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An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer
Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. (Centre for Personalized Medicine, Linköping University, Linköping, Sweden)
Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Yonsei Univ, South Korea. (Centre for Personalized Medicine, Linköping University, Linköping, Sweden)
Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. (Centre for Personalized Medicine, Linköping University, Linköping, Sweden)
Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. (Centre for Personalized Medicine, Linköping University, Linköping, Sweden)
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 15575Article in journal (Refereed) Published
Abstract [en]

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 9, article id 15575
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
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URN: urn:nbn:se:liu:diva-162064DOI: 10.1038/s41598-019-51999-9ISI: 000493276600006PubMedID: 31666584OAI: oai:DiVA.org:liu-162064DiVA, id: diva2:1370983
Note

Funding Agencies|Swedish Cancerfoundation; Regional Hospitalfunding (ALF); Foundation of Clinical Cancer Research, Jonkoping, Sweden [FUTURUM-809641]

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2020-01-09

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Gawel, DanutaJung Lee, Eun JungLi, XinxiuLilja, SandraSchäfer, SamuelStenmarker, MargarethaZhang, HuanBenson, Mikael
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Division of Children's and Women's healthFaculty of Medicine and Health SciencesDepartment of Clinical and Experimental MedicineH.K.H. Kronprinsessan Victorias barn- och ungdomssjukhus Linköping/Motala
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