Identification of novel tumor markers in prostate, colon and breast cancer by unbiased methylation profiling.
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ABSTRACT:
SUBMITTER: Chung W
PROVIDER: S-EPMC2323612 | biostudies-literature | 2008 Apr
REPOSITORIES: biostudies-literature
Publications
Identification of novel tumor markers in prostate, colon and breast cancer by unbiased methylation profiling.
Chung Woonbok W Kwabi-Addo Bernard B Ittmann Michael M Jelinek Jaroslav J Shen Lanlan L Yu Yinhua Y Issa Jean-Pierre J JP
PloS one 20080430 4
DNA hypermethylation is a common epigenetic abnormality in cancer and may serve as a useful marker to clone cancer-related genes as well as a marker of clinical disease activity. To identify CpG islands methylated in prostate cancer, we used methylated CpG island amplification (MCA) coupled with representational difference analysis (RDA) on prostate cancer cell lines. We isolated 34 clones that corresponded to promoter CpG islands, including 5 reported targets of hypermethylation in cancer. We c ...[more]
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Project description:BackgroundTargeting tumor angiogenesis and vasculature is a promising strategy for the inhibition of tumor growth and dissemination. Evidence suggests that tumor vasculature expresses unique markers that distinguish it from normal vasculature. Our efforts focused on the molecular characterization of endothelial cells (EC) in the search for selective markers of tumor vasculature that might be helpful for the development of effective therapeutic approaches.ResultsWe investigated by microarray analysis the gene expression profiles of EC purified and cultured from tumor (ovarian carcinoma [HOC-EC]) and normal (human adrenal gland [HA-EC]) tissue specimens. We found distinct transcriptional features characterizing the EC of different origin, and identified 158 transcripts highly expressed by HOC-EC. We analyzed four of these genes, ADAM23, FAP, GPNMB and PRSS3, which were not previously known to be expressed by endothelium. In vitro experiments confirmed the higher expression of the selected genes in tumor-derived endothelium with no expression in tumor cells. In vivo investigation by in situ hybridization established that ADAM23, GPNMB and PRSS3 expression is localized on blood vessels of human cancer specimens.ConclusionThese findings elucidate some of the molecular features of the tumor endothelium. Comparative transcriptomic analysis allowed us to determine molecular differences of tumor and normal tissue-derived endothelium and to identify novel markers that might be exploited to selectively target tumor vasculature.
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2011-01-04 | E-GEOD-24747 | biostudies-arrayexpress
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2011-01-04 | GSE24747 | GEO
Project description:BackgroundNumerous evidence has suggested that long non-coding RNA (lncRNA) acts an important role in tumor biology. This study focuses on the identification of novel prognostic lncRNA biomarkers predicting tumor recurrence in human colon adenocarcinoma.MethodsWe obtained the research data from The Cancer Genome Atlas (TCGA) database. The interaction among different expressed lncRNA, miRNA and mRNA markers between colon adenocarcinoma patients with and without tumor recurrence were verified with miRcode, starBase and miRTarBase databases. We established the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on the verified association between the selected markers. We performed the functional enrichment analysis to obtain better understanding of the selected lncRNAs. Then we use multivariate logistic regression to identify the prognostic lncRNA markers with covariates. We also generated a nomogram predicting tumor recurrence risk based on the identified lncRNA biomarkers and clinical covariates.ResultsWe included 12,727 lncRNA, 1881 miRNA and 47,761 mRNA profiling and clinical features for 113 colon adenocarcinoma patients obtained from the TCGA database. After filtration, we used 37 specific lncRNAs, 60 miRNAs and 148 mRNAs in the ceRNA network analysis. We identified five lncRNAs as prognostic lncRNA markers predicting tumor recurrence in colon adenocarcinoma, in which four of them were identified for the first time. Finally, we generated a nomogram illustrating the association between the identified lncRNAs and the tumor recurrence risk in colon adenocarcinoma.ConclusionsThe four newly identified lncRNA biomarkers might be potential prognostic biomarkers predicting tumor recurrence in colon adenocarcinoma. We recommend that further clinical and fundamental researches be conducted on the identified lncRNA markers.
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Project description:Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation. We aimed to identify genetic markers associated with breast cancer-specific survival. We conducted a large meta-analysis of studies in populations of European ancestry, including 37954 patients with 2900 deaths from breast cancer. Each study had been genotyped for between 200000 and 900000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project. We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23059 ER-positive patients (1333 events). All statistical tests were two-sided. We identified one new locus (rs2059614 at 11q24.2) associated with survival in ER-negative breast cancer cases (hazard ratio [HR] = 1.95, 95% confidence interval [CI] = 1.55 to 2.47, P = 1.91 x 10(-8)). Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation. The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes. A second locus (rs148760487 at 2q24.2) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients. Here the results of genotyping suggested that the finding was less robust. This is currently the largest study investigating genetic variation associated with breast cancer survival. Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.
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Project description:BackgroundBeing the most widely used biomarker for immunotherapy, the microsatellite status has limitations in identifying all patients who benefit in clinical practice. It is essential to identify additional biomarkers to guide immunotherapy. Aberrant DNA methylation is consistently associated with changes in the anti-tumor immune response, which can promote tumor progression. This study aims to explore immunotherapy biomarkers for colon cancers from the perspective of DNA methylation.MethodsThe related data (RNA sequencing data and DNA methylation data) were obtained from The Cancer Genome Atlas (TCGA) and UCSC XENA database. Methylation-driven genes (MDGs) were identified through the Pearson correlation analysis. Unsupervised consensus clustering was conducted using these MDGs to identify distinct clusters of colon cancers. Subsequently, we evaluated the immune status and predicted the efficacy of immunotherapy by tumor immune dysfunction and exclusion (Tide) score. Finally, The Quantitative Differentially Methylated Regions (QDMR) software was used to identify the specific DNA methylation markers within particular clusters.ResultsA total of 282 MDGs were identified by integrating the DNA methylation and RNA-seq data. Consensus clustering using the K-means algorithm revealed that the optimal number of clusters was 4. It was revealed that the composition of the tumor immune microenvironment (TIME) in Cluster 1 was significantly different from others, and it exhibited a higher level of tumor mutation burdens (TMB) and stronger anti-tumor immune activity. Furthermore, we identified three specific hypermethylation genes that defined Cluster 1 (PCDH20, APCDD1, COCH). Receiver operating characteristic (ROC) curves demonstrated that these specific markers could effectively distinguish Cluster 1 from other clusters, with an AUC of 0.947 (95% CI 0.903-0.990). Finally, we selected clinical samples for immunohistochemical validation.ConclusionIn conclusion, through the analysis of DNA methylation, consensus clustering of colon cancer could effectively identify the cluster that benefit from immunotherapy along with specific methylation biomarkers.
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