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Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors.

ABSTRACT: Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ovarian tumor.Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls.Global lipidomics analysis identified 34 metabolites (of 372 assessed) differing significantly (P<0.05) between cases and controls in both training and testing sets, with 17 candidates satisfying FDR q<0.05, and two reaching Bonferroni significance. Targeted profiling of ~150 aqueous metabolites identified a single amino acid, alanine, as differentially abundant (P<0.05). A multivariate classification model built using the top four lipid metabolites achieved an estimated AUC of 0.85 (SD=0.07) based on Monte Carlo cross validation. Evaluation of a hybrid model incorporating both CA125 and lipid metabolites was suggestive of increased classification accuracy (AUC=0.91, SD=0.05) relative to CA125 alone (AUC=0.87, SD=0.07), particularly at high fixed levels of sensitivity, without reaching significance.Our results provide insight into metabolic changes potentially correlated with the presence of serous OC versus benign ovarian tumor and suggest that plasma metabolites may help differentiate these two conditions.

SUBMITTER: Buas MF

PROVIDER: S-EPMC5310763 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors.

Buas Matthew F MF Gu Haiwei H Djukovic Danijel D Zhu Jiangjiang J Drescher Charles W CW Urban Nicole N Raftery Daniel D Li Christopher I CI

Gynecologic oncology 20151030 1

<h4>Objective</h4>Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ...[more]

PMID: 26521694

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Project description: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.

Dataset Information

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors.

Publications

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors.

Similar Datasets

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