Literature DB >> 28200092

A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples.

Guini Hong1, Hongdong Li1, Mengyao Li1, Weicheng Zheng1, Jing Li1, Meirong Chi1, Jun Cheng1, Zheng Guo1.   

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.

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Year:  2018        PMID: 28200092     DOI: 10.1093/bib/bbx009

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  3 in total

1.  Identification of differential DNA methylation alterations of ovarian cancer in peripheral whole blood based on within-sample relative methylation orderings.

Authors:  Hongdong Li; Fengle Jiang; Yuhui Du; Na Li; Zhihong Chen; Hao Cai; You Guo; Guini Hong
Journal:  Epigenetics       Date:  2021-03-22       Impact factor: 4.528

2.  Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers.

Authors:  Guini Hong; Fengyuan Luo; Zhihong Chen; Liyuan Ma; Guiyang Lin; Tong Wu; Na Li; Hao Cai; Tao Hu; Haijian Zhong; You Guo; Hongdong Li
Journal:  Front Med (Lausanne)       Date:  2022-08-02

3.  Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of Alzheimer's disease.

Authors:  Hongdong Li; Guini Hong; Mengna Lin; Yidan Shi; Lili Wang; Fengle Jiang; Fan Zhang; Yuhang Wang; Zheng Guo
Journal:  Sci Rep       Date:  2017-10-25       Impact factor: 4.379

  3 in total

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