Disease-specific classification using deconvoluted whole blood gene expression.

ABSTRACT: 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

SUBMITTER: Wang L 

PROVIDER: S-EPMC5011717 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature