Correlation analysis of urine metabolites and clinical staging in patients with ovarian cancer

  • Ting Jiang
  • , Yunliang Lin
  • , Haiqin Yin
  • , Shanshan Wang
  • , Qinglei Sun
  • , Peihai Zhang
  • , Wenxiang Bi*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study is to investigate the correlation between urine metabolites and clinical staging in patients with ovarian cancer. The urina sanguinis from 56 cases of primary epithelial ovarian cancer patients and 15 healthy volunteers was collected and the urine metabolites were extracted. Ultra high performance liquid chromatography/time-of-flight mass spectrometry (UPLC-Q-TOF-MS) analysis was performed. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyze the mass spectrometry data. Database retrieval and comparison of the screened metabolites were performed and one-way ANOVA and least significant dif­ference (LSD) t test were carried out. PCA analysis of UPLC-Q-TOF-MS results showed that the score plots of samples from healthy people and patients with ovarian cancer at different clinical stages were separated. Further PLS-DA analysis significantly improved the classification results. The R2X was 0.757, the R2Y was 0.977 and the Q2Y was 0.87, indicating that the model stability and predictability were good. Eight metabolites, including N-acetylneuraminic acid-9-phosphate, 5’-methioadenosine, uric acid-3-nucleoside, pseudouridine, L-valine, succinic acid, L-proline and β-nicotinamide mononucleotide were identified. The contents of these metabolites increased with the development of the disease. There was correlation between urine metabolites and clinical staging in patients with ovarian cancer.

Original languageEnglish
Pages (from-to)18165-18171
Number of pages7
JournalInternational Journal of Clinical and Experimental Medicine
Volume8
Issue number10
Publication statusPublished - 30 Oct 2015
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Metabonomics
  • Ovarian cancer
  • Staging
  • Ultra high performance liquid chromatography/time-of-flight mass spectrometry
  • Urine

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