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Differential expression pattern-based prioritization of candidate genes through integrating disease-specific expression data.

机译：通过整合疾病特异性表达数据，基于差异表达模式对候选基因进行优先排序。

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摘要

Expression data can reveal subtle transcriptional changes that mediate the clinical phenotype of the disease resulting from interaction between genetic and environmental factors, which offers us a new perspective to prioritize candidate genes. Here, we proposed a novel differential expression pattern (DEP)-based approach integrating numerous disease-specific expression data sets for prioritizing candidate genes. Using breast cancer as a case study, we validated the efficiency of our approach through integrating 12 breast cancer-related expression data sets based on the leave-one-out cross-validation. Particularly, prioritization based on subtype-specific expression data sets could generate significantly higher performance. The performance could be continually improved with the increasing expression data sets regardless of platform heterogeneity. We further validated the robustness of this approach by application to prostate cancer. Additionally, our approach showed higher performance in comparison with other expression-based approaches and better capability of identification of less well-studied disease genes in comparison with other integration-based approaches.
机译：表达数据可以揭示由遗传和环境因素之间的相互作用导致的介导疾病临床表型的细微转录变化，这为我们优先考虑候选基因提供了新的视角。在这里，我们提出了一种新的基于差异表达模式（DEP）的方法，该方法整合了许多疾病特异性表达数据集，以对候选基因进行优先排序。我们以乳腺癌为例，通过基于留一法交叉验证的12种乳腺癌相关表达数据集，验证了我们方法的有效性。特别是，基于特定于亚型的表达数据集的优先级排序可以产生更高的性能。无论平台的异构性如何，随着表达数据集的增加，性能可以不断提高。我们通过应用于前列腺癌进一步验证了这种方法的鲁棒性。此外，与其他基于表达的方法相比，我们的方法显示出更高的性能，与其他基于整合的方法相比，我们的方法可以更好地识别研究较少的疾病基因。

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