Dataset Information

Prognostic CpG methylation biomarkers identified by methylation array in esophageal squamous cell carcinoma patients.

ABSTRACT:

Background

Esophageal squamous cell carcinoma (ESCC) is an aggressive cancer with poor prognosis. We aimed to identify a panel of CpG methylation biomarkers for prognosis prediction of ESCC patients.

Methods

Illumina's GoldenGate methylation array, supervised principal components, Kaplan-Meier survival analyses and Cox regression model were conducted on dissected tumor tissues from a training cohort of 40 ESCC patients to identify potential CpG methylation biomarkers. Pyrosequencing quantitative methylation assay were performed to validate prognostic CpG methylation biomarkers in 61 ESCC patients. The correlation between DNA methylation and RNA expression of a validated marker, SOX17, was examined in a validation cohort of 61 ESCC patients.

Results

We identified a panel of nine CpG methylation probes located at promoter or exon1 region of eight genes including DDIT3, FES, FLT3, NTRK3, SEPT5, SEPT9, SOX1, and SOX17, for prognosis prediction in ESCC patients. Risk score calculated using the eight-gene panel statistically predicted poor outcome for patients with high risk score. These eight-gene also showed a significantly higher methylation level in tumor tissues than their corresponding normal samples in all patients analyzed. In addition, we also detected an inverse correlation between CpG hypermethylation and the mRNA expression level of SOX17 gene in ESCC patients, indicating that DNA hypermethylation was responsible for decreased expression of SOX17.

Conclusions

This study established a proof-of-concept CpG methylation biomarker panel for ESCC prognosis that can be further validated by multiple cohort studies. Functional characterization of the eight prognostic methylation genes in our biomarker panel could help to dissect the mechanism of ESCC tumorigenesis.

SUBMITTER: Kuo IY

PROVIDER: S-EPMC4057483 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic CpG methylation biomarkers identified by methylation array in esophageal squamous cell carcinoma patients.

Kuo I-Ying IY Chang Jia-Ming JM Jiang Shih-Sheng SS Chen Chung-Hsin CH Chang I-Shou IS Sheu Bor-Shyang BS Lu Pei-Jung PJ Chang Wei-Lun WL Lai Wu-Wei WW Wang Yi-Ching YC

International journal of medical sciences 20140530 8

<h4>Background</h4>Esophageal squamous cell carcinoma (ESCC) is an aggressive cancer with poor prognosis. We aimed to identify a panel of CpG methylation biomarkers for prognosis prediction of ESCC patients.<h4>Methods</h4>Illumina's GoldenGate methylation array, supervised principal components, Kaplan-Meier survival analyses and Cox regression model were conducted on dissected tumor tissues from a training cohort of 40 ESCC patients to identify potential CpG methylation biomarkers. Pyrosequenci ...[more]

PMID: 24936140

Similar Datasets

Project description:Background:DNA methylation has been implicated as a promising biomarker for precise cancer diagnosis. However, limited DNA methylation-based biomarkers have been described in esophageal squamous cell carcinoma (ESCC). Methods:A high-throughput DNA methylation dataset (100 samples) of ESCC from The Cancer Genome Atlas (TCGA) project was analyzed and validated along with another independent dataset (12 samples) from the Gene Expression Omnibus (GEO) database. The methylation status of peripheral blood mononuclear cells and peripheral blood leukocytes from healthy controls was also utilized for biomarker selection. The candidate CpG sites as well as their adjacent regions were further validated in 94 pairs of ESCC tumor and adjacent normal tissues from the Chinese Han population using the targeted bisulfite sequencing method. Logistic regression and several machine learning methods were applied for evaluation of the diagnostic ability of our panel. Results:In the discovery stage, five hyper-methylated CpG sites were selected as candidate biomarkers for further analysis as shown below: cg15830431, P?=?2.20?×?10-4; cg19396867, P?=?3.60?×?10-4; cg20655070, P?=?3.60?×?10-4; cg26671652, P?=?5.77?×?10-4; and cg27062795, P?=?3.60?×?10-4. In the validation stage, the methylation status of both the five CpG sites and their adjacent genomic regions were tested. The diagnostic model based on the combination of these five genomic regions yielded a robust performance (sensitivity?=?0.75, specificity?=?0.88, AUC?=?0.85). Eight statistical models along with five-fold cross-validation were further applied, in which the SVM model reached the best accuracy in both training and test dataset (accuracy?=?0.82 and 0.80, respectively). In addition, subgroup analyses revealed a significant difference in diagnostic performance between the alcohol use and non-alcohol use subgroups. Conclusions:Methylation profiles of the five genomic regions covering cg15830431 (STK3), cg19396867, cg20655070, cg26671652 (ZNF418), and cg27062795 (ZNF542) can be used for effective methylation-based testing for ESCC diagnosis.

Project description:BACKGROUND:Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC). METHODS AND MATERIALS:After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation set. Finally, based on the multivariate analysis, a nomogram including the FIGO stage, therapy outcome, and risk score level was built to predict progression-free survival (PFS) probability. RESULTS:A mRNA-based signature was developed to classify patients into high- and low-risk groups with significantly different PFS and overall survival (OS) rate (training set: p?<?0.001 for PFS, p?=?0.016 for OS; validation set: p?=?0.002 for PFS, p?=?0.028 for OS). The prognostic classifier was an independent and powerful prognostic biomarker for PFS in both cohorts (training set: hazard ratio [HR]?=?0.13, 95% CI 0.05-0.33, p?<?0.001; validation set: HR?=?0.02, 95% CI 0.01-0.04, p?<?0.001). A nomogram that integrated the independent prognostic factors was constructed for clinical application. The calibration curve showed that the nomogram was able to predict 1-, 3-, and 5-year PFS accurately, and it performed well in the external validation cohorts (concordance index: 0.828 and 0.864, respectively). CONCLUSION:The mRNA-based biomarker is a powerful and independent prognostic factor. Furthermore, the nomogram comprising our prognostic classifier is a promising predictor in identifying the progression risk of CSCC patients.

Dataset Information

Prognostic CpG methylation biomarkers identified by methylation array in esophageal squamous cell carcinoma patients.

Background

Methods

Results

Conclusions

Publications

Prognostic CpG methylation biomarkers identified by methylation array in esophageal squamous cell carcinoma patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets