Dataset Information

High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer.

ABSTRACT: Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC?=?0.94, PPV?=?0.92, sensitivity?=?0.85 and specificity?=?0.93 for detection of ovarian cancer stages I-IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.

SUBMITTER: Enroth S

PROVIDER: S-EPMC6586828 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer.

Enroth Stefan S Berggrund Malin M Lycke Maria M Broberg John J Lundberg Martin M Assarsson Erika E Olovsson Matts M Stålberg Karin K Sundfeldt Karin K Gyllensten Ulf U

Communications biology 20190620

Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model cons ...[more]

PMID: 31240259

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Project description:Significant inter-individual variation in terms of susceptibility to several stress-related disorders, such as myocardial infarction and Alzheimer's disease, and therapeutic response has been observed among healthy subjects. The molecular features responsible for this phenomenon have not been fully elucidated. Proteomics, in association with bioinformatics analysis, offer a comprehensive description of molecular phenotypes with clear links to human disease pathophysiology. The aim of this study was to conduct a comparative plasma proteomics analysis of glucocorticoid resistant and glucocorticoid sensitive healthy subjects and provide clues of the underlying physiological differences. For this purpose, 101 healthy volunteers were given a very low dose (0.25 mg) of dexamethasone at midnight, and were stratified into the 10% most glucocorticoid sensitive (S) (n = 11) and 10% most glucocorticoid resistant (R) (n = 11) according to the 08:00 h serum cortisol concentrations determined the following morning. One month following the very-low dose dexamethasone suppression test, DNA and plasma samples were collected from the 22 selected individuals. Sequencing analysis did not reveal any genetic defects in the human glucocorticoid receptor (NR3C1) gene. To investigate the proteomic profile of plasma samples, we used Liquid Chromatography-Mass Spectrometry (LC-MS/MS) and found 110 up-regulated and 66 down-regulated proteins in the S compared to the R group. The majority of the up-regulated proteins in the S group were implicated in platelet activation. To predict response to cortisol prior to administration, a random forest classifier was developed by using the proteomics data in order to distinguish S from R individuals. Apolipoprotein A4 (APOA4) and gelsolin (GSN) were the most important variables in the classification, and warrant further investigation. Our results indicate that a proteomics signature may differentiate the S from the R healthy subjects, and may be useful in clinical practice. In addition, it may provide clues of the underlying molecular mechanisms of the chronic stress-related diseases, including myocardial infarction and Alzheimer's disease.

Dataset Information

High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer.

Publications

High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer.

Similar Datasets

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