title: A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples.
abstract: Blood is a promising surrogate for solid tissue to investigate disease-associated molecular biomarkers. However, proportion changes of the constituent cells in the often-used peripheral whole blood (PWB) or peripheral blood mononuclear cell (PBMC) samples may influence the detection of cell-specific alterations under disease states. We propose a simple method, Ref-REO, to detect molecular alterations in leukocytes using the mixed-cell blood samples. The method is based on the predetermined within-sample relative expression orderings (REOs) of genes in purified leukocytes of healthy people. Both the simulated and real mixed-cell blood gene expression profiles were used to evaluate the method. Approximately 99% of the differentially expressed genes (DEGs) detected by Ref-REO in the simulated mixed-cell data are owing to the transcriptional alterations in leukocytes rather than the proportion changes of leukocytes. For the real mixed-cell data, the DEGs detected by Ref-REO in the PBMCs expression data for systemic lupus erythematosus (SLE) patients overlap significantly with the DEGs detected in the expression data of SLE CD4 + T cells and B cells and they are mainly enriched with mRNA editing and interferon-associated genes. The detected DEGs in the PWB data for lung carcinoma patients are significantly enriched with coagulation-associated functional categories that are closely associated with cancer progression. In conclusion, the proposed method is capable of detecting the disease-associated leukocyte-specific molecular alterations, using mixed-cell blood samples, which provides simple, transferable and easy-to-use candidates for disease biomarkers.
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Guini
authors.initials: G
authors.lastname: Hong
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Hongdong
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Mengyao
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Weicheng
authors.initials: W
authors.lastname: Zheng
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Meirong
authors.initials: M
authors.lastname: Chi
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.affiliation: Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D015415:Biomarkers / Q000097:blood; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood / Q000235:genetics / Q000473:pathology*; D008180:Lupus Erythematosus, Systemic / Q000097:blood / Q000235:genetics / Q000473:pathology*
chemical_list: D015415:Biomarkers