Circulating cell-free nucleosomes as biomarkers for early detection of colorectal cancer
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Louise Rasmussen1, Ib Jarle Christensen1, Marielle Herzog2, Jake Micallef2, Hans Jørgen Nielsen1,3 and For the Danish Collaborative Group on Early Detection of Colorectal Cancer4
1Department of Surgical Gastroenterology 360, Hvidovre Hospital, Hvidovre, Denmark
2Belgian Volition SPRL, Isnes, Belgium
3Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
Louise Rasmussen, email: firstname.lastname@example.org
Keywords: biomarkers, tumor, colorectal neoplasms, early detection of cancer, histone code
Received: December 10, 2016 Accepted: October 10, 2017 Published: October 20, 2017
The aim was to evaluate serum levels of circulating cell-free nucleosomes (ccfn) containing a variety of epigenetic signals including 5-methylcytosine DNA, histone modifications H3K9Me3, H3K9Ac, H3S10PO4, H3K36Me3, H4K20Me3, H4PanAc and pH2AX, nucleosome variant H2AZ and nucleosome adducts with HMGB1 and EZH2 as well as ccfn per se, in addition to develop and evaluate predictor models based on the above mentioned ccfn and including serum levels of carcinoembryonic antigen (CEA), in early detection of colorectal cancer (CRC).
Blood-samples were collected from 4,105 individuals undergoing colonoscopy. Serum levels of ccfn and CEA were determined using enzyme-linked immunosorbent assays platforms. Individual assessment of levels of ccfn showed area under the receiver operating characteristic curve (AUCROC) = 0.525–0.576 in discrimination of individuals with CRC from individuals with non-malignant findings. Predictor models including ccfn containing 5-methylcytosine DNA, CEA, age and gender improved results (AUCROC = 0.736, sensitivity = 0.37 at specificity = 0.90). Further improvement was achieved in discrimination of individuals with CRC from individuals with clean colorectum (AUCROC = 0.840, sensitivity = 0.57 at specificity = 0.90). The levels of ccfn among patients with CRC appeared to be stage-independent. In conclusion, the performance of the developed predictor models is potentially promising in early detection of CRC.
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