title: Similar blood-borne DNA methylation alterations in cancer and inflammatory diseases determined by subpopulation shifts in peripheral leukocytes.
abstract: BACKGROUND
  Although many DNA methylation (DNAm) alterations observed in peripheral whole blood/leukocytes and serum have been considered as potential diagnostic markers for cancer, their origin and their specificity for cancer (e.g., vs inflammatory diseases) remain unclear.
  
  
  METHODS
  From publicly available datasets, we identified changes in the methylation of blood-borne DNA for multiple cancers and inflammatory diseases. We compared the identified changes with DNAm difference between myeloid and lymphoid cells extracted from two datasets.
  
  
  RESULTS
  At least 94.7% of the differentially methylated DNA loci (DM loci) observed in peripheral whole blood/leukocytes and serum of cancer patients overlapped with DM loci that distinguish between myeloid and lymphoid cells and >99.9% of the overlapped DM loci had consistent alteration states (hyper- or hypomethylation) in cancer samples compared to normal controls with those in myeloid cells compared to lymphoid cells (binomial test, P-value <2.2 × 10(-16)). Similar results were observed for DM loci in peripheral whole blood/leukocytes in patients with rheumatoid arthritis or inflammatory bowel diseases. The direct comparison between DM loci observed in the peripheral whole blood/leukocytes of patients with inflammatory diseases and DM loci observed in the peripheral whole blood of patients with cancer showed that DM loci detected from cancer and inflammatory diseases also had significantly consistent alteration states (binomial test, P-value <2.2 × 10(-16)).
  
  
  CONCLUSIONS
  DNAm changes observed in the peripheral whole blood/leukocytes and serum of cancer patients and in the peripheral whole blood/leukocytes of inflammatory disease patients are predominantly determined by the increase of myeloid cells and the decrease of lymphoid cells under the disease conditions, in the sense that their alteration states in disease samples compared to normal controls mainly reflect the DNAm difference between myeloid and lymphoid cells. These analyses highlight the importance of comparing cancer and inflammatory disease directly for the identification of cancer-specific diagnostic biomarkers.
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: H
authors: H
authors: Li
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: T
authors: T
authors: Zheng
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
authors: B
authors: B
authors: Chen
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: G
authors: G
authors: Hong
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: W
authors: W
authors: Zhang
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: T
authors: T
authors: Shi
authors: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: S
authors: S
authors: Li
authors: 1] College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China [2] Department of Bioinformatics, School of Basic Medical College, Fujian Medical University, Fuzhou 350004, China.
authors: L
authors: L
authors: Ao
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
authors: C
authors: C
authors: Wang
authors: 1] Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China [2] College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China [3] Department of Bioinformatics, School of Basic Medical College, Fujian Medical University, Fuzhou 350004, China.
authors: Z
authors: Z
authors: Guo
mesh_terms: D001172:Arthritis, Rheumatoid / Q000097:blood* / Q000235:genetics / Q000473:pathology; D014408:Biomarkers, Tumor / Q000235:genetics; D002294:Carcinoma, Squamous Cell / Q000097:blood* / Q000235:genetics / Q000473:pathology; D016022:Case-Control Studies; D019175:DNA Methylation; D005260:Female; D056426:Genetic Loci; D006258:Head and Neck Neoplasms / Q000097:blood* / Q000235:genetics / Q000473:pathology; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000378:metabolism; D008175:Lung Neoplasms / Q000097:blood* / Q000235:genetics / Q000473:pathology; D022423:Myeloid Cells / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000097:blood* / Q000235:genetics / Q000473:pathology; D055752:Small Cell Lung Carcinoma / Q000097:blood* / Q000235:genetics / Q000473:pathology; D000077195:Squamous Cell Carcinoma of Head and Neck
chemical_list: D014408:Biomarkers, Tumor