4.7 Article

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 21, Issue 9, Pages 2146-2159

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.2c00218

Keywords

serum biomarker; high-grade serous ovarian cancer; proteomics; mass spectrometry; data-independent acquisition

Funding

  1. Bertis Inc.
  2. Korea Basic Science Institute (KBSI) National Research Facilities and Equipment Center - Korean government (Ministry of Education) [2019R1A6C1010028]
  3. National Research Foundation of Korea [2019R1A6C1010028] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study identified potential diagnostic biomarkers for HGSOC using liquid chromatography data-independent acquisition tandem mass spectrometry. The findings revealed activations and suppressions of specific signaling pathways in HGSOC, and validation in an independent cohort showed promising sensitivity and specificity. Functional assays on selected biomarkers suggested their roles in cancer cell proliferation and migration in HGSOC.
High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor beta signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

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