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基于七个CpG的预后特征与基因表达相结合可预测口腔鳞状细胞癌的生存率。

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

作者信息

Shen Sipeng, Wang Guanrong, Shi Qianwen, Zhang Ruyang, Zhao Yang, Wei Yongyue, Chen Feng, Christiani David C

机构信息

Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China.

出版信息

Clin Epigenetics. 2017 Aug 24;9:88. doi: 10.1186/s13148-017-0392-9. eCollection 2017.


DOI:10.1186/s13148-017-0392-9
PMID:28852427
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5571486/
Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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,  = 5.52 × 10, area under the curve (AUC) = 0.76; validation set 1: HR = 2.79,  = 0.010, AUC = 0.67; validation set 2: HR = 3.69,  = 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 (, , , , , and ) 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). CONCLUSION: 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.

摘要

背景:DNA甲基化通过识别多种癌症(包括口腔鳞状细胞癌(OSCC),最常见的头颈部鳞状细胞癌)的关键生物标志物,在基因组生物学领域引发了一场新的革命。 方法:采用多阶段筛选策略来识别基于DNA甲基化的OSCC预后特征。我们将癌症基因组图谱(TCGA)数据用作训练集,并在来自基因表达综合数据库(GEO)的两个独立数据集中进行验证。同时还探讨了DNA甲基化与相应基因表达之间的相关性以及基因表达的预后价值。 结果:确定了7个与OSCC总生存期显著相关的DNA甲基化CpG位点。预后特征是这7个CpG位点的加权线性组合,成功区分了OSCC患者的总生存期,并且对生存具有中等预测能力[训练集:风险比(HR)= 3.23,= 5.52×10,曲线下面积(AUC)= 0.76;验证集1:HR = 2.79,= 0.010,AUC = 0.67;验证集2:HR = 3.69,= 0.011,AUC = 0.66]。按人乳头瘤病毒状态、临床分期、年龄、性别、吸烟状态和分级进行的分层分析具有统计学意义。与候选CpG位点(、、、、、和)相对应的基因表达也与患者生存显著相关。整合DNA甲基化、基因表达和临床信息的特征显示出更好的预后预测能力(AUC = 0.78)。 结论:整合DNA甲基化、基因表达和临床信息的预后特征为OSCC患者提供了比仅使用临床信息更好的预后预测价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/1443d3154093/13148_2017_392_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/50bd1356193c/13148_2017_392_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/39a1acfaf9d4/13148_2017_392_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/b01715164ff5/13148_2017_392_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/24c2c60e15b3/13148_2017_392_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/fe5ae17f42dd/13148_2017_392_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/1443d3154093/13148_2017_392_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/50bd1356193c/13148_2017_392_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/39a1acfaf9d4/13148_2017_392_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/b01715164ff5/13148_2017_392_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/24c2c60e15b3/13148_2017_392_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/fe5ae17f42dd/13148_2017_392_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/5571486/1443d3154093/13148_2017_392_Fig6_HTML.jpg

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