change in the expression levels of genes compared with ABT is shown.

Close modal
Fig. 2.

A, forward primer (FP), reverse primer (RP), and probes used for Taqman real-time PCR. B, expression levels of genes ZNF185, PSP94, BPAG1, and Erg-2 as validated by Taqman real-time PCR in 36 samples (28 cancer and 8 benign) used for microarray analysis and an additional 8 samples (4 cancer and 4 benign). Values are expressed as the copy number of the gene relative to glyceraldehyde-3-phosphate dehydrogenase levels. Metastatic tissues (Met ♦) n = 5, Gleason score 9, lymph node-positive (9P ▪) n = 7 or -negative (9N □) n = 8 and Gleason score 6, lymph node-positive (6P •) n = 6 or -negative tissues (6N ○) n = 6 and adjacent benign tissues (ABT ▴) n = 12 were used. (n represents the number of tissues). Mean ± SD of relative expression levels of each group is shown on the left.

Fig. 2.

A, forward primer (FP), reverse primer (RP), and probes used for Taqman real-time PCR. B, expression levels of genes ZNF185, PSP94, BPAG1, and Erg-2 as validated by Taqman real-time PCR in 36 samples (28 cancer and 8 benign) used for microarray analysis and an additional 8 samples (4 cancer and 4 benign). Values are expressed as the copy number of the gene relative to glyceraldehyde-3-phosphate dehydrogenase levels. Metastatic tissues (Met ♦) n = 5, Gleason score 9, lymph node-positive (9P ▪) n = 7 or -negative (9N □) n = 8 and Gleason score 6, lymph node-positive (6P •) n = 6 or -negative tissues (6N ○) n = 6 and adjacent benign tissues (ABT ▴) n = 12 were used. (n represents the number of tissues). Mean ± SD of relative expression levels of each group is shown on the left.

Close modal
Fig. 3.

A, expression of ZNF185 levels in prostate cancer cells treated with 6 μm 5-Aza-CdR for 6 days. Four separate experiments are represented; bars, ± SD. ∗, indicates statistical significance over the untreated cells (P < 0.05%); B, the PCR primers [forward primer (FP), reverse primer (RP)], used for MSP of prostate tissues. W represents unmodified or wild type primers. M, methylated-specific primers; and U, unmethylated-specific primers. Sequence difference between modified primers and unmodified DNA are in boldface type, and differences between methylated/modified and unmethylated/modified are underlined. C, MSP analysis of ZNF185 DNA in prostate tissue samples and cell lines with and without 5-Aza-CdR treatment. The amplified products were directly loaded onto DNA 500 lab chip and analyzed on Agilent 2100 Bioanalyzer. Molecular size marker is shown at left. All DNA samples were bisulfite-treated except those designated untreated. The experiments were repeated twice and the representative band of the PCR product in Lanes U, M, and W indicates the presence of unmethylated, methylated, and wild-type ZNF185 DNA, respectively. D, summary of the incidence of methylation of ZNF185 DNA in prostate tissues analyzed by MSP.

Fig. 3.

A, expression of ZNF185 levels in prostate cancer cells treated with 6 μm 5-Aza-CdR for 6 days. Four separate experiments are represented; bars, ± SD. ∗, indicates statistical significance over the untreated cells (P < 0.05%); B, the PCR primers [forward primer (FP), reverse primer (RP)], used for MSP of prostate tissues. W represents unmodified or wild type primers. M, methylated-specific primers; and U, unmethylated-specific primers. Sequence difference between modified primers and unmodified DNA are in boldface type, and differences between methylated/modified and unmethylated/modified are underlined. C, MSP analysis of ZNF185 DNA in prostate tissue samples and cell lines with and without 5-Aza-CdR treatment. The amplified products were directly loaded onto DNA 500 lab chip and analyzed on Agilent 2100 Bioanalyzer. Molecular size marker is shown at left. All DNA samples were bisulfite-treated except those designated untreated. The experiments were repeated twice and the representative band of the PCR product in Lanes U, M, and W indicates the presence of unmethylated, methylated, and wild-type ZNF185 DNA, respectively. D, summary of the incidence of methylation of ZNF185 DNA in prostate tissues analyzed by MSP.

Close modal
Table 1

Patient data

Gleason grade/lymph nodeSample IDAgePreop PSA (ng/ml)Tumor-Node-Metastasis (97)Metastatic site
6/Negative 6N 1 55 9.4 T2b,N0−  
 6N 2 50 7.5 T2b,N0−  
 6N 3 57 10.3 T2b,N0−  
 6N 4 67 16.7 T2b,N0−  
 6N 5 68 8.1 T2a,N0−  
6/Positive 6P 1 71 17.1 T2b,N1+  
 6P 2 61 5.2 T2b,N0+  
 6P 3 71 41.0 T2b,N0+  
 6P 4 65 7.0 T2a,N0+  
 6P 5 51 14.3 T2b,N0+  
 6P 6 66 23.5 T2b,N0+  
9/Negative 9N 1 67 21.6 T3a,N0−  
 9N 2 65 29.4 T3b,N0−  
 9N 3 65 24.9 T3b,N0−  
 9N 4 54 50.0 T3b,N0−  
 9N 5 59 25.8 T3b,N0−  
 9N 6 71 6.1 T3b,N0−  
9/Positive 9P 1 66 4.5 T3a,N0+  
 9P 2 65 6.69 T3b,N0+  
 9P 3 76 7.6 T3b,N1+  
 9P 4 71 467.0 T3b,N0+  
 9P 5 69 5.6 T3b,N0+  
 9P 6 66 2.9 T3b,N1−  
Metastatic Met 1 62 0.15  Liver 
 Met 2 72 97.3  Peritoneum 
 Met 3 49 0.15  Lymph node 
 Met 4 60 18.4  Lymph node 
 Met 5 68 8.9  Lung 
Gleason grade/lymph nodeSample IDAgePreop PSA (ng/ml)Tumor-Node-Metastasis (97)Metastatic site
6/Negative 6N 1 55 9.4 T2b,N0−  
 6N 2 50 7.5 T2b,N0−  
 6N 3 57 10.3 T2b,N0−  
 6N 4 67 16.7 T2b,N0−  
 6N 5 68 8.1 T2a,N0−  
6/Positive 6P 1 71 17.1 T2b,N1+  
 6P 2 61 5.2 T2b,N0+  
 6P 3 71 41.0 T2b,N0+  
 6P 4 65 7.0 T2a,N0+  
 6P 5 51 14.3 T2b,N0+  
 6P 6 66 23.5 T2b,N0+  
9/Negative 9N 1 67 21.6 T3a,N0−  
 9N 2 65 29.4 T3b,N0−  
 9N 3 65 24.9 T3b,N0−  
 9N 4 54 50.0 T3b,N0−  
 9N 5 59 25.8 T3b,N0−  
 9N 6 71 6.1 T3b,N0−  
9/Positive 9P 1 66 4.5 T3a,N0+  
 9P 2 65 6.69 T3b,N0+  
 9P 3 76 7.6 T3b,N1+  
 9P 4 71 467.0 T3b,N0+  
 9P 5 69 5.6 T3b,N0+  
 9P 6 66 2.9 T3b,N1−  
Metastatic Met 1 62 0.15  Liver 
 Met 2 72 97.3  Peritoneum 
 Met 3 49 0.15  Lymph node 
 Met 4 60 18.4  Lymph node 
 Met 5 68 8.9  Lung 

We thank Micheal E. Grossmann for critical reading of the article and helpful comments.

1
DeMarzo A. M., Nelson W. G., Isaacs W. B., Epstein J. I. Pathological and molecular aspects of prostate cancer.
Lancet
,
361
:
955
-964,  
2003
.
2
Dhanasekaran S. M., Barrette T. R., Ghosh D., Shah R., Varambally S., Kurachi K., Pienta K. J., Rubin M. A., Chinnaiyan A. M. Delineation of prognostic biomarkers in prostate cancer.
Nature (Lond.)
,
412
:
822
-826,  
2001
.
3
Luo J., Duggan D. J., Chen Y., Sauvageot J., Ewing C. M., Bittner M. L., Trent J. M., Isaacs W. B. Human prostate cancer and benign prostatic hyperplasia: Molecular dissection by gene expression profiling.
Cancer Res.
,
61
:
4683
-4688,  
2001
.
4
Magee J. A., Araki T., Patil S., Ehrig T., True L., Humphrey P. A., Catalona W. J., Watson M. A., Milbrandt J. Expression profiling reveals hepsin overexpression in prostate cancer.
Cancer Res.
,
61
:
5692
-5696,  
2001
.
5
Welsh J. B., Sapinoso L. M., Su A. I., Kern S. G., Wang-Rodriguez J., Moskaluk C. A., Frierson H. F., Jr., Hampton G. M. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer.
Cancer Res.
,
61
:
5974
-5978,  
2001
.
6
Rubin M. A., Zhou M., Dhanasekaran S. M., Varambally S., Barrette T. R., Sanda M. G., Pienta K. J., Ghiesh D., Chinnaiyan A. M. α-methyl coenzyme A racemase as a tissue biomarker for prostate cancer.
JAMA
,
287
:
1662
-1670,  
2002
.
7
Ernst T., Hergenhahn M., Kenzelmann M., Cohen C. D., Bonrouhi M., Weninger A., Klaren R., Grone E., Wiesel M., Gudemann C., Kuster J., Schott W., Staehler G., Kretzler M., Hollstein M., Grone H. J. Decrease and gain of gene expression are equally discriminatory markers for prostate carcinoma: a gene expression analysis on total and microdissected prostate tissue.
Am. J. Pathol.
,
160
:
2169
-2180,  
2002
.
8
Ehrlich M. DNA methylation in cancer: too much, but also too little.
Oncogene
,
21
:
5400
-5413,  
2002
.
9
Wong I. H. N., Lo Y. M. D., Zhang J. Detection of aberrant p16 methylation in the plasma and serum of liver cancer patients.
Cancer Res.
,
59
:
71
-73,  
1999
.
10
Jones P. A., Baylin S. B. The fundamental role of epigenetic events in cancer.
Nat. Rev. Genet.
,
3
:
415
-428,  
2002
.
11
Plass C. Cancer epigenomics.
Hum. Mol. Genet.
,
11
:
2479
-2488,  
2002
.
12
Wong I. H. N., Lo Y. M. D. New markers for cancer detection.
Cancer Prev.
,
4
:
471
-477,  
2002
.
13
Giordano T. J., Shedden K. A., Schwartz D. R., Kuick R., Taylor J. M., Lee N., Misek D. E., Greenson J. K., Kardia S. L., Beer D. G., Rennert G., Cho K. R., Gruber S. B., Fearon E. R., Hanash S. Organ-specific molecular classification of primary lung, and ovarian adenocarcinomas using gene expression profiles.
Am. J. Pathol.
,
159
:
1231
-1238,  
2001
.
14
Herman J. G., Graff J. R., Myohanen S., Nelkin B. D., Baylin S. B. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands.
Proc. Natl. Acad. Sci. USA
,
93
:
9821
-9826,  
1996
.
15
Esteller M., Corn P. G., Baylin S. B., Herman J. G. A gene hypermethylation profile of human cancer.
Cancer Res.
,
61
:
3225
-3229,  
2001
.
16
Eisen M. B., Spellman P. T., Brown P. O., Botstein D. Cluster analysis and display of genome-wide expression patterns.
Proc. Natl. Acad. Sci. USA
,
95
:
14863
-14868,  
1998
.
17
LaTulippe E., Satagopan J., Smith A., Scher H., Scardino P., Reuter V., Gerald W. L. Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease.
Cancer Res.
,
62
:
4499
-506,  
2002
.
18
Luo J. H., Yu Y. P., Cieply K., Lin F., Deflavia P., Dhir R., Finkelstein S., Michalopoulos G., Becich M. Gene expression analysis of prostate cancers.
Mol. Carcinog.
,
33
:
25
-35,  
2002
.
19
Shields J. M., Rogers-Graham K., Der C. J. Loss of transgelin in breast and colon tumors and in RIE-1 cells by Ras deregulation of gene expression through Raf-independent pathways.
J. Biol. Chem.
,
277
:
9790
-9799,  
2002
.
20
Rhodes D. R., Barrette T. R., Rubin M. A., Ghosh D., Chinnaiyan A. M. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.
Cancer Res.
,
62
:
4427
-4433,  
2002
.
21
Stamey T. A., Warrington J. A., Caldwell M. C., Chen Z., Fan Z., Mahadevappa M., McNeal J. E., Nolley R., Zhang Z. Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia.
J. Urol.
,
166
:
2171
-2177,  
2001
.
22
Sakai H., Tsurusaki T., Kanda S., Koji T., Xuan J. W., Saito Y. Prognostic significance of β-microseminoprotein mRNA expression in prostate cancer.
Prostate
,
38
:
278
-284,  
1999
.
23
Garde S. V., Basrur V. S., Finkelman M. A., Krishnan A., Wellham L., Ben-Josef E., Garde H. S. V., Basrur V. S., Finkelman M. A., Krishnan A., Wellham L., Ben-Josef E., Haddad M., Taylor J. D., Porter A. T., Tang D. G. Prostate secretory protein (PSP94) suppresses the growth of androgen-independent prostate cancer cell line (PC3) and xenografts by inducing apoptosis.
Prostate
,
38
:
118
-125,  
1999
.
24
Bergstraesser L. M., Srinivasan G., Jones J. C., Stahl S., Weitzman S. A. Expression of hemidesmosomes and component proteins is lost by invasive breast cancer cells.
Am. J. Pathol.
,
147
:
1823
-1839,  
1995
.
25
Herold-Mende C., Kartenbeck J., Tomakidi P., Bosch F. X. Metastatic growth of squamous cell carcinomas is correlated with upregulation and redistribution of hemidesmosomal components.
Cell Tissue Res.
,
306
:
399
-408,  
2001
.
26
Simpson S., Woodworth C. D., DiPaolo J. A. Altered expression of Erg and Ets-2 transcription factors is associated with genetic changes at 21q22.2–22.3 in immortal and cervical carcinoma cell lines.
Oncogene
,
14
:
2149
-2157,  
1997
.
27
Antonyak M. A., McNeill C. J., Wakshlag J. J., Boehm J. E., Cerione R. A. Activation of the Ras-ERK pathway inhibits retinoic acid-induced stimulation of tissue transglutaminase expression in NIH3T3 cells.
J. Biol. Chem.
,
278
:
15859
-15866,  
2003
.
28
Lozano E., Betson M., Braga V. M. Tumor progression: small GTPases and loss of cell-cell adhesion.
Bioessays
,
25
:
452
-463,  
2003
.
29
Heiss N. S., Gloeckner G., Bachner D., Kioschis P., Klauck S. M., Hinzmann B., Rosenthal A., Herman G. E., Poustka A. Genomic structure of a novel LIM domain gene (ZNF185) in Xq28 and comparisons with the orthologous murine transcripts.
Genomics
,
43
:
329
-338,  
1997
.
30
Bach I. The LIM domain: regulation by association.
Mech. Dev.
,
91
:
5
-17,  
2000
.
31
McLoughlin P., Ehler E., Carlile G., Licht J. D., Schafer B. W. The LIM-only protein DRAL/FHL2 interacts with and is a corepressor for the promyelocytic leukemia zinc finger protein.
J. Biol. Chem.
,
277
:
37045
-37053,  
2002
.
32
Mousses S., Bubendorf L., Wagner U., Hostetter G., Kononen J., Cornelison R., Goldberger N., Elkahloun A. G., Willi N., Koivisto P., Ferhle W., Raffeld M., Sauter G., Kallioniemi O. P. Clinical validation of candidate genes associated with prostate cancer progression in the CWR22 model system using tissue microarrays.
Cancer Res.
,
62
:
1256
-1260,  
2002
.
33
Yamada Y., Pannell R., Forster A., Rabbitts T. H. The LIM-domain protein Lmo2 is a key regulator of tumor angiogenesis: a new anti-angiogenesis drug target.
Oncogene
,
21
:
1309
-1315,  
2002
.
34
Robert M. F., Morin S., Beaulieu N., Gauthier F., Chute I. C., Barsalou A., MacLeod A. R. DNMT1 is required to maintain CpG methylation and aberrant gene silencing in human cancer cells.
Nat. Genet.
,
33
:
61
-65,  
2003
.