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Related Concept Videos

  1. Home
  3. Plasma-based Untargeted Metabolomics Reveals Potential Biomarkers For Screening And Distinguishing Of Ovarian Tumors.
  1. Home
  3. Plasma-based Untargeted Metabolomics Reveals Potential Biomarkers For Screening And Distinguishing Of Ovarian Tumors.

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Plasma-based untargeted metabolomics reveals potential biomarkers for screening and distinguishing of ovarian tumors.

Shen Peng1, Yiming Zhu2, Jing Zhu3

  • 1College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|March 19, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Plasma metabolomics can identify biomarkers for ovarian tumor screening. Specific metabolites like TMAO and hippuric acid effectively differentiate ovarian tumors from healthy controls, aiding early detection.

Keywords:
KynurenineMetabolomicsOvarian tumorPlasma

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Area of Science:

  • Oncology
  • Biochemistry
  • Analytical Chemistry

Background:

  • Ovarian cancer (OC) is a leading cause of gynecological cancer mortality, often diagnosed at advanced stages due to asymptomatic progression.
  • Early detection and differentiation of ovarian tumors (OT) are critical for improving patient outcomes.
  • Plasma-based biomarkers offer a promising avenue for noninvasive screening and diagnosis.

Purpose of the Study:

  • To investigate plasma untargeted metabolomics for identifying biomarkers to screen and differentiate ovarian tumors (OT).
  • To assess the discriminatory capacity of identified metabolites between OC, benign ovarian tumors (BOT), and healthy controls (HC).

Main Methods:

  • Plasma samples from OC, BOT, and HC groups were analyzed using untargeted metabolomics.
  • Samples were divided into training and testing sets for statistical analysis.
  • Differential metabolites were identified using two-tailed Student's t-test and partial least squares discriminant analysis (PLS-DA).
  • Receiver operating characteristic (ROC) models were employed to evaluate the diagnostic accuracy of the biomarkers.

    • Main Results:

    • TMAO and hippuric acid demonstrated high predictive value in distinguishing OT from HC (AUC > 0.95).
    • Linoleic acid, alpha-linolenic acid, and arachidonic acid further supported OT screening (AUC > 0.9).
    • Kynurenine effectively differentiated OC from BOT (AUC = 0.808).
    • Reduced levels of specific lysophosphatidylcholines (LPC (17:0/0:0), LPC (15:0/0:0)) also distinguished OT from HC (AUC = 0.771-0.89).

    Conclusions:

    • Plasma metabolomics shows potential for noninvasive biomarker discovery in OT screening.
    • Metabolite profiles can aid in differentiating between malignant and benign ovarian tumors.
    • Further validation of metabolite quantification is necessary before clinical application.