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

8 Scopus citations


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
Issue number1
StatePublished - 12 Oct 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s).


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


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