22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Low-mass-ion discriminant equation (LOME) for ovarian cancer screening

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          A low-mass-ion discriminant equation (LOME) was constructed to investigate whether systematic low-mass-ion (LMI) profiling could be applied to ovarian cancer (OVC) screening.

          Results

          Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry was performed to obtain mass spectral data on metabolites detected as LMIs up to a mass-to-charge ratio ( m/z) of 2500 for 1184 serum samples collected from healthy individuals and patients with OVC, other types of cancer, or several types of benign tumor. Principal component analysis-based discriminant analysis and two search algorithms were employed to identify discriminative low-mass ions for distinguishing OVC from non-OVC cases. OVC LOME with 13 discriminative LMIs produced excellent classification results in a validation set (sensitivity, 93.10 %; specificity, 100.0 %). Among 13 LMIs showing differential mass intensities in OVC, 3 metabolic compounds were identified and semi-quantitated. The relative amount of LPC 16:0 was somewhat decreased in OVC, but not significantly so. In contrast, D,L -glutamine and fibrinogen alpha chain fragment were significantly increased in OVC compared to the control group ( p = 0.001 and 0.002, respectively).

          Conclusion

          The present study suggested that OVC LOME might be a useful non-invasive tool with high sensitivity and specificity for OVC screening. The LOME approach could enable screening for multiple diseases, including various types of cancer, based on a single blood sample. Furthermore, the serum levels of three metabolic compounds— D,L -glutamine, LPC 16:0 and fibrinogen alpha chain fragment—might facilitate screening for OVC.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13040-016-0111-7) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: found

          Metabolic reprogramming in cancer: unraveling the role of glutamine in tumorigenesis.

          Increased glutaminolysis is now recognized as a key feature of the metabolic profile of cancer cells, along with increased aerobic glycolysis (the Warburg effect). In this review, we discuss the roles of glutamine in contributing to the core metabolism of proliferating cells by supporting energy production and biosynthesis. We address how oncogenes and tumor suppressors regulate glutamine metabolism and how cells coordinate glucose and glutamine as nutrient sources. Finally, we highlight the novel therapeutic and imaging applications that are emerging as a result of our improved understanding of the role of glutamine metabolism in cancer. Copyright © 2012 Elsevier Ltd. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Clinical practice. Screening for ovarian cancer.

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              A Review of Applications of Metabolomics in Cancer

              Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.
                Bookmark

                Author and article information

                Contributors
                junhwalee@ewha.ac.kr
                yoo_akh@ncc.re.kr
                medok74@gmail.com
                70389@ncc.re.kr
                md6630@daum.net
                jaecho67@gmail.com
                kyunghee@ncc.re.kr
                onco@ewha.ac.kr
                Journal
                BioData Min
                BioData Min
                BioData Mining
                BioMed Central (London )
                1756-0381
                12 October 2016
                12 October 2016
                2016
                : 9
                : 32
                Affiliations
                [1 ]Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
                [2 ]Colorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea
                [3 ]Department of Radiation Oncology, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea
                [4 ]Department of Genetic Engineering, Sungkyunkwan University, Suwon, Gyeonggi Republic of Korea
                [5 ]Omics Core Laboratory, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea
                Article
                111
                10.1186/s13040-016-0111-7
                5059959
                016d7452-be22-45d8-a7fa-a52160e29d62
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 July 2016
                : 30 September 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003625, Ministry of Health and Welfare;
                Award ID: HI12C0050
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Bioinformatics & Computational biology
                ovarian cancer,screening,serum profiling,maldi-tof mass spectrometry,pattern recognition

                Comments

                Comment on this article