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Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma.

ABSTRACT: DNA methylation has started a recent revolution in genomics biology by identifying key biomarkers for multiple cancers, including oral squamous cell carcinoma (OSCC), the most common head and neck squamous cell carcinoma.A multi-stage screening strategy was used to identify DNA-methylation-based signatures for OSCC prognosis. We used The Cancer Genome Atlas (TCGA) data as training set which were validated in two independent datasets from Gene Expression Omnibus (GEO). The correlation between DNA methylation and corresponding gene expression and the prognostic value of the gene expression were explored as well.The seven DNA methylation CpG sites were identified which were significantly associated with OSCC overall survival. Prognostic signature, a weighted linear combination of the seven CpG sites, successfully distinguished the overall survival of OSCC patients and had a moderate predictive ability for survival [training set: hazard ratio (HR) = 3.23, P = 5.52 × 10-10, area under the curve (AUC) = 0.76; validation set 1: HR = 2.79, P = 0.010, AUC = 0.67; validation set 2: HR = 3.69, P = 0.011, AUC = 0.66]. Stratification analysis by human papillomavirus status, clinical stage, age, gender, smoking status, and grade retained statistical significance. Expression of genes corresponding to candidate CpG sites (AJAP1, SHANK2, FOXA2, MT1A, ZNF570, HOXC4, and HOXB4) was also significantly associated with patient's survival. Signature integrating of DNA methylation, gene expression, and clinical information showed a superior ability for prognostic prediction (AUC = 0.78).Prognostic signature integrated of DNA methylation, gene expression, and clinical information provides a better prognostic prediction value for OSCC patients than that with clinical information only.

SUBMITTER: Shen S

PROVIDER: S-EPMC5571486 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma.

Shen Sipeng S Wang Guanrong G Shi Qianwen Q Zhang Ruyang R Zhao Yang Y Wei Yongyue Y Chen Feng F Christiani David C DC

Clinical epigenetics 20170824

<h4>Background</h4>DNA methylation has started a recent revolution in genomics biology by identifying key biomarkers for multiple cancers, including oral squamous cell carcinoma (OSCC), the most common head and neck squamous cell carcinoma.<h4>Methods</h4>A multi-stage screening strategy was used to identify DNA-methylation-based signatures for OSCC prognosis. We used The Cancer Genome Atlas (TCGA) data as training set which were validated in two independent datasets from Gene Expression Omnibus ...[more]

PMID: 28852427

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Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA).MethodsExpression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual's overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed.ResultsThree mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (-0.38602 × expression level of CLEC3B) + (-0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis.ConclusionA 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma.

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Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma.

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