Dataset Information

Diagnostic and prognostic utility of a DNA hypermethylated gene signature in prostate cancer.

ABSTRACT: We aimed to identify a prostate cancer DNA hypermethylation microarray signature (denoted as PHYMA) that differentiates prostate cancer from benign prostate hyperplasia (BPH), high from low-grade and lethal from non-lethal cancers. This is a non-randomized retrospective study in 111 local Asian men (87 prostate cancers and 24 BPH) treated from 1995 to 2009 in our institution. Archival prostate epithelia were laser-capture microdissected and genomic DNA extracted and bisulfite-converted. Samples were profiled using Illumina GoldenGate Methylation microarray, with raw data processed by GenomeStudio. A classification model was generated using support vector machine, consisting of a 55-probe DNA methylation signature of 46 genes. The model was independently validated on an internal testing dataset which yielded cancer detection sensitivity and specificity of 95.3% and 100% respectively, with overall accuracy of 96.4%. Second validation on another independent western cohort yielded 89.8% sensitivity and 66.7% specificity, with overall accuracy of 88.7%. A PHYMA score was developed for each sample based on the state of methylation in the PHYMA signature. Increasing PHYMA score was significantly associated with higher Gleason score and Gleason primary grade. Men with higher PHYMA scores have poorer survival on univariate (p?=?0.0038, HR?=?3.89) and multivariate analyses when controlled for (i) clinical stage (p?=?0.055, HR?=?2.57), and (ii) clinical stage and Gleason score (p?=?0.043, HR?=?2.61). We further performed bisulfite genomic sequencing on 2 relatively unknown genes to demonstrate robustness of the assay results. PHYMA is thus a signature with high sensitivity and specificity for discriminating tumors from BPH, and has a potential role in early detection and in predicting survival.

SUBMITTER: Goh LK

PROVIDER: S-EPMC3953552 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Diagnostic and prognostic utility of a DNA hypermethylated gene signature in prostate cancer.

Goh Liang Kee LK Liem Natalia N Vijayaraghavan Aadhitthya A Chen Gengbo G Lim Pei Li PL Tay Kae-Jack KJ Chang Michelle M Low John Soon Wah JS Joshi Adita A Huang Hong Hong HH Kalaw Emarene E Tan Puay Hoon PH Hsieh Wen-Son WS Yong Wei Peng WP Alumkal Joshi J Sim Hong Gee HG

PloS one 20140313 3

We aimed to identify a prostate cancer DNA hypermethylation microarray signature (denoted as PHYMA) that differentiates prostate cancer from benign prostate hyperplasia (BPH), high from low-grade and lethal from non-lethal cancers. This is a non-randomized retrospective study in 111 local Asian men (87 prostate cancers and 24 BPH) treated from 1995 to 2009 in our institution. Archival prostate epithelia were laser-capture microdissected and genomic DNA extracted and bisulfite-converted. Samples ...[more]

PMID: 24626295

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Project description:Previous studies have revealed that upregulation of interleukin 15 receptor ? (IL15RA) contributes to improved prognosis of breast cancer. The present study aimed to elucidate the molecular mechanisms underlying the antitumor effect induced by IL15RA upregulation, and to identify a gene signature capable of predicting the survival of patients with breast cancer. Using paired gene expression and methylation data of breast cancer samples from The Cancer Genome Atlas data portal, differentially expressed genes (DEGs) were identified in hypermethylated and hypomethylated IL15RA breast cancer samples. Furthermore, a gene signature-based risk-scoring model was developed according to the Cox regression coefficients of survival-associated DEGS. The gene signature was applied to classify patients with breast cancer and hypermethylated IL15RA into two risk groups via Kaplan-Meier survival analysis of overall survival (OS) time. Functional enrichment analysis was conducted to decipher the biological roles of the DEGs between the two risk groups. A total of 326 DEGs were present in the hypomethylation and hypermethylation samples compared with in the normal samples. A four-gene signature [SH3 and cysteine rich domain 2 (STAC2), proline rich 11 (PRR11), homeobox C11 (HOXC11) and nucleolar and spindle associated protein 1 (NUSAP1)] was identified as able to successfully separate patients with breast cancer and hypermethylated IL15RA into two risk groups with significantly different OS time. The signature revealed similar predictive performance in an independent set. Significant enrichment of the 'receptor interaction' and 'cell adhesion molecules (CAM)' pathways, which involved the DEGs, occurred between the two risk groups. These findings suggested that IL15RA may participate in the regulation of STAC2, PRR11, HOXC11, NUSAP1, and 'ECM-receptor interaction' and 'cell adhesion molecules' pathways, and therefore in the suppression of breast cancer development and progression. The four-gene signature may have potential prognostic value for breast cancer.

Project description:Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC). Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database. Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset. Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

Dataset Information

Diagnostic and prognostic utility of a DNA hypermethylated gene signature in prostate cancer.

Publications

Diagnostic and prognostic utility of a DNA hypermethylated gene signature in prostate cancer.

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