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

  • Jun Hwa Lee
  • , Byong Chul Yoo
  • , Yun Hwan Kim
  • , Sun A. Ahn
  • , Seung Gu Yeo
  • , Jae Youl Cho
  • , Kyung Hee Kim
  • , Seung Cheol Kim

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalBioData Mining
Volume9
Issue number1
DOIs
StatePublished - 12 Oct 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s).

Keywords

  • MALDI-TOF mass spectrometry
  • Ovarian cancer
  • Pattern recognition
  • Screening
  • Serum profiling

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