Screening by Q Exactive liquid chromatography/tandem mass spectrometry identified Choline, 25-hydroxyvitamin D2, and SM(d18:0/16:1(9Z) (OH)) as biomarkers for high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGSOC) has a high death rate and poor prognosis. The main causes of poor prognosis are asymptomatic early disease, no effective screening method at present, and advanced disease. Changes in cellular metabolism are characteristic of cancer, and plasma metabolome analysis can be used to identify biomarkers. In this study, we used Q Exactive liquid chromatography tandem mass spectrometry (LC-MS/MS, QE) to compare the differentiation between plasma samples (22 HGSOC samples and 22 normal samples). In total, we detected 124 metabolites, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish HGSOC patients from healthy controls. Choline, 25-hydroxyvitamin D2, and sphingomyelin (d18:0/16:1(9Z) (OH))/SM(d18:0/16:1(9Z) (OH)) showed significantly differential plasma levels in HGSOC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC)  ≥ 1.5 or ≤ 0.667. Metabolic pathway analysis can provide valuable information to enhance the understanding of the underlying pathophysiology of HGSOC. In conclusion, the Q Exactive LC/MS/MS method validation-based plasma metabolomics approach may have potential as a convenient screening method for HGSOC and may be a method to monitor tumor recurrence in patients with HGSOC after surgery SIGNIFICANCE: High-grade serous ovarian cancer (HGSOC) has a high death rate and poor prognosis. The main causes of poor prognosis are asymptomatic early disease, no effective screening method at present, and advanced disease. Changes in cellular metabolism are characteristic of cancer, and plasma metabolome analysis can be used to identify biomarkers. In this study, we used Q Exactive liquid chromatography tandem mass spectrometry (LC-MS/MS, QE) to compare the differentiation between plasma samples (20 HGSOC samples and 20 normal samples). In total, we detected 124 metabolites, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish HGSOC patients from healthy controls. Choline, 25-hydroxyvitamin D2, and sphingomyelin (d18:0/16:1(9Z) (OH))/SM(d18:0/16:1(9Z) (OH)) showed significantly differential plasma levels in HGSOC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.5 or ≤ 0.667. Metabolic pathway analysis can provide valuable information to enhance the understanding of the underlying pathophysiology of HGSOC. In conclusion, the Q Exactive LC/MS/MS method validation-based plasma metabolomics approach may have potential as a convenient screening method for HGSOC and may be a method to monitor tumor recurrence in patients with HGSOC after surgery.

Genetically predicted serum testosterone and risk of gynecological disorders: a Mendelian randomization study.

Background: Testosterone plays a key role in women, but the associations of serum testosterone level with gynecological disorders risk are inconclusive in observational studies. Methods: We leveraged public genome-wide association studies to analyze the effects of four testosterone related exposure factors on nine gynecological diseases. Causal estimates were calculated by inverse variance-weighted (IVW), MR-Egger and weighted median methods. The heterogeneity test was performed on the obtained data through Cochrane's Q value, and the horizontal pleiotropy test was performed on the data through MR-Egger intercept and MR-PRESSO methods. "mRnd" online analysis tool was used to evaluate the statistical power of MR estimates. Results: The results showed that total testosterone and bioavailable testosterone were protective factors for ovarian cancer (odds ratio (OR) = 0.885, P = 0.012; OR = 0.871, P = 0.005) and endometriosis (OR = 0.805, P = 0.020; OR = 0.842, P = 0.028) but were risk factors for endometrial cancer (OR = 1.549, P < 0.001; OR = 1.499, P < 0.001) and polycystic ovary syndrome (PCOS) (OR = 1.606, P = 0.019; OR = 1.637, P = 0.017). dehydroepiandrosterone sulfate (DHEAS) is a protective factor against endometriosis (OR = 0.840, P = 0.016) and premature ovarian failure (POF) (OR = 0.461, P = 0.046) and a risk factor for endometrial cancer (OR= 1.788, P < 0.001) and PCOS (OR= 1.970, P = 0.014). sex hormone-binding globulin (SHBG) is a protective factor against endometrial cancer (OR = 0.823, P < 0.001) and PCOS (OR = 0.715, P = 0.031). Conclusion: Our analysis suggested causal associations between serum testosterone level and ovarian cancer, endometrial cancer, endometriosis, PCOS, POF.

Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS).

BACKGROUND: Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. The poor prognosis of EOC is mainly due to its asymptomatic early stage, lack of effective screening methods, and a late diagnosis in the advanced stages of the disease. OBJECTIVE: This study investigated metabolomic abnormalities in epithelial ovarian cancers. METHODS: Our study developed a novel strategy to rapidly identify the metabolic biomarkers in the plasma of the EOC patients using Internal Extraction Electrospray Ionization Mass Spectrometry (IEESI-MS) and Liquid Chromatography-mass Spectrometry (HPLC-MS), which could distinguish the differential metabolites in between plasma samples collected from 98 patients with epithelial ovarian cancer, including 78 cases with original (P), and 20 cases with self-configuration (ZP), as well as 60 healthy subjects, including 30 cases in the original sample (H), 30 cases in self-configuration (ZH), and 6 cases in a blind sample (B). RESULTS: Our study detected 880 metabolites based on criteria variable importance in projection (VIP) > 1, among which 26 metabolites were selected for further identification. They are mainly metabolism-related lipids, amino acids, nucleic acids, and others. The metabolic pathways associated with the differential metabolites were explored by the KEGG analysis, a comprehensive database that integrates genome, chemistry, and system function information. The abnormal metabolites of EOC patients identified by IEESI-MS and HPLC-MS included Lysophosphatidylcholine (16:0) [Lyso PC (16:0)], L-Phenylalanine, L-Leucine, Phenylpyruvic acid, L-Tryptophan, and L-Histidine. CONCLUSIONS: Identifying the abnormal metabolites of EOC patients through metabolomics analyses could provide a new strategy to identify valuable potential biomarkers for the screening and early diagnosis of EOC.