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Keywords:

  • 2DE;
  • 2D-DIGE;
  • Biomarker;
  • MALDI-TOF;
  • ovary;
  • PCOS;
  • polycystic;
  • proteomic;
  • RP-SPE;
  • SELDI;
  • syndrome

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

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.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Traditional hypothesis-driven approaches to the understanding of complex disease pathology have resulted in significant insights into the mechanisms and management of some conditions in obstetrics and gynaecology.1,2 However, progress appears to be slow as the complexity of some gynaecological disorders means that a step-by-step hypothesis-driven approach does not always capture the entire complexity of the condition being studied in the desired time frame. The emerging field of discovery-driven ‘omics’ approaches offers much promise and has been applied to some complex conditions in obstetrics and gynaecology. These approaches include genomics, proteomics and metabolomics and allow the study of several genes, proteins and metabolites in one single experiment using novel molecular biology techniques. This paper reviews the application of the ‘proteomics’ approach to a common complex disorder in gynaecology, polycystic ovary syndrome (PCOS), and uses it as a framework to highlight the future potential and challenges of the application of discovery-driven approaches to the understanding of complex disorders.

PCOS affects the quality of life of many women in the UK. The clinical problems include anovulatory infertility, menstrual disorders, hirsutism, obesity and an increased risk of type II diabetes, cardiovascular disease and endometrial cancer in later life.3 It is present in 5–10% of women in the UK population and the current estimated annual costs of diagnosing and treating infertility secondary to PCOS range from £16 to £22 million pounds (Based on an average cost of diagnosing PCOS of £417 per women, treatment costs that range from £1233 to £1805 [Hum Reprod 2000;15:95–106] and an annual UK incidence of infertility due to PCOS of 180 per million population [Gynecol Endocrinol 1987;1:235–45.]). This figure excludes the costs of managing obesity (current NHS annual costs of £2.5 billion), type II diabetes and endometrial cancer. The health economic impact of PCOS in the USA has been estimated at up to $1.77 billion.4 Although significant advances have been made in the understanding, diagnosis and treatment of PCOS, there are currently still challenges with the diagnosis, understanding of the pathophysiology and prediction of response to therapy and risk stratification. For example, the current diagnosis of PCOS is one of exclusion of other androgenic-based diseases following clinical identification of at least two of the following three criteria: chronic oligo-/anovulation, clinical and/or biochemical signs of hyperandrogenism and ultrasound evidence of polycystic ovaries.5 However, relative weightings of each criterion tend to vary depending on the clinician’s medical background. Ultrasound scans are also operator dependent and can be expensive.

Recent developments in the emerging field of proteomic technologies provide an opportunity to address some of the current challenges associated with the diagnosis and treatment of PCOS. Proteomic technologies involve experimental approaches that allow the study of the ‘proteome’ or total protein or peptide complement of a given sample, especially because proteins are involved in almost all biological processes. Proteomic approaches have been used in the study of other diseases such as multiple sclerosis,6 acute and chronic kidney disease7 and sporadic Creutzfeldt–Jakob disease.8 The specific potential in PCOS includes the development of biomarkers that could predict which women with PCOS would go on to develop the known metabolic complications and endometrial cancer and identification of women who would respond easily to treatment such as diet and exercise for obesity and ovulation induction for infertility. In clinical practice, this would allow a more rational approach to treatment, and in clinical trials, this would reduce the numbers needed to treat. Proteomic biomarkers also offer the potential of a less invasive (compared to vaginal ultrasound scans) and more precise way of diagnosing PCOS. The aims of this systematic review were to evaluate the progress made so far with the application of proteomic approaches to PCOS, to highlight any significant novel insights and to outline challenges to be addressed in future research. Specifically, we aimed to identify the number and nature of proteomic biomarkers found in PCOS so far and to identify their diagnostic and therapeutic potential.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Studies eligible for review

Studies were eligible if they were retrospective or prospective, case–control cohort studies, cross-sectional or randomised controlled studies containing original data on proteomic biomarkers in the diagnosis or risk stratification of PCOS.

Finding relevant studies

The MEDLINE (1966–June 2008), EMBASE (1980–June 2008) and the ISI web of knowledge (v4.2) databases were searched using the terms ‘polycystic ovary syndrome’ and ‘proteomic’, ‘proteomics’, ‘proteomic biomarker’ or ‘proteomics biomarker’ without any limits/restrictions. All studies obtained as a result of the search were reviewed. The original PDFs of studies obtained from the search were located through direct online links to the files from the search results, for example through ‘Science Direct’ or through indirect links provided by the electronic library resource gateway of the University of Nottingham. A manual search of references from all the studies was also conducted to identify any other potentially relevant studies. The search criterion ended in June 2008. The search findings were independently double-checked by a second author (R.L.).

Data abstraction

Studies identified from the different databases were initially saved separately on Microsoft Excel 2007. These results were then merged and sorted to enable the identification and removal of duplicated search results. Full articles and abstracts derived as a result of the search were read, and original data were abstracted where available and summarised on a separate database. The variables abstracted included the name of the biomarker identified in the study or m/z value (The m/z value or mass/charge ratio is a number used in mass spectrometry to define how a particle/ion [in proteomics this refers to protein or peptide ions] in a sample responds to an electric or magnetic field, which is calculated by dividing the mass of the ion by its charge. It is a useful way of identifying and quantifying unknown compounds in a sample) where this was the only information provided, whether the biomarker was over- or underexpressed in PCOS, the tissue of origin, number of women studied, sensitivity of the biomarker, proteomic technique used and physiological or pathological role in PCOS suggested by the authors.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

A total of 17 individual articles were obtained from the literature review with the first published in 2003 (Figure 1). Nine of these contained primary data as indicated in their abstracts,9–17 one was a review article18 containing original data on one biomarker, five were review articles19–23 with no original data and the other two24,25 discussed PCOS but had original data on healthy controls. Of the nine original studies, the abstracts of two articles15,17 in Chinese appeared to contain the data that overlapped with a full English article in an American journal,16 so this paper was reviewed as the identities of the biomarkers could not be reliably established from the abstracts of the articles where the full paper was in Chinese. The abstract of a further paper12 was from conference proceedings, which the same group later published in a British journal.13 Information on biomarkers from six papers containing original data and from one review article was therefore summarised (Table 1).

image

Figure 1. Classification of articles reviewed in systematic review.

Download figure to PowerPoint

Table 1.  Six publications containing original PCOS biomarker data that were analysed
AuthorTarget tissueProteomic techniqueSample size
  1. 2D-DIGE, two-dimensional in gel electrophoresis; NR, not recorded.

Borro et al.9T lymphocytes2DE10
Choi et al.10Follicular fluid2DENR
Corton et al.11Omental biopsies (adipose tissue)2D-DIGE5
Ma et al.13Ovarian biopsies2DE3
Zhao et al.16SerumSELDI30 insulin-resistant and 30 non-insulin-resistant PCOS women
Matharoo-Ball et al.14Serum2DE, RP-SPE and MALDI-TOF5, 6 and 12

The size of the population in these studies studied ranged from 3 to 30 women with PCOS with a median of 6. All studies had a control group. The biomarkers were evaluated in a variety of target tissues, and these included serum in two papers, omental biopsies in one paper, ovarian biopsies in one paper, follicular fluid in one paper and T lymphocytes in the final paper (Table 1).

Proteomic techniques used

In general, proteomic techniques involve separating proteins and peptides present in a complex sample by subjecting the sample to electrophoresis in a gel or in non-gel-based techniques; molecules are fragmented into charged particles by exposing them to an ion source in a mass spectrometer.26 In gel-based techniques, this complex mixture of proteins is subjected to electrophoresis in ‘two-dimensions’, which simply means that the proteins are electrophoresed first in one direction and then in another. This allows small differences in a protein to be visualised by separating a modified protein from its unmodified form. This methodology is known as ‘two-dimensional gel electrophoresis’.27 In non-gel-based techniques, such as surface-enhanced laser desorption/ionisation mass spectrometry (SELDI MS) or matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF MS), a mass analyzer sorts the fragmented charged particles by their masses by applying electric and magnetic fields, and a detector measures the value of some indicator quantity and thus provides data for calculating the abundances of each ion (protein or peptide) fragment present.26

A variety of different but complementary approaches were used to collect proteomic data from the biological samples (methods reviewed in Chuthapisith et al.28 and Hughes et al.18) in this systematic review (Table 1). Briefly, these were gel-based profiling of protein expression, typically using two-dimensional gel electrophoresis (2DE) or conventional one-dimensional sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) following sample fractionation, for example by a reversed-phase–solid-phase extraction (RP-SPE) approach. An advance of the former, the technique of two-dimensional in gel electrophoresis, where multiple biological samples are profiled within the same 2D gel (2D-DIGE) (following selective labelling of proteins prior to electrophoresis), which reduces gel-to-gel variability, was also used. In addition, non-gel mass spectrometry (MS)-based profiling of peptides or proteins within biological samples, SELDI MS or MALDI-TOF MS, was in some cases used to identify biomarkers.

A total of 153 named proteomic biomarkers were identified from these studies, of which 41 were downregulated and 112 upregulated (Table S1) compared with controls without PCOS. Six biomarkers were identified in follicular fluid, 8 in omental biopsies, 70 in ovarian biopsies, 58 in serum and 11 from T lymphocytes. However, five pairs of serum biomarkers were highlighted twice by the same research group, so 148 separate biomarkers have actually been identified.

The functions/roles of the biomarkers identified and discussed by the authors included regulation of fibrinolysis and thrombosis, insulin resistance, glucose metabolism, regulation of the immune response, the antioxidant pathway, lipoprotein metabolism, apoptosis, angiogenesis, cholesterol transport, regulation of the cytoskeleton structure, fibrosis and collagen metabolism, homocysteine removal and protein transport (Table S2).

Biomarkers involved in the antioxidant pathway were identified in three separate studies.9,11,13 These biomarkers include peroxiredoxin-1, peroxiredoxin 2 isoform-a, glutathione S-transferase M3 (GSTM3) and heat-shock 27-kDa protein (HSP 27). Peroxiredoxin-1 is an antioxidant enzyme thought to be involved in controlling the redox cellular state and was found to be upregulated in T lymphocytes, perhaps representing differentiating steps of the immune reaction to PCOS.9 Peroxiredoxin 2 isoform-a has antioxidant activity, and it was downregulated in omental biopsies. It was suggested that downregulation of this enzyme probably reflected an increase in the concentration of hydrogen peroxide (H2O2) in PCOS fat cells, thus damaging the DNA. It was also suggested that DNA damage induced by H2O2 may explain the increased endometrial cancer susceptibility in PCOS women.11 GSTM3, which was upregulated in omental biopsies,11 is an antioxidant enzyme involved in the degradation of cytotoxic products in the cell and could be implicated in the biosynthesis of leucotrienes, prostaglandins, testosterone and progesterone.11 It also performs a cytoprotective function through detoxification of lipid peroxidation products in adipocytes.11 HSP 27, which was downregulated in ovarian biopsies,13 was thought to suppress reactive oxygen species generation.

Alterations in the immune state/inflammatory response in women with PCOS also appeared to be an emerging theme from these studies. Biomarkers related to this pathway were identified in three separate studies.9,10,14 In one of these studies,14 biomarkers including complement C4α3c chain, C4γ chain and haptoglobin were identified by 2DE and RP-SPE combined with SDS–PAGE as being differentially expressed in PCOS, with complement C4α4c chain upregulation, consistent with breakdown products of complement C4, found on subsequent Western blotting validation experiments. Other biomarkers directly or indirectly linked to inflammation and the immune response were the Raf kinase inhibitor protein (RKIP), Rho guanosine diphosphate (GDP)-dissociation inhibitor-1, peroxiredoxin-1, F-actin-capping protein alpha-1 subunit, Cofilin-1 and Annexin V.9,11

The individual sensitivities for the diagnosis of PCOS were reported for 11 named biomarkers, and they ranged from 57 to 100% (Table 2).

Table 2.  Biomarkers with sensitivities reported
Biomarker (m/z value or name)Overexpressed (+) or underexpressed (−)Sensitivity (%)Site foundProteomic technique usedReference
  1. IR, insulin resistant.

1351+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
1977+100SerumMALDI-TOFMatharoo-Ball, et al.14
2924+100SerumMALDI-TOFMatharoo-Ball, et al.14
3025+100SerumMALDI-TOFMatharoo-Ball, et al.14
6871+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
8668+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
8673+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
8674+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
8727+90.4SerumMALDI-TOFMatharoo-Ball, et al.14
apolipoprotein C-I+56.67Serum from IR PCOS womenSELDIZhao, et al.16
apolipoprotein C-I+73.73Serum from non-IR PCOS womenSELDIZhao, et al.16

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

This systematic review of the application of the emerging field of discovery-driven approaches (in this case proteomics) to the understanding of complex disorders such as PCOS has highlighted some of the potential and challenges of this approach. On the one hand, while validating some already established themes thought to underpin PCOS such as insulin resistance, the fibrinolytic/thrombotic system, lipid metabolism and apoptosis, some relatively new themes worthy of further focused hypothesis-driven research were identified including the antioxidant pathway and the immune system. On the other hand, the variety of proteomic techniques used, variety of tissues studied, small sample sizes (3–30) and the large number and functions of biomarkers identified highlight the significant challenges of data analysis and interpretation that lie ahead as this emerging field evolves.

The biomarkers identified in the studies in this systematic review may contribute towards a better understanding of the heterogeneity of PCOS and the current clinical challenges in the understanding of the pathophysiology, treatment and long-term health risks. The pathology of PCOS has, for example, been attributed to insulin resistance, genetics, fetal programming, abnormal steroidogenesis and environmental factors,29 but no single unifying hypothesis has been identified. Biomarkers linked to oxidative stress were a relatively new development in the pathophysiology of PCOS identified in this systematic review. Although oxidative stress has been linked to PCOS in previous studies,30–32 it has not been extensively investigated for its potential role in the pathophysiology of PCOS compared, for example, with the role of insulin resistance. Biomarkers of oxidative stress may help predict which women with PCOS will go on to develop endometrial cancer as is has been suggested that DNA damage induced by H2O2 may explain the increased endometrial cancer susceptibility in PCOS women.11 However, this requires further research.

In the treatment of PCOS, lifestyle intervention such as weight loss and exercise are currently key strategies in the management of these women, but not all women will respond to these strategies with some women finding weight loss easier than others.33 The prediction of response to ovulation induction is also a challenge.34 The link to inflammation in some of the proteomic studies identified from our systematic review warrants further research as this pathway may help address some of these therapeutic challenges. Previous studies have linked PCOS with inflammation,35 and inflammation has also been linked with insulin resistance, obesity and coronary heart disease, all of which are associated with PCOS. There are also pre-existing reports of associations between the complement pathway and infertility,36 autoimmunity,37 coronary heart disease,38 insulin resistance and type I diabetes.39 Inflammatory biomarkers may therefore help predict which women with PCOS will would go on to develop the metabolic complications and endometrial cancer and may be response to therapy; however, again, this warrants further focused hypothesis-driven research.

The strengths of this systematic review were that it was the first time, a systematic approach was applied to the capture and evaluation of the data from proteomic biomarker research in PCOS, no original data were excluded from the analysis and the table of biomarkers provides a useful framework for the development of future databases of proteomic biomarkers identified in PCOS. In addition, some pathways worthy of further research were identified. The study was, however, limited by the relatively small number of sample sizes of the original studies, the variety of proteomic techniques used and the variety of tissues studies. The lack of reproducibility of many of the biomarkers in independent studies did not allow the application of some of the formal quantitative techniques often used in a systematic review of clinical trials. The need for consensus guidelines for designing clinical proteomic analyses have been noted;40 however, sample sizes varied considerably in the different studies analysed, and although there are currently no agreed minimum sample sizes for proteomic analyses, reliability of data is clearly likely to increase with sample size as biological variation becomes less significant. Reproducibility was also an issue. For example, the fact that of the 148 biomarkers identified, only 11 sets of biomarkers appeared to be independently validated by separate research groups (data not shown) emphasises the need for replication studies under standardised conditions. In this context, for large-scale datasets such as proteomic biomarker data, adoption of generalised meta-analysis methods will be vital to properly combine results from multiple studies, as has been proposed for microarray data.41

Despite the limitations of this systematic review that reflect the relative infancy of the application of the emerging field of proteomic techniques to PCOS, proteomics still offers the potential for improved understanding and management of PCOS. There is, however, a need for wider collaboration (interdisciplinary and international) and standardisation in future research studies to realise the potential of proteomic biomarker discovery in PCOS. Some of the specific needs of the collaboration are the need to develop a framework to adequately capture and summarise the data published from future studies and agree a framework for systematic reviews. This might, for example, involve the establishment of a novel open access ‘PCOS’-specific data bank where researchers would deposit the results of their findings to facilitate the mining and analysis of global research findings using bioinformatic techniques. The table of biomarkers proteomic biomarkers identified in PCOS provided in this systematic review might provide a useful benchmark.

In conclusion, this systematic review of the application of the emerging field of proteomic technologies to the understanding of PCOS found that 148 biomarkers derived from a variety of tissues were identified from 6 original studies. Only 11 sets of biomarkers appeared to be independently validated by separate research groups. The functions of some of the biomarkers identified included regulation of fibrinolysis and thrombosis, insulin resistance, glucose metabolism, immune response, the antioxidant pathway, lipoprotein metabolism, apoptosis, angiogenesis, cholesterol transport, the cytoskeleton structure, fibrosis and collagen metabolism, homocysteine removal and protein transport. There were, however, limitations in the studies as the sample sizes were small (3–30), a variety of tissues were studied and a variety of proteomic techniques used. Therefore, there is a need to develop a critical mass of collaborating international and cross-disciplinary research scientists to enable standardisation, structured collection of larger original datasets, the development of an open access data bank and the agreement of a framework for systematic reviews. A structural (but not financial) framework for the type of collaboration required might be the recently initiated Geneva Large Hadron Collider project.42,43

Disclosure of interests

W.A. and R.L. are named applicants on patents of PCOS proteomic biomarkers.

Contribution to authorship

W.A. conducted and supervised the literature review, summarised and analysed the data and wrote and edited the drafts of the paper. S.K., S.P. and M.H. participated in the initial conception of the idea, participated in the literature review and the edition of the final draft of the paper. Dr R.L. participated in the generation of original data from the Nottingham-based proteomic studies in women with PCOS and independently double-checked the results of the literature search and edited the final draft of the paper.

Details of ethics approval

Not applicable as this study did not involve direct patient intervention.

Funding

No funding was obtained for this study.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We thank the staff at the University of Nottingham Library for guidance on the use of the electronic gateway. Dr A Sharma, MRCOG, for her comments on the final draft of the paper.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Additional Supporting Information may be found in the online version of this article:

Table S1. Proteomic biomarkers over- or underexpressed in women with polycystic ovary syndrome.

Table S2. Biomarker functions discussed in publications.

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FilenameFormatSizeDescription
BJO_2041_sm_TableS1.doc329KSupporting info item
BJO_2041_sm_TableS2.doc74KSupporting info item

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