FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022052582> ?p ?o ?g. }

• W2022052582 endingPage "2036" @default.
• W2022052582 startingPage "2028" @default.
• W2022052582 abstract "Altered genome methylation is a hallmark of human malignancies. In this study, high-throughput analyses of concordant gene methylation and expression events were performed for 91 human prostate specimens, including prostate tumor (T), matched normal adjacent to tumor (AT), and organ donor (OD). Methylated DNA in genomic DNA was immunoprecipitated with anti-methylcytidine antibodies and detected by Affymetrix human whole genome SNP 6.0 chips. Among the methylated CpG islands, 11,481 islands were found located in the promoter and exon 1 regions of 9295 genes. Genes (7641) were methylated frequently across OD, AT, and T samples, whereas 239 genes were differentially methylated in only T and 785 genes in both AT and T but not OD. Genes with promoter methylation and concordantly suppressed expression were identified. Pathway analysis suggested that many of the methylated genes in T and AT are involved in cell growth and mitogenesis. Classification analysis of the differentially methylated genes in T or OD produced a specificity of 89.4% and a sensitivity of 85.7%. The T and AT groups, however, were only slightly separated by the prediction analysis, indicating a strong field effect. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. These results suggest methylation patterns useful in predicting clinical outcomes of prostate cancer. Altered genome methylation is a hallmark of human malignancies. In this study, high-throughput analyses of concordant gene methylation and expression events were performed for 91 human prostate specimens, including prostate tumor (T), matched normal adjacent to tumor (AT), and organ donor (OD). Methylated DNA in genomic DNA was immunoprecipitated with anti-methylcytidine antibodies and detected by Affymetrix human whole genome SNP 6.0 chips. Among the methylated CpG islands, 11,481 islands were found located in the promoter and exon 1 regions of 9295 genes. Genes (7641) were methylated frequently across OD, AT, and T samples, whereas 239 genes were differentially methylated in only T and 785 genes in both AT and T but not OD. Genes with promoter methylation and concordantly suppressed expression were identified. Pathway analysis suggested that many of the methylated genes in T and AT are involved in cell growth and mitogenesis. Classification analysis of the differentially methylated genes in T or OD produced a specificity of 89.4% and a sensitivity of 85.7%. The T and AT groups, however, were only slightly separated by the prediction analysis, indicating a strong field effect. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. These results suggest methylation patterns useful in predicting clinical outcomes of prostate cancer. Prostate cancer is one of the most prevalent malignancies among American men, with approximately 280,000 new cases diagnosed annually. Each year up to 28,050 patients with prostate cancer die in the United States alone, and mortality from prostate cancer is only second to lung carcinoma in the United States.1Siegel R. Naishadham D. Jemal A. Cancer statistics, 2012.CA Cancer J Clin. 2012; 62: 10-29Crossref PubMed Scopus (10476) Google Scholar Although most prostate cancers are indolent and responsive to the available hormone therapies, a significant number of cases become hormone refractory and metastatic. The precise cause of prostate cancer progression has remained elusive, despite extensive research efforts and recent advances in our understanding of this disease. Comprehensive gene expression and genome analyses have suggested that a global pattern of gene expression and copy number alterations exist for prostate cancer.2Yu Y.P. Landsittel D. Jing L. Nelson J. Ren B. Liu L. McDonald C. Thomas R. Dhir R. Finkelstein S. Michalopoulos G. Becich M. Luo J.H. Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy.J Clin Oncol. 2004; 22: 2790-2799Crossref PubMed Scopus (594) Google Scholar, 3Yu Y.P. Song C. Tseng G. Ren B.G. Laframboise W. Michalopoulos G. Nelson J. Luo J.H. Genome abnormalities precede prostate cancer and predict clinical relapse.Am J Pathol. 2012; 180: 2240-2248Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 4Luo 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. 2002; 33: 25-35Crossref PubMed Scopus (210) Google Scholar The related gene products include critical molecules in signaling pathways, DNA replication, cell growth, cell cycle checkpoints, and apoptosis.5Magee 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. 2001; 61: 5692-5696PubMed Google Scholar, 6Luo J.H. Gene expression alterations in human prostate cancer.Drugs Today (Barc). 2002; 38: 713-719Crossref PubMed Scopus (16) Google Scholar, 7Ernst T. Hergenhahn M. Kenzelmann M. Cohen C.D. Ikinger U. Kretzler M. Hollstein M. Grone H.J. [Gene expression profiling in prostatic cancer].Verh Dtsch Ges Pathol. 2002; 86 (German): 165-175PubMed Google Scholar, 8Glinsky G.V. Glinskii A.B. Stephenson A.J. Hoffman R.M. Gerald W.L. Gene expression profiling predicts clinical outcome of prostate cancer.J Clin Invest. 2004; 113: 913-923Crossref PubMed Scopus (398) Google Scholar Hypermethylation of the gene promoter is a well-known epigenetic event that silences gene expression and is a critical regulatory component in normal physiology, mediating gene imprinting for inactivation of the X chromosome9Monk M. Grant M. Preferential X-chromosome inactivation, DNA methylation and imprinting.Dev Suppl. 1990; : 55-62PubMed Google Scholar and tissue-specific gene expression,10Hu J.F. Vu T.H. Hoffman A.R. Promoter-specific modulation of insulin-like growth factor II genomic imprinting by inhibitors of DNA methylation.J Biol Chem. 1996; 271: 18253-18262Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar and in pathological processes, mediating inactivation of tumor suppressor genes and promoting tumorigenesis.11Yu G. Tseng G.C. Yu Y.P. Gavel T. Nelson J. Wells A. Michalopoulos G. Kokkinakis D. Luo J.H. CSR1 suppresses tumor growth and metastasis of prostate cancer.Am J Pathol. 2006; 168: 597-607Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar, 12Yu Y.P. Yu G. Tseng G. Cieply K. Nelson J. Defrances M. Zarnegar R. Michalopoulos G. Luo J.H. Glutathione peroxidase 3, deleted or methylated in prostate cancer, suppresses prostate cancer growth and metastasis.Cancer Res. 2007; 67: 8043-8050Crossref PubMed Scopus (180) Google Scholar Addition of a methyl group to the cytosine residue of CpG dinucleotides by methyltransferase creates a binding motif for methyl-cytosine binding proteins, methyl-CpG-binding domain (MBD) or methyl-CpG-binding protein 2 (MeCP2), which in turn produces steric hindrance at the CpG clusters located in the promoter regions to transcriptional activators or repressors.13Lund A.H. van Lohuizen M. Epigenetics and cancer.Genes Dev. 2004; 18: 2315-2335Crossref PubMed Scopus (423) Google Scholar, 14Bird A. DNA methylation patterns and epigenetic memory.Genes Dev. 2002; 16: 6-21Crossref PubMed Scopus (5486) Google Scholar Silencing of genes involved in cell cycle control, cell survival, DNA damage repair, and signal transduction is the characteristics of cancer cells.15Graff J.R. Herman J.G. Lapidus R.G. Chopra H. Xu R. Jarrard D.F. Isaacs W.B. Pitha P.M. Davidson N.E. Baylin S.B. E-cadherin expression is silenced by DNA hypermethylation in human breast and prostate carcinomas.Cancer Res. 1995; 55: 5195-5199PubMed Google Scholar, 16Herman J.G. Merlo A. Mao L. Lapidus R.G. Issa J.P. Davidson N.E. Sidransky D. Baylin S.B. Inactivation of the CDKN2/p16/MTS1 gene is frequently associated with aberrant DNA methylation in all common human cancers.Cancer Res. 1995; 55: 4525-4530PubMed Google Scholar, 17Kito H. Suzuki H. Ichikawa T. Sekita N. Kamiya N. Akakura K. Igarashi T. Nakayama T. Watanabe M. Harigaya K. Ito H. Hypermethylation of the CD44 gene is associated with progression and metastasis of human prostate cancer.Prostate. 2001; 49: 110-115Crossref PubMed Scopus (68) Google Scholar, 18Vanaja D.K. Cheville J.C. Iturria S.J. Young C.Y. Transcriptional silencing of zinc finger protein 185 identified by expression profiling is associated with prostate cancer progression.Cancer Res. 2003; 63: 3877-3882PubMed Google Scholar, 19Woodson K. Hayes R. Wideroff L. Villaruz L. Tangrea J. Hypermethylation of GSTP1, CD44, and E-cadherin genes in prostate cancer among US Blacks and Whites.Prostate. 2003; 55: 199-205Crossref PubMed Scopus (111) Google Scholar, 20Yoshiura K. Kanai Y. Ochiai A. Shimoyama Y. Sugimura T. Hirohashi S. Silencing of the E-cadherin invasion-suppressor gene by CpG methylation in human carcinomas.Proc Natl Acad Sci U S A. 1995; 92: 7416-7419Crossref PubMed Scopus (596) Google Scholar, 21Bastian P.J. Yegnasubramanian S. Palapattu G.S. Rogers C.G. Lin X. De Marzo A.M. Nelson W.G. Molecular biomarker in prostate cancer: the role of CpG island hypermethylation.Eur Urol. 2004; 46: 698-708Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar, 22Nakayama M. Gonzalgo M.L. Yegnasubramanian S. Lin X. De Marzo A.M. Nelson W.G. GSTP1 CpG island hypermethylation as a molecular biomarker for prostate cancer.J Cell Biochem. 2004; 91: 540-552Crossref PubMed Scopus (170) Google Scholar, 23Kang G.H. Lee S. Lee H.J. Hwang K.S. Aberrant CpG island hypermethylation of multiple genes in prostate cancer and prostatic intraepithelial neoplasia.J Pathol. 2004; 202: 233-240Crossref PubMed Scopus (206) Google Scholar There is a lack of global correlation of CpG island methylation and gene expression in prostate cancer. To map the epigenetic regulation leading to altered expression of hundreds of genes in prostate cancer, we performed a genome-wide concordance analysis of gene methylation and expression in matched prostate tumor (T), benign prostate tissues adjacent to cancer (AT), and organ donor prostate without history of urological disease or any malignancy (OD). We found unique methylation profiles that distinguished the T, AT, and OD samples. Ninety-one specimens of prostate cancer and adjacent benign prostate tissues and organ donor prostates were obtained from University of Pittsburgh Tissue Bank in compliance with institutional regulatory guidelines (Supplemental Table S1). To ensure high purity (≥80%) of tumor cells, needle-microdissection was performed by pathologists to isolate the tumor cells from adjacent normal tissues (≥3-mm distance from the tumor). For AT and OD samples, similar needle-microdissections were performed to achieve 80% epithelial purity. Genomic DNA of these T and AT tissues was extracted with a commercially available tissue and blood DNA extraction kit (Qiagen, Hilden, Germany). The protocols of tissue procurement and procedure were approved by Institution Board of Review of the University of Pittsburgh. Each DNA sample was divided into two aliquots of 250 ng and was digested with either StyI or Nsp1 (New England BioLabs, Ipswich, MA) at 37°C for 2 hours, followed by ligation to the corresponding StyI or Nsp1 adapters (Affymetrix, Santa Clara, CA) at 16°C for 16 hours. The two adapter-ligated DNAs were pooled and purified with an Amicon Ultra-centrifugation filter (Millipore, Billerica, MA). Fifty nanograms of the purified DNA in 450 μL of Tris-EDTA buffer were denatured by boiling in water for 10 minutes. Methylated DNA was then immunoprecipitated with 5 μg of anti–5-methylcytosine antibody (Zymo Research, Irvine, CA) in immunoprecipitation buffer (10 mmol/L NaPO4, 140 mmol/L NaCl, and 0.05% Triton X-100, pH 7) by rocking at 4°C for 2 hours. The immunocomplexes were captured with magnetic bead-protein A/G (Millipore) by shaking at room temperature for 30 minutes. Three washes were performed with 1 mL of immunoprecipitation buffer, and additional washes with 300 μL of elution buffer heated to 50°C for 10 minutes were performed until the DNA in flow-through reached zero. Methylated DNA was then eluted from the beads with 100 μL of elution buffer heated to 75°C for 5 minutes. The eluted DNA was PCR amplified with titanium DNA polymerase and primer 002 from Affymetrix SNP 6.0 reagent kit, using 30 thermal cycles of 94°C for 30 seconds, 60°C for 45 seconds, and 65°C for 60 seconds. Amplification efficiency was assessed by resolving amplicons with 1% agarose gel electrophoresis. Samples were then purified and fragmented by incubating with DNase1 at 37°C for 35 minutes. The fragmented DNA samples (range, 100–200 bp) were biotin labeled with terminal transferase at 37°C for 4 hours and hybridized to Affymetrix human whole genome SNP 6.0 chips at 50°C for 19 hours. After washes with 6× SSPE (saline, sodium phosphate, EDTA) buffer and staining with phycoerythrin streptoavidin in an automated Affymetrix fluid station, the chips were scanned by Affymetrix GeneChip scanner 3000 7G. To determine the baseline copy number of each sample, genome DNA of each sample was analyzed with the Affymetrix SNP 6.0 chip. Briefly, the adaptor ligated DNA was prepared from StyI and Nsp1 digestion as described in Immunoprecipitation and Amplification of Methylated DNA. The efficiency of amplification was verified by resolving amplicons with 1% agarose gel electrophoresis. The total amount of purified amplicons was in the range of 200 to 250 μg.3Yu Y.P. Song C. Tseng G. Ren B.G. Laframboise W. Michalopoulos G. Nelson J. Luo J.H. Genome abnormalities precede prostate cancer and predict clinical relapse.Am J Pathol. 2012; 180: 2240-2248Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 24Nalesnik M.A. Tseng G. Ding Y. Xiang G.S. Zheng Z.L. Yu Y. Marsh J.W. Michalopoulos G.K. Luo J.H. Gene deletions and amplifications in human hepatocellular carcinomas: correlation with hepatocyte growth regulation.Am J Pathol. 2012; 180: 1495-1508Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar As described in the section above, the amplicons were DNAse1 fragmented, biotin labeled, hybridized to the Affymetrix SNP 6.0 chips, and processed for genome copy number analysis. The hybridization signals of methylation-enriched DNA from 91 prostate tissues were analyzed by Partek Genomics Suite 6.6 (Partek, Inc., St. Louis, MO). Pair-wise copy number analyses of methylation-enriched DNA were performed with genome copy numbers of the unenriched DNA samples as baselines with criteria of marker numbers >10 and segment length >2000 bp. The segments that were detected as amplified or unchanged in comparison with the baseline copy number of the duplicate samples were considered to be enriched by the methylation-specific antibodies. These methylated fragments were then screened for CpG islands. Criteria of 50% C and G and expected CpG of 0.65 in a region of >200 bp was used to define a CpG island.25Jones P.A. Takai D. The role of DNA methylation in mammalian epigenetics.Science. 2001; 293: 1068-1070Crossref PubMed Scopus (1560) Google Scholar, 26Takai D. Jones P.A. The CpG island searcher: a new WWW resource.In Silico Biol. 2003; 3: 235-240PubMed Google Scholar, 27Takai D. Jones P.A. Comprehensive analysis of CpG islands in human chromosomes 21 and 22.Proc Natl Acad Sci U S A. 2002; 99: 3740-3745Crossref PubMed Scopus (1174) Google Scholar The CpG islands located in the region of 1000 bp upstream and 500 bp downstream of mRNA start site of a gene were designated as gene-associated CpG islands and were annotated through the UCSC (University of California Santa Cruz) genome build hg18. Microarray data location of Raw *.cel files of data will be available at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo; accession number GSE45000). The differential profiles of methylated genes were determined by comparing the CpG islands of T/OD, AT/OD, and T/AT, respectively. For each given gene-associated CpG island, the fraction of samples that were methylated was tabulated for each group. A two-by-two contingency table was constructed and Fisher exact test was performed to detect the differential methylation of CpG islands. To examine whether the differential methylations of these gene-associated CpG islands were functional, the previously published U95 Affymetrix chip gene expression data of 130 cases of prostate cancer and donors (overlapping 75 of 91 samples of our present methylation analyses or 82%) were assessed.4Luo 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. 2002; 33: 25-35Crossref PubMed Scopus (210) Google Scholar The expression of each methylated gene was measured by calculating Pearson correlation coefficient between gene expression values4Luo 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. 2002; 33: 25-35Crossref PubMed Scopus (210) Google Scholar, 7Ernst T. Hergenhahn M. Kenzelmann M. Cohen C.D. Ikinger U. Kretzler M. Hollstein M. Grone H.J. [Gene expression profiling in prostatic cancer].Verh Dtsch Ges Pathol. 2002; 86 (German): 165-175PubMed Google Scholar and methylation state vector (0,1) for each matched sample (33). The differential gene expression analysis that used a one-sided t-test was performed. A functional methylated gene was defined as a gene that was detected as hypermethylated in the analysis and was shown to have down-regulated gene expression. The difference in methylation status of these functional methylated genes between the compared groups was named as differential functional methylation events in the analysis. Significant differences in methylation and expression were indicated by a P value <0.05. Six sets of concordant genes were identified, including the hypermethylated and hypomethylated gene sets in T/D, AT/D, and T/AT groups. Pathway analysis was performed for the six sets of concordant genes. A total of 2287 pathways were curated from MSigDB,28Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (27801) Google Scholar which contains information from Biocarta, Kyoto Encyclopedia of Genes and Genomes, Reactome, and Gene Ontology databases. The one-sided Fisher exact test (overrepresentation) was applied to calculate the statistical significance of pathway enrichment, and P values were corrected by the Benjamini-Hochberg procedure for multiple comparisons to calculate q values. Prediction (classification) analyses were performed for the four comparison groups: i) T versus OD, ii) AT versus OD, iii) T relapse versus T nonrelapse, and iv) T fast relapse versus slow relapse or nonrelapse. The conventional leave-one-out cross validation (LOOCV) approach was used to assess accuracy.29Golub T.R. Slonim D.K. Tamayo P. Huard C. Gaasenbeek M. Mesirov J.P. Coller H. Loh M.L. Downing J.R. Caligiuri M.A. Bloomfield C.D. Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.Science. 1999; 286: 531-537Crossref PubMed Scopus (9264) Google Scholar In LOOCV, one sample was left out for evaluation, and the remaining samples were used to construct a prediction model, which was then applied to the left-out sample evaluation for accuracy of prediction. The process was repeated until all of the samples were left out as test samples and the sensitivity and the specificity of the model for prediction of cancer or normal were achieved. The differentially methylated CpG islands were then applied as predictive features in the prediction model, and the top (parameter r) differentially methylated CpG islands (having equal number of differentially hypermethylation and hypomethylation) were selected for their significance in group comparisons of hypermethylation and hypomethylation with the use of Fisher exact test. To predict the left-out test sample, the percentage of concordance (parameter t) was calculated as a threshold for the methylation pattern in the training set with the prediction model in the top (r) differentially hypermethylated and hypomethylated CpG islands. Parameter (t) was used to balance the sensitivity and specificity trade-off in the prediction model. By varying p, a receiver operating characteristic (ROC) curve for classification was produced. In this analysis, the (r) that produced the best area under the curve (AUC) was selected, and the threshold p was determined by maximizing the Youden index (sensitivity + specificity − 1) for the best sensitivity and specificity trade-off and overall accuracy rate. The criterion used gave equal significance in sensitivity and specificity. To evaluate whether the prediction result was better than that obtained by a random approach, AUC was used as a test. Permutation analyses were performed to assess the statistical significance by random shuffling of the class labels (case and control) with new AUC values being calculated; this procedure was repeated 100 times to generate the null distribution. The P value was calculated as the percentage of 100 null AUCs from permutation and was greater than the observed AUC. The genomic DNA from the same group of prostate tissue samples was treated with sodium bisulfite (Epitec bisulfite kit; Qiagen) at 95°C for 5 minutes, followed by 60°C for 4 hours, and desulfonated as described in the manufacturer’s manual. The bisulfite-converted DNA was amplified as previously described,30Kobatake T. Yano M. Toyooka S. Tsukuda K. Dote H. Kikuchi T. Toyota M. Ouchida M. Aoe M. Date H. Pass H.I. Doihara H. Shimizu N. Aberrant methylation of p57KIP2 gene in lung and breast cancers and malignant mesotheliomas.Oncol Rep. 2004; 12: 1087-1092PubMed Google Scholar, 31Shin J.Y. Kim H.S. Park J. Park J.B. Lee J.Y. Mechanism for inactivation of the KIP family cyclin-dependent kinase inhibitor genes in gastric cancer cells.Cancer Res. 2000; 60: 262-265PubMed Google Scholar using CDKN1c methylation-specific primers (5′-CGCGGTCGTTAATTAGTCGC-3′/5′-ACACAACGCACTTAACCTATAA-3′) or CDKN1c unmethylation-specific primers (5′-TTTGTTTTGTGGTTGTTAATTAGTTGT-3′/5′-ACACAACACACTTAACCTATAA-3′) with the following thermal cycling conditions: 40 cycles of 95°C for 30 seconds and 61°C for 1 minute, followed by 72°C for 2 minutes for methylation primers; or 40 cycles of 95°C for 30 seconds and 60°C for 1 minute, followed by 72°C for 2 minutes for unmethylation primers. To investigate the global DNA methylation patterns of prostate cancer, methylated DNA segments from the prostate genome were analyzed by detecting methylation-enriched DNA with Affymetrix human whole genome SNP 6.0. The methylated genome DNA samples were immunoprecipitated by antibodies specific for 5-methylcytidine, amplified, and hybridized to the SNP 6.0 chip. The methylation genome DNA profile of each sample was then analyzed with the whole genome DNA profile from the same sample as baseline. Regions of DNA were deemed methylated if they were detected as amplified or unchanged in comparison with the matched whole genome profile. A total of 34,413 genome segments were identified as methylated regions. To study whether all these genome segments were relevant to the regulation of gene expression, we screened each for the presence of gene-associated CpG islands. CpG islands located within 1000 bp upstream and 500 bp downstream of the mRNA start site of a gene were considered potentially functional and were included. Based on these criteria, 11,481 potentially functional CpG islands were identified along with a total of 9136 genes. Among the gene-associated CpG islands, 9082 were found to be methylated in T, 8458 in AT, and 8093 in OD samples. To investigate the differential methylation status of prostate cancer-related CpG islands, the data were classified into groups of T (n = 47), AT (n = 30), and OD (n = 14) and compared in pairs for each CpG island identified. The proportion of each group’s samples that showed methylation in each gene-associated CpG island assessed was determined, and the distributions of these CpG islands in different chromosomes are shown in Figure 1A. By comparing three group pairs (T/OD + AT, AT/T + OD, and OD/T + AT), the differentially methylated CpG islands (P < 0.01) in T were identified in comparison with OD or AT (Figure 1A). Similarly, the genes associated with CpG islands that were differentially methylated in AT or OD were identified (Figure 1A). The number of genes that had at least one associated CpG island and were found to be methylated in at least 20% of samples in each patient group is shown in a Venn diagram (Figure 1B). Many of these methylated segments were not unique and overlapped with at least one of the other groups. A total of 7641 genes were methylated in all three groups (T, AT, and OD). Genes (785) were methylated in the T and AT groups but not in the OD group. Only 239 genes were methylated in the T group only, compared with the 19 and 22 genes that were uniquely methylated in the AT and OD groups, respectively. The differentially methylated genes between the T and OD groups mainly clustered in regions of chromosomes 1, 3, 15, and 17 (Figure 2). Interestingly, the T and AT groups shared many of the same differentially methylated genes, which were not methylated in the OD group. Few genes were detected with differential methylation between the T group and the AT group. To rule out the effect of age on methylation pattern, samples from patients and organ donors of age 50 years were analyzed in an age-matched manner. The results also suggested a significant field effect: 110 CpG islands were found uniquely methylated in the T group, whereas only 27 were in the AT group and 25 in the OD group. Greater than 79% (423 of 533) of CpG islands differentially methylated in T group (versus OD) were also methylated in AT samples.Figure 2Ideograms of differential methylations between T, AT, and OD; T versus OD, AT versus OD, and T versus AT. Upper panel: Differential methylation in T (blue) and OD (red). Middle panel: Differential methylation in AT (blue) and OD (red). Lower panel: Differential methylation in T (blue) and AT (red).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Gene methylation generally has a negative effect on gene expression. However, such effect can vary according to the characteristic of an associated CpG island, the level or position of the cytosine methylation, and other structural features. To investigate whether methylation of gene-associated CpG islands affected the corresponding gene expression, we performed a concordance analysis on differentially methylated genes with the use of gene expression data available from our previously published Affymetrix U95 expression data set. These data overlapped 75 of 91 samples of the present methylation analysis. Thus, the concordance analysis largely reflected a direct methylation effect on expression. Differentially methylated genes with down-regulation of gene expression (P < 0.01) were designated as functional methylation. Genes with methylation but having no suppression of gene expression were considered as nonfunctional methylation. Our analyses showed that 12.5% of the differentially methylated genes are functional (Figure 3, A–C). The highest frequency of functional differential methylation occurred in the T group (n = 47), with the lowest occurring in the OD group (n = 14) (Figure 3A). In particular, 95 of 650 genes were functionally methylated in the T group, whereas only 7 of 35 were functionally methylated in the AT group. When the AT group was compared with the OD group, 23 of 315 genes were classified as differentially methylated with suppressed gene expression being detected in either the AT or OD group. Only five functional hypermethylation and one functional hypomethylation events were found when the AT group was compared with the T group. These results suggest a significant similarity between the T and AT groups in their methylation patterns. To understand the significance of these differential DNA methylation events, pathway analyses were performed on the most significantly differentially hypermethylated and hypomethylated genes of each group (as identified by the Fisher exact test of independence, P < 0.01). The most frequently identified pathways corresponding to the DNA methylation and associated genes are shown in Supplemental Tables S2 and S3. Many of the hypermethylated genes in T (T/OD) were involved in inhibition of cell proliferation or cell division, such as cyclin-dependent kinase inhibitor 1C (CDKN1C) and CDKN2D, whereas the hypomethylated genes in T were involved in membrane transport. No significant pathway enrichment was identified when T was compared with AT. In AT versus OD, an apoptosis pathway was suppressed in the AT group [according to hypermethylation of some critical apoptotic genes, such as BCL2-associated X protein (BAX) and proteasome (prosome, macropain) 26S unit, ATPase, 4 (PSMC4)], whereas the membrane transport molecules were up-regulated by hypomethylation (Supplemental Table S3), although with relatively weaker statistical significance. To investigate whether the differentially methylated genes in prostate samples are predictiv" @default.
• W2022052582 created "2016-06-24" @default.
• W2022052582 creator A5016331956 @default.
• W2022052582 creator A5023821329 @default.
• W2022052582 creator A5037387251 @default.
• W2022052582 creator A5050299013 @default.
• W2022052582 creator A5053453125 @default.
• W2022052582 creator A5069300507 @default.
• W2022052582 creator A5072424476 @default.
• W2022052582 date "2013-06-01" @default.
• W2022052582 modified "2023-09-26" @default.
• W2022052582 title "Genome-Wide Methylation Analysis of Prostate Tissues Reveals Global Methylation Patterns of Prostate Cancer" @default.
• W2022052582 cites W1570023893 @default.
• W2022052582 cites W1602721903 @default.
• W2022052582 cites W19153779 @default.
• W2022052582 cites W1967406448 @default.
• W2022052582 cites W1967735720 @default.
• W2022052582 cites W1971804295 @default.
• W2022052582 cites W1975530512 @default.
• W2022052582 cites W1977422169 @default.
• W2022052582 cites W1977583598 @default.
• W2022052582 cites W1992936191 @default.
• W2022052582 cites W1993947858 @default.
• W2022052582 cites W1996072637 @default.
• W2022052582 cites W1996695360 @default.
• W2022052582 cites W2003526079 @default.
• W2022052582 cites W2004193381 @default.
• W2022052582 cites W2019409311 @default.
• W2022052582 cites W2031929061 @default.
• W2022052582 cites W2036902495 @default.
• W2022052582 cites W2040149948 @default.
• W2022052582 cites W2064159164 @default.
• W2022052582 cites W2069397160 @default.
• W2022052582 cites W2070136352 @default.
• W2022052582 cites W2070166913 @default.
• W2022052582 cites W2078028724 @default.
• W2022052582 cites W2079700566 @default.
• W2022052582 cites W2100703820 @default.
• W2022052582 cites W2104096208 @default.
• W2022052582 cites W2105123252 @default.
• W2022052582 cites W2106563560 @default.
• W2022052582 cites W2108426942 @default.
• W2022052582 cites W2109363337 @default.
• W2022052582 cites W2114511168 @default.
• W2022052582 cites W2114660910 @default.
• W2022052582 cites W2114884175 @default.
• W2022052582 cites W2121417064 @default.
• W2022052582 cites W2130410032 @default.
• W2022052582 cites W2130742313 @default.
• W2022052582 cites W2135094627 @default.
• W2022052582 cites W2136492640 @default.
• W2022052582 cites W2151903938 @default.
• W2022052582 cites W2154432559 @default.
• W2022052582 cites W2159406970 @default.
• W2022052582 cites W2164218164 @default.
• W2022052582 cites W2165948908 @default.
• W2022052582 cites W2403309454 @default.
• W2022052582 cites W2408089991 @default.
• W2022052582 doi "https://doi.org/10.1016/j.ajpath.2013.02.040" @default.
• W2022052582 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3668028" @default.
• W2022052582 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23583283" @default.
• W2022052582 hasPublicationYear "2013" @default.
• W2022052582 type Work @default.
• W2022052582 sameAs 2022052582 @default.
• W2022052582 citedByCount "39" @default.
• W2022052582 countsByYear W20220525822013 @default.
• W2022052582 countsByYear W20220525822014 @default.
• W2022052582 countsByYear W20220525822015 @default.
• W2022052582 countsByYear W20220525822016 @default.
• W2022052582 countsByYear W20220525822017 @default.
• W2022052582 countsByYear W20220525822018 @default.
• W2022052582 countsByYear W20220525822019 @default.
• W2022052582 countsByYear W20220525822020 @default.
• W2022052582 countsByYear W20220525822021 @default.
• W2022052582 countsByYear W20220525822022 @default.
• W2022052582 countsByYear W20220525822023 @default.
• W2022052582 crossrefType "journal-article" @default.
• W2022052582 hasAuthorship W2022052582A5016331956 @default.
• W2022052582 hasAuthorship W2022052582A5023821329 @default.
• W2022052582 hasAuthorship W2022052582A5037387251 @default.
• W2022052582 hasAuthorship W2022052582A5050299013 @default.
• W2022052582 hasAuthorship W2022052582A5053453125 @default.
• W2022052582 hasAuthorship W2022052582A5069300507 @default.
• W2022052582 hasAuthorship W2022052582A5072424476 @default.
• W2022052582 hasBestOaLocation W20220525821 @default.
• W2022052582 hasConcept C104317684 @default.
• W2022052582 hasConcept C121608353 @default.
• W2022052582 hasConcept C141231307 @default.
• W2022052582 hasConcept C142724271 @default.
• W2022052582 hasConcept C150194340 @default.
• W2022052582 hasConcept C190727270 @default.
• W2022052582 hasConcept C2776235491 @default.
• W2022052582 hasConcept C2780192828 @default.
• W2022052582 hasConcept C33288867 @default.
• W2022052582 hasConcept C54355233 @default.
• W2022052582 hasConcept C60644358 @default.
• W2022052582 hasConcept C70721500 @default.
• W2022052582 hasConcept C71924100 @default.

• next