Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, UK.
Bioinformatics. 2012 Jun 1;28(11):1487-94. doi: 10.1093/bioinformatics/bts170. Epub 2012 Apr 6.
The standard paradigm in omic disciplines has been to identify biologically relevant biomarkers using statistics that reflect differences in mean levels of a molecular quantity such as mRNA expression or DNA methylation. Recently, however, it has been proposed that differential epigenetic variability may mark genes that contribute to the risk of complex genetic diseases like cancer and that identification of risk and early detection markers may therefore benefit from statistics based on differential variability.
Using four genome-wide DNA methylation datasets totalling 311 epithelial samples and encompassing all stages of cervical carcinogenesis, we here formally demonstrate that differential variability, as a criterion for selecting DNA methylation features, can identify cancer risk markers more reliably than statistics based on differences in mean methylation. We show that differential variability selects features with heterogeneous outlier methylation profiles and that these play a key role in the early stages of carcinogenesis. Moreover, differentially variable features identified in precursor non-invasive lesions exhibit significantly increased enrichment for developmental genes compared with differentially methylated sites. Conversely, differential variability does not add predictive value in cancer studies profiling invasive tumours or whole-blood tissue. Finally, we incorporate the differential variability feature selection step into a novel adaptive index prediction algorithm called EVORA (epigenetic variable outliers for risk prediction analysis), and demonstrate that EVORA compares favourably to powerful prediction algorithms based on differential methylation statistics.
Statistics based on differential variability improve the detection of cancer risk markers in the context of DNA methylation studies profiling epithelial preinvasive neoplasias. We present a novel algorithm (EVORA) which could be used for prediction and diagnosis of precursor epithelial cancer lesions.
R-scripts implementing EVORA are available from CRAN (www.r-project.org).
在组学领域，标准范式一直是使用统计学方法来识别生物学相关的生物标志物，这些统计学方法反映了分子数量（如 mRNA 表达或 DNA 甲基化）的平均水平差异。然而，最近有人提出，差异表观遗传可变性可能标志着导致复杂遗传疾病（如癌症）风险的基因，因此，风险和早期检测标志物的识别可能受益于基于差异可变性的统计学方法。
我们使用了四个全基因组 DNA 甲基化数据集，共包含 311 个上皮样本，涵盖了宫颈癌发生的所有阶段，正式证明了作为选择 DNA 甲基化特征的标准，差异可变性比基于平均甲基化差异的统计学方法更可靠地识别癌症风险标志物。我们表明，差异可变性选择具有异质异常甲基化谱的特征，这些特征在致癌作用的早期阶段发挥关键作用。此外，在癌前非浸润性病变中识别出的差异可变性特征与发育基因的显著富集相比，差异甲基化位点更为明显。相反，在 profiling 侵袭性肿瘤或全血组织的癌症研究中，差异可变性并不能增加预测价值。最后，我们将差异可变性特征选择步骤纳入一种称为 EVORA（用于风险预测分析的表观遗传变量异常值）的新型自适应指数预测算法中，并证明 EVORA 与基于差异甲基化统计的强大预测算法相比具有优势。
在 profiling 上皮前肿瘤的 DNA 甲基化研究中，基于差异可变性的统计学方法提高了癌症风险标志物的检测能力。我们提出了一种新算法（EVORA），可用于预测和诊断上皮前癌症病变。
实现 EVORA 的 R 脚本可从 CRAN（www.r-project.org）获得。
