TY - JOUR
T1 - Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry
AU - Huh, Sunghyun
AU - Kang, Chaewon
AU - Park, Ji Eun
AU - Nam, Dowoon
AU - Kim, Se Ik
AU - Seol, Aeran
AU - Choi, Kyerim
AU - Hwang, Daehee
AU - Yu, Myeong Hee
AU - Chung, Hyun Hoon
AU - Lee, Sang Won
AU - Kang, Un Beom
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/9/2
Y1 - 2022/9/2
N2 - 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 β 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).
AB - 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 β 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).
KW - data-independent acquisition
KW - high-grade serous ovarian cancer
KW - mass spectrometry
KW - proteomics
KW - serum biomarker
UR - http://www.scopus.com/inward/record.url?scp=85136271720&partnerID=8YFLogxK
U2 - 10.1021/acs.jproteome.2c00218
DO - 10.1021/acs.jproteome.2c00218
M3 - Article
C2 - 35939567
AN - SCOPUS:85136271720
SN - 1535-3893
VL - 21
SP - 2146
EP - 2159
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 9
ER -
