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

References

• American Cancer Society, Cancer Facts & Figures 2020.
   Google Scholar
• Cheung AH, Chow C, To KF. Latest development of liquid biopsy. J Thorac Dis. 2018;10(Suppl 14):S1645–S1651.
   Google Scholar
• Hausler SF, Keller A, Chandran PA, et al. Whole blood-derived miRNA profiles as potential new tools for ovarian cancer screening. Br J Cancer. 2010;103(5):693–700.
   Google Scholar
• Wang L, Ni S, Du Z, et al. A six-CpG-based methylation markers for the diagnosis of ovarian cancer in blood. J Cell Biochem. 2020;121(2):1409–1419.
   Google Scholar
• Accomando WP, Wiencke JK, Houseman EA, et al. Decreased NK cells in patients with head and neck cancer determined in archival DNA. Clin Cancer Res. 2012;18(22):6147–6154.
   Google Scholar
• Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
   Google Scholar
• Li H, Zheng T, Chen B, et al. Similar blood-borne DNA methylation alterations in cancer and inflammatory diseases determined by subpopulation shifts in peripheral leukocytes. Br J Cancer. 2014;111(3):525–531.
   Google Scholar
• Chakraborty S, Datta S, Datta S. Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Bioinformatics. 2012;28(6):799–806.
   Google Scholar
• Langevin SM, Houseman EA, Accomando WP, et al. Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics. 2014;9(6):884–895.
   Google Scholar
• Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–739.
   Google Scholar
• Shi P, Ray S, Zhu Q, et al. Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction. BMC Bioinformatics. 2011;12:375.
   Google Scholar
• Hong G, Li H, Li M, et al. A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples. Brief Bioinform. 2018;19(4):613–621.
   Google Scholar
• Li H, Hong G, Lin M, et al. Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of Alzheimer’s disease. Sci Rep. 2017;7(1):14027.
   Google Scholar
• Shiao SPK, Xiao H, Dong L, et al. Genome wide DNA differential methylation regions in colorectal cancer patients in relation to blood related family members, obese and non-obese controls - a preliminary report. Oncotarget. 2018;9(39):25557–25571.
   Google Scholar
• Reinius LE, Acevedo N, Joerink M, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012;7(7):e41361.
   Google Scholar
• Teschendorff AE, Menon U, Gentry-Maharaj A, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20(4):440–446.
   Google Scholar
• Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy. Stat Soc B. 1995;57:289–300.
   Google Scholar
• Liu Y, Aryee MJ, Padyukov L, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142–147.
   Google Scholar
• Koestler DC, Christensen B, Karagas MR, et al. Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis. Epigenetics. 2013;8(8):816–826.
   Google Scholar
• W. Huang da, B. T. Sherman, and R. A. Lempicki. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13.
   Google Scholar
• Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362–D368.
   Google Scholar
• Forbes SA, Beare D, Boutselakis H, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017;45(D1):D777–D783.
   Google Scholar
• Teschendorff AE, Menon U, Gentry-Maharaj A, et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One. 2009;4(12):e8274.
   Google Scholar
• Ju SW, Ju SG, Wang FM, et al. A functional anti-human 4-1BB ligand monoclonal antibody that enhances proliferation of monocytes by reverse signaling of 4-1BBL. Hybrid Hybridomics. 2003;22(5):333–338.
   Google Scholar
• Schwarz H, Tuckwell J, Lotz M. A receptor induced by lymphocyte activation (ILA): a new member of the human nerve-growth-factor/tumor-necrosis-factor receptor family. Gene. 1993;134(2):295–298.
   Google Scholar
• Gatzka M, Piekorz R, Moriggl R, et al. A role for STAT5A/B in protection of peripheral T-lymphocytes from postactivation apoptosis: insights from gene expression profiling. Cytokine. 2006;34(3–4):143–154.
   Google Scholar
• Theocharis AD, Skandalis SS, Neill T, et al. Insights into the key roles of proteoglycans in breast cancer biology and translational medicine. Biochim Biophys Acta. 2015;1855(2):276–300.
   Google Scholar
• Shacter E, Weitzman SA. Chronic inflammation and cancer. Oncology (Williston Park). 2002;16(2):217–226. 229; discussion 230–2.
   Google Scholar
• Bach M, Moon J, Moore R, et al. A neutrophil activation biomarker panel in prognosis and monitoring of patients with rheumatoid arthritis. Arthritis Rheumatol. 2020;72(1):47–56.
   Google Scholar
• Odegaard E, Davidson B, Elgaaen BV, et al. Circulating calprotectin in ovarian carcinomas and borderline tumors of the ovary. Am J Obstet Gynecol. 2008;198(4):418 e1–7.
   Google Scholar
• Mollica Poeta V, Massara M, Capucetti A, et al. Chemokines and chemokine receptors: new targets for cancer immunotherapy. Front Immunol. 2019;10:379.
   Google Scholar
• Pajonk F, McBride WH. The proteasome in cancer biology and treatment. Radiat Res. 2001;156(5 Pt 1):447–459.
   Google Scholar
• Qureshi N, Vogel SN, Van Way C 3rd, et al. The proteasome: a central regulator of inflammation and macrophage function. Immunol Res. 2005;31(3):243–260.
   Google Scholar
• Zhang L, Yu M, Deng J, et al. Chemokine signaling pathway involved in CCL2 expression in patients with rheumatoid arthritis. Yonsei Med J. 2015;56(4):1134–1142.
   Google Scholar
• Neu B, Rad R, Reindl W, et al. Expression of tumor necrosis factor- alpha -related apoptosis-inducing ligand and its proapoptotic receptors is down-regulated during gastric infection with virulent cagA+/vacAs1+ Helicobacter pylori strains. J Infect Dis. 2005;191(4):571–578.
   Google Scholar
• Shivapurkar N, Toyooka S, Toyooka KO, et al. Aberrant methylation of trail decoy receptor genes is frequent in multiple tumor types. Int J Cancer. 2004;109(5):786–792.
   Google Scholar
• Venza M, Visalli M, Catalano T, et al. Impact of DNA methyltransferases on the epigenetic regulation of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptor expression in malignant melanoma. Biochem Biophys Res Commun. 2013;441(4):743–750.
   Google Scholar
• Murata M. Inflammation and cancer. Environ Health Prev Med. 2018;23(1):50.
   Google Scholar
• Jung YJ, Isaacs JS, Lee S, et al. IL-1beta-mediated up-regulation of HIF-1alpha via an NFkappaB/COX-2 pathway identifies HIF-1 as a critical link between inflammation and oncogenesis. Faseb J. 2003;17(14):2115–2117.
   Google Scholar
• Horak P, Crawford AR, Vadysirisack DD, et al. Negative feedback control of HIF-1 through REDD1-regulated ROS suppresses tumorigenesis. Proc Natl Acad Sci U S A. 2010;107(10):4675–4680.
   Google Scholar
• Trebec-Reynolds DP, Voronov I, Heersche JN, et al. VEGF-A expression in osteoclasts is regulated by NF-kappaB induction of HIF-1alpha. J Cell Biochem. 2010;110(2):343–351.
   Google Scholar