Machado Catia M, Freitas Ana T, Couto Francisco M
LaSIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
J Biomed Semantics. 2013 Oct 8;4(1):21. doi: 10.1186/2041-1480-4-21.
: Enrichment analysis is well established in the field of transcriptomics, where it is used to identify relevant biological features that characterize a set of genes obtained in an experiment.This article proposes the application of enrichment analysis as a first step in a disease prognosis methodology, in particular of diseases with a strong genetic component. With this analysis the objective is to identify clinical and biological features that characterize groups of patients with a common disease, and that can be used to distinguish between groups of patients associated with disease-related events. Data mining methodologies can then be used to exploit those features, and assist medical doctors in the evaluation of the patients in respect to their predisposition for a specific event.In this work the disease hypertrophic cardiomyopathy (HCM) is used as a case-study, as a first test to assess the feasibility of the application of an enrichment analysis to disease prognosis. To perform this assessment, two groups of patients have been considered: patients that have suffered a sudden cardiac death episode and patients that have not.The results presented were obtained with genetic data and the Gene Ontology, in two enrichment analyses: an enrichment profiling aiming at characterizing a group of patients (e.g. that suffered a disease-related event) based on their mutations; and a differential enrichment aiming at identifying differentiating features between a sub-group of patients and all the patients with the disease. These analyses correspond to an adaptation of the standard enrichment analysis, since multiple sets of genes are being considered, one for each patient.The preliminary results are promising, as the sets of terms obtained reflect the current knowledge about the gene functions commonly altered in HCM patients, thus allowing their characterization. Nevertheless, some factors need to be taken into consideration before the full potential of the enrichment analysis in the prognosis methodology can be evaluated. One of such factors is the need to test the enrichment analysis with clinical data, in addition to genetic data, since both types of data are expected to be necessary for prognosis purposes.
富集分析在转录组学领域已得到广泛应用，用于识别实验中获得的一组基因所具有的相关生物学特征。本文提出将富集分析作为疾病预后方法的第一步，特别是对于具有强大遗传成分的疾病。通过这种分析，目标是识别出表征患有常见疾病的患者群体的临床和生物学特征，并可用于区分与疾病相关事件相关的患者群体。然后可以使用数据挖掘方法来利用这些特征，并协助医生评估患者发生特定事件的易感性。在这项工作中，肥厚型心肌病（HCM）被用作案例研究，作为评估富集分析应用于疾病预后可行性的首次测试。为了进行此评估，考虑了两组患者：经历过心脏性猝死事件的患者和未经历过的患者。呈现的结果是通过基因数据和基因本体论在两次富集分析中获得的：一次富集分析旨在根据患者的突变来表征一组患者（例如经历过疾病相关事件的患者）；另一次差异富集分析旨在识别患者亚组与所有患有该疾病的患者之间的差异特征。这些分析对应于对标准富集分析的改编，因为正在考虑多组基因，每个患者一组。初步结果很有希望，因为获得的术语集反映了目前关于HCM患者中常见改变的基因功能的知识，从而允许对其进行表征。然而，在评估富集分析在预后方法中的全部潜力之前，需要考虑一些因素。其中一个因素是除了基因数据之外，还需要用临床数据测试富集分析，因为这两种类型的数据对于预后目的都可能是必要的。
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