title: Low-mass-ion discriminant equation (LOME) for ovarian cancer screening.
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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
authors.forename: Jun Hwa
authors.initials: JH
authors.lastname: Lee
authors.affiliation: Colorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
authors.forename: Byong Chul
authors.initials: BC
authors.lastname: Yoo
authors.affiliation: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
authors.forename: Yun Hwan
authors.initials: YH
authors.lastname: Kim
authors.affiliation: Colorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
authors.forename: Sun-A
authors.initials: SA
authors.lastname: Ahn
authors.affiliation: Department of Radiation Oncology, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea.
authors.forename: Seung-Gu
authors.initials: SG
authors.lastname: Yeo
authors.affiliation: Department of Genetic Engineering, Sungkyunkwan University, Suwon, Gyeonggi Republic of Korea.
authors.forename: Jae Youl
authors.initials: JY
authors.lastname: Cho
authors.affiliation: Colorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.|Omics Core Laboratory, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
authors.forename: Kyung-Hee
authors.initials: KH
authors.lastname: Kim
authors.affiliation: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
authors.forename: Seung Cheol
authors.initials: SC
authors.lastname: Kim
keywords: MALDI-TOF mass spectrometry; Ovarian cancer; Pattern recognition; Screening; Serum profiling