W Atiomo1, S Khalid, S Parameshweran, M Houda, R Layfield. 1. Department of Obstetrics and Gynaecology, School of Human Development, University of Nottingham, and Nottingham University Hospitals, Nottingham, UK. william.atiomo@nottingham.ac.uk
Abstract
BACKGROUND: The exact causes of polycystic ovary syndrome (PCOS) are uncertain, and treatment could be improved. Discovery-based approaches like 'proteomics' may result in faster insights into the causes of PCOS and improved treatment. OBJECTIVES: To identify the number and nature of proteomic biomarkers found in PCOS so far and to identify their diagnostic and therapeutic potential. SEARCH STRATEGY: All published studies on proteomic biomarkers in women with PCOS identified through the MEDLINE (1966-2008), EMBASE (1980-2008) and the ISI web of knowledge (v4.2) databases. SELECTION CRITERIA: The terms 'polycystic ovary syndrome' and 'proteomic', 'proteomics', 'proteomic biomarker' or 'proteomics biomarker' without any limits/restrictions were used. DATA COLLECTION AND ANALYSIS: Original data were abstracted where available and summarised on a separate Microsoft Excel (2007) database for analysis. MAIN RESULTS: Seventeen articles were identified, of which 6 original papers and 1 review article contained original data. Tissues investigated included serum, omental biopsies, ovarian biopsies, follicular fluid and T lymphocytes. Sample sizes ranged from 3 to 30 women. One hundred and forty-eight biomarkers were identified. The biomarkers were involved in many pathways, for example the regulation of fibrinolysis and thrombosis, insulin resistance, immunity/inflammation and the antioxidant pathway. Eleven groups of biomarkers appeared to be independently validated. The individual sensitivities for the diagnosis of PCOS were reported for 11 named biomarkers and ranged from 57 to 100%. AUTHOR'S CONCLUSIONS: Proteomic biomarker discovery in PCOS offers great potential. Current challenges include reproducibility and data analysis. The establishment of a PCOS-specific biomarker data bank and international consensus on the framework of systematic reviews in this field are required.
BACKGROUND: The exact causes of polycystic ovary syndrome (PCOS) are uncertain, and treatment could be improved. Discovery-based approaches like 'proteomics' may result in faster insights into the causes of PCOS and improved treatment. OBJECTIVES: To identify the number and nature of proteomic biomarkers found in PCOS so far and to identify their diagnostic and therapeutic potential. SEARCH STRATEGY: All published studies on proteomic biomarkers in women with PCOS identified through the MEDLINE (1966-2008), EMBASE (1980-2008) and the ISI web of knowledge (v4.2) databases. SELECTION CRITERIA: The terms 'polycystic ovary syndrome' and 'proteomic', 'proteomics', 'proteomic biomarker' or 'proteomics biomarker' without any limits/restrictions were used. DATA COLLECTION AND ANALYSIS: Original data were abstracted where available and summarised on a separate Microsoft Excel (2007) database for analysis. MAIN RESULTS: Seventeen articles were identified, of which 6 original papers and 1 review article contained original data. Tissues investigated included serum, omental biopsies, ovarian biopsies, follicular fluid and T lymphocytes. Sample sizes ranged from 3 to 30 women. One hundred and forty-eight biomarkers were identified. The biomarkers were involved in many pathways, for example the regulation of fibrinolysis and thrombosis, insulin resistance, immunity/inflammation and the antioxidant pathway. Eleven groups of biomarkers appeared to be independently validated. The individual sensitivities for the diagnosis of PCOS were reported for 11 named biomarkers and ranged from 57 to 100%. AUTHOR'S CONCLUSIONS: Proteomic biomarker discovery in PCOS offers great potential. Current challenges include reproducibility and data analysis. The establishment of a PCOS-specific biomarker data bank and international consensus on the framework of systematic reviews in this field are required.
Authors: Zeina Haoula; Srinivasarao Ravipati; Dov J Stekel; Catharine A Ortori; Charlie Hodgman; Clare Daykin; Nick Raine-Fenning; David A Barrett; William Atiomo Journal: Metabolomics Date: 2014-08-17 Impact factor: 4.290
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