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Low-mass-ion discriminant equation (LOME) for ovarian cancer screening
- Lee, Jun Hwa;
- Yoo, Byong Chul;
- Kim, Yun Hwan;
- Ahn, Sun-A;
- Yeo, Seung-Gu;
- 외 3명
WEB OF SCIENCE
9SCOPUS
11초록
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.
키워드
- 제목
- Low-mass-ion discriminant equation (LOME) for ovarian cancer screening
- 저자
- Lee, Jun Hwa; Yoo, Byong Chul; Kim, Yun Hwan; Ahn, Sun-A; Yeo, Seung-Gu; Cho, Jae Youl; Kim, Kyung-Hee; Kim, Seung Cheol
- 발행일
- 2016-10-12
- 유형
- Article
- 저널명
- BioData Mining
- 권
- 9
- 언어
- ENG
- 출판사
- BioMed Central
- 발행국가
- 영국
- ISSN
- P 1756-0381