Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets. Graphical Abstract Highlights • We aimed to investigate novel, blood-based prognostic biomarkers in HGSOC. • MS-based label-free quantification was conducted using frozen plasma samples. • Candidate biomarkers were validated with an independent set of samples via ELISA. • Plasma GSN was identified as an independent poor prognostic biomarker for PFS. • We successfully developed models predicting the 18-month PFS rate for clinical use. In Brief To investigate novel, prognostic protein biomarkers, we conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed high-grade serous ovarian carcinoma. Candidate biomarkers underwent validation with an independent set of plasma samples via ELISA. By combining clinical factors and ELISA results, we successfully developed models and nomograms to predict the 18-month progression-free survival rate for clinical use.
【저자키워드】 proteomics, Prognosis, enzyme-linked immunosorbent assay, FDR, false discovery rate, CI, Confidence interval, Ovarian neoplasms, PARP, poly(ADP-ribose) polymerase, MS, mass spectrometry, GO, Gene ontology, aHR, adjusted hazard ratio, OS, overall survival, PFS, progression-free survival, AUC, area under the receiver operating characteristic curve, FIGO, Federation of Gynecology and Obstetrics, GSN, gelsolin, HGSOC, high-grade serous ovarian carcinoma, high-grade serous carcinoma, iBAQ, intensity-based absolute quantification, IDS, interval debulking surgery, NAC, neoadjuvant chemotherapy, PDS, primary debulking surgery, PRMT1, protein arginine methyltransferase 1, SIGLEC14, sialic acid–binding Ig-like lectin 14, SND1, staphylococcal nuclease, tudor domain containing 1, VCAN, versican,