Disease-specific classification using deconvoluted whole blood gene expression.
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.
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.
Li
L
Wang
The Tisch Cancer Institute, Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, NY, 10029, USA.
William K
WK
Oh
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.|The Tisch Cancer Institute, Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, NY, 10029, USA.
Jun
J
Zhu
D015415:Biomarkers / Q000378:metabolism*; D019295:Computational Biology; D004194:Disease / Q000145:classification* / Q000235:genetics*; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis
D015415:Biomarkers