Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, NY, 10029, USA.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, 10029, USA.
Sci Rep. 2016 Sep 6;6:32976. doi: 10.1038/srep32976.
Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.
基于血液的生物标志物检测具有微创的优势。已经有报道称，基于外周血基因表达的诊断和预后模型可用于各种类型的疾病。然而，这些研究大多只关注一种疾病类型，未能解决所确定的基因表达特征是特定于疾病的，还是更广泛地适用于多种疾病。我们对涵盖广泛疾病和生理状况的 46 个全血基因表达数据集进行了荟萃分析。我们的分析揭示了多个疾病之间共享的特征基因的惊人重叠，这是由细胞成分变化的潜在共同模式驱动的，具体表现为髓样细胞增加和淋巴细胞减少。这些观察结果揭示了构建能够区分不同疾病类型和正常对照的特定于疾病的分类器的必要性，并强调了在基于血液基因表达的模型中细胞成分变化的重要性。我们开发了一种新策略，通过利用细胞成分变化和细胞分子状态变化来开发基于血液的特定于疾病的模型，并使用独立数据集证明了其优越性。
