Figures
Abstract
Background
Preterm Birth (PTB) is a major cause of neonatal mortality and morbidity. Women with Polycystic Ovary Syndrome (PCOS) are at high risk of PTB. There is a need for research studies to investigate the mechanisms linking PCOS and PTB, to facilitate screening, and develop novel preventative strategies.
Objective
To list all the proteomic biomarkers of PTB and integrate this list with the PCOS biomarker database to identify commonly expressed biomarkers of the two conditions.
Search Strategy
A systematic review of PTB biomarkers and update of PCOS biomarker database. All eligible published studies on proteomic biomarkers for PTB and PCOS identified through various databases were evaluated.
Selection Criteria
For the identification of the relevant studies, the following search terms were used: “proteomics”, “proteomic”, “preterm birth”, “preterm labour”, “proteomic biomarker” and “polycystic ovary syndrome”. This search was restricted to humans only
Data Collection and Analysis
A database on proteomic biomarkers for PTB was created while an already existing PCOS biomarker database was updated. The two databases were integrated and biomarkers that were co-expressed in both women with PCOS and PTB were identified and investigated.
Results
A panel of six proteomic biomarkers was similarly differentially expressed in women with PTB and women with PCOS compared to their respective controls (normal age-matched women in the case of PCOS studies and women with term pregnancy in the case of PTB studies). These biomarkers include Pyruvate kinase M1/M2, Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin.
Conclusions
These proteomic biomarkers (Pyruvate kinase M1/M2, Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin) can be potentially used to better understand the pathophysiological mechanisms linking PCOS and PTB. This would help to identify subgroups of women with PCOS at risk of PTB and hence the potential of developing preventative strategies.
Citation: Galazis N, Docheva N, Nicolaides KH, Atiomo W (2013) Proteomic Biomarkers of Preterm Birth Risk in Women with Polycystic Ovary Syndrome (PCOS): A Systematic Review and Biomarker Database Integration. PLoS ONE 8(1): e53801. https://doi.org/10.1371/journal.pone.0053801
Editor: Tamas Zakar, John Hunter Hospital, Australia
Received: May 17, 2012; Accepted: December 5, 2012; Published: January 29, 2013
Copyright: © 2013 Galazis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Polycystic ovary syndrome (PCOS) is a complex disorder with reproductive and metabolic consequences including infertility, oligomenorrhoea, hirsutism, acne, hyperandrogenaemia, obesity and an increased risk of hypertension, insulin resistance and Type 2 diabetes in later life [1]–[3]. Women with PCOS are also at increased risk of developing obstetrics complications including pre-eclampsia, gestational diabetes and preterm birth (PTB) [4]–[7]. A recent systematic review showed that pregnant women with PCOS were at least 2 times more likely to give birth prematurely (i.e. before the 37th of gestation) compared to controls (4).
However, the pathophysiological mechanisms underpinning the link between PCOS and PTB are not determined yet.
Various aetiologies have been suggested including the increased incidence of multiple pregnancies and nulliparity [7]. However, when these factors were accounted for and eliminated in recent meta-analyses, pregnant women with PCOS had still increased risk of giving birth prematurely [4]. The pathophysiological mechanisms involved in PTB in women with PCOS are not completely understood but it might be possible that the associated raised estrone levels, hyperinsulinaemia and the subsequent diabetic and hypertensive predispositions may act as co-factors [4], [6].
PTB, defined as birth before the 37th week of gestation, is responsible for 75% of all neonatal deaths and over half the neurological handicap in children [8]–[10]. Despite the advances in antenatal care and the availability of routine screening tests, the rate of PTB has not decreased in the past 30 years [11], mainly because of failure to identify the high-risk groups.
Proteomics is an emerging discipline which involves a large-scale study of the structure and function of proteins allowing the researcher to define protein expression changes in a single experiment [12]. An initial search of the literature through MEDLINE, EMBASE and Cochrane databases using the terms: “proteomics”, “proteomic”, “preterm labour”, “preterm birth”, and “PCOS” or “polycystic ovary syndrome”; no studies were identified where proteomic biomarkers for PTB had been specifically investigated in women with PCOS. However, there were studies where proteomic techniques had been used in the study of PTB and studies where proteomic approaches had been applied to women with PCOS. The aim of this study was therefore to systematically review the research undertaken in PTB using proteomic methodologies to create a database of potential biomarkers of PTB. By integrating this database with an already published database of PCOS biomarkers [13], we aimed to identify any biomarkers that were similarly expressed in both women with PCOS and PTB. Any biomarker common to both conditions would be investigated further.
Methods
Patient contact was not involved in this study hence Institutional Review Board approval was not necessary.
Studies Eligible for Review
MEDLINE, EMBASE and Cochrane (registered clinical trials) databases were searched using the terms “proteomics”, “proteomic” and “preterm birth” or “preterm labour”. Animal studies, those which applied proteomics to different PTB groups (eg with intra-amniotic inflammation, without inflammation etc) without comparing them to a normal-term group (the control) or which presented their results as peaks and not as named proteins were excluded.
Data Abstraction
The original PDFs of studies obtained from the search were located through direct online links to the files from the search results. A manual search of references from all the studies was also conducted to identify any other potentially relevant studies. The search ended in March 2012. The search findings were independently conducted by 2 of the authors (NG and ND). This process is also presented in Figure 1.
The Main Characteristics of the Studies
The selected studies were screened and specific study characteristics were recorded. These included: number of participants (N), type of proteomic technique used, type of sample collected in each study (eg amniotic fluid) and the selection criteria used. Finally, a list of proteins differentially expressed in women with PTB versus controls (term birth) was created (Table 1). Proteins identified in 2 or more of the primary studies are further listed on Table 2. To minimize selection bias, screening of the studies was independently performed by 2 of the co-authors after agreeing on the selection criteria (NG and ND).
Methodological Quality Assessment
The methodological quality of primary studies applying proteomics in women with PTB was determined using the QUADOMICS Tool, an adaptation of QUADAS (a quality assessment tool for use in systematic reviews of the diagnostic accuracy studies) which takes into account the particular challenges encountered in “-omics” based techniques (Figure 2) [14]. The methodologies of the studies which achieved 12/16 or more on the QUODOMICS Tool were classified as high quality (HQ) whereas those which scored 11/16 or less were classified as low quality (LQ). This quality assessment was performed independently by two of the co-authors (NG and ND).
Updating the PCOS Proteomics Database
The methods used to search for and collect the data on the PCOS proteomic database have been previously published and validated [13]. An updated literature search was performed on MEDLINE, EMBASE and the ISI web of knowledge (v4.2) databases using the following search terms ‘polycystic ovary syndrome’ and “proteomic”, “proteomics”, “proteomic biomarker” without any limits/restrictions. All relevant studies published after the original PCOS database were reviewed. Eleven studies [15]–[25] were identified including four reviews and one study on mice. The review articles and the study on mice [18], [23]–[24] were excluded. A further three studies were abstracts from conference proceedings with no primary proteomic data on PCOS so they were also excluded [19]–[21]. The data from the three remaining studies was accessed through direct online links to the files from the search results [15]–[16], [22].
Integrating the Proteomic Database of PTB with the PCOS Database
Proteomic biomarkers for PTB identified in two or more of the primary studies are listed on Table 2. These were then compared to the updated database of proteomic biomarkers for PCOS. Any commonly expressed biomarkers where indentified. A note was made of their function and of the tissue from which they originated in women with PCOS. Given the limited number of commonly expressed biomarkers identified, this exercise was expanded to all the proteomic biomarkers identified in PTB against the updated PCOS database. This process was independently performed by two of the authors (NG and ND).
Results
Proteomic Studies for PTB
Figure 1 demonstrates the selection process of the primary studies where proteomic methodologies were used for the identification of biomarkers of PTB. The initial search conducted through MEDLINE yielded 47 articles which included 7 reviews. After screening the titles and abstracts, 15 primary studies were isolated. Studies were excluded if they were review articles, proteomic techniques were not used or if they did not compare PTB with a term birth (control) group. Three studies involving animals only, 2 presenting proteomic peaks rather than proteins and 1 comparing different proteomic approaches were further excluded leaving 9 primary studies [26]–[34] eligible for this review. Further searches of the Cochrane (registered clinical trials) and EMBASE databases and hand searching of the references of relevant manuscripts did not yield additional articles.
General Characteristics of the Proteomic Studies Investigating Biomarkers of PTB
A total of 9 studies were identified from the literature (Table 1). The overall number of participants was 820. Sample sites differed between studies; 5 studies used amniotic fluid (AF) only [26], , 2 studies used AF and maternal serum [27], [29] and 2 studies used maternal serum only [32]–[33]. In general, the selection criteria were adequately described. However, 4 studies failed to explicitly state their exclusion criteria [26], [28], [30], [32]. The study population was fully described in 8 studies with only one study not describing the mean age and age range of the patients [32]. Various proteomic techniques were used in the 9 studies with SELDI-TOF (Surface-enhanced laser desorption ionization time-of-flight), MALDI-TOF (Matrix-assisted laser desorption time-of-flight) and LC-MS/MS (Liquid Chromatography – Tandem Mass Spectrometry) being the most common (Table 1).
Assessing the Quality of the Relevant Studies
Six out of the 9 studies were HQ fulfilling 12 or more of the 16 QUADOMICS criteria [27]–[28], [30]–[31], [33]–[34]. The remaining 3 studies were LQ achieving less than 12 out of the 16 quality criteria [26], [29], [32].
Determining the Proteins Most Frequently Affected in the PTB Studies
A total of 201 different proteomic biomarkers were identified in the 9 studies, 15 of which were identified in 2 studies or more (Table 2). These included: Neutrophil defensin-1 (precursor) (HNP-1), Neutrophil defensin-2 (precursor) (HNP-2), Calgranulin A (S100-A8), Calgranulin B (S100-A9), Calgranulin C (S100-A12), IGFBP-1 (proteolytic fragment precursor), APO A-1, Retinol-binding protein, FLNA (Filamin A α), Macrophage-capping protein, Neutrophil gelatinase-associated lipocalin (precursor), Myeloperoxidase precursor/MPO isoform H17 of Myeloperoxidase Precursor, FALL-39 (precursor), Leukocyte elastase inhibitor (SERPINB1), and Von Ebner’s gland protein precursor/Novel protein similar to mouse von Ebner salivary gland protein.
Cross Referencing Proteomic Biomarkers Identified in Primary Studies of PTB in Database of Proteomic Biomarkers for PCOS
Thirty-two additional proteomic biomarkers for PCOS were identified in the process of updating the PCOS proteomic database (available on request) and these were merged with the old database. Some biomarkers were variants of the same protein which was presumed to be due to varied post-translational modifications or splicing variants. A free text search of the PCOS proteomic biomarker database was carried out initially using the 15 PTB biomarkers identified in two or more studies in our systematic review.
This search was then expanded to include the remaining 186 PTB biomarkers identified in the 9 PTB studies. Six biomarkers were similarly over-expressed in women with PTB and with PCOS compared to controls. These biomarkers include Pyruvate kinase M1/M2 (PKM1/M2), Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin.
Discussion
For this review, a biomarker was defined as a characteristic that can be objectively measured and evaluated as an indicator of pathological processes [35]. This study has, for the first time, identified a panel of 6 proteomic biomarkers which were similarly over-expressed in women with PTB and in women with PCOS. These biomarkers include PKM1/M2, Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin.
PKM1/M2 was found to be elevated both in patients with PCOS and with PTB. Pyruvate kinase catalyzes the last step of glycolysis where phosphoenolpyruvate (PEP) is converted to ADP. PKM2 is known to interact with a variety of biological molecules such as A-Raf, FGFR-1 and Jak-2 mutant and is also implicated in cancer metabolism [36]. High Pyruvate Kinase activity has been found both in rat and human placentae, indicating that the placenta is having a high glycolytic potential [37]–[38]. This was indeed the case, since further results on placentae in women with gestational diabetes showed increased Pyruvate Kinase activity [39]–[40]. A large meta-analysis involving pregnant women with PCOS demonstrated an increase in the prevalence of gestational diabetes compared to pregnant women without PCOS [6]. It is also well established that women with PCOS have an increased risk of developing Type 2 diabetes compared to the general population. We therefore believe that the increased levels of PKM1/M2 observed in both PCOS and PTB may represent a common defect in glucose metabolism. Fructose Bisphosphonate Aldolase A is a glycolytic enzyme found in all tissues [41]. It acts in the same pathway as PKM1/M2 and thus the increase in both PCOS and PTB can be explained using the above hypothesis.
Vimentin is an intermediate filament (IF) protein which is an important cytoskeletal part of mesenchymal cells. It plays a vital role in anchoring and positioning organelles in the cytosol [42]. Vimentin expression seems to be increased in inflammatory and immunological processes evident in studies involving patients with rheumatoid arthritis and Group A streptococcal infections [43]–[44]. Its increase in both PCOS and PTB is thus justified since both conditions have inflammatory and immunological pathophysiology.
Transferrin is a glycoprotein that transports iron and is known to promote iron transport in the ovarian follicles [45]. Transferrin also plays a crucial role in pregnancy where its expression in the villous syncytiotrophoblasts is significantly increased in women with PTB compared to those with normal pregnancies [46]. Transferrin is a recognized stress/acute phase response molecule. Its increase in both women with PCOS and PTB can be explained on the basis of the inflammatory component of the two conditions.
HSPB1 is also known as HSP27 and HSP28 and its levels are increased by mechanisms such as oxidative stress, heat shock exposure, infection, inflammation and ischemia [47]–[48]. As with Transferrin and Vimentin, the higher expression of HSPB1 observed in both women with PCOS and PTB compared to controls reflect the inflammatory process involved in this conditions.
Peroxiredoxin-1 is involved in antioxidant defense mechanisms, cellular redox reactions, signaling transduction pathways and may have possible chaperone activity [49]. Its over-expression in both PCOS and PTB may represent the differentiating steps of the immune reaction that take place in the two conditions.
We acknowledge that the disparate accuracy and precision of the various quantitative and semi-quantitative techniques could pose a challenge with a combined assessment of the results. This is, however, an issue with all systematic reviews and metanalyses which could be affected by clinical heterogeneity. This was the reason we chose to report differential protein expression as either up- or down-regulated which is consistent with previously published systematic reviews of proteomic biomarkers [50].
A consistently emerging theme from studies using proteomic approaches in PCOS is the potential role of immunoregulation/inflammation and antioxidants in the pathogenesis of the condition. These two pathways have also been implicated in PTB and insulin resistance which are both of concern in women with PCOS [1]–[7]. Using inflammatory factors as biomarkers for disease conditions is challenging as inflammation is associated with a multitude of other pathological conditions. However, this is a limitation that applies to all biomarker studies of complex diseases such as one previously published in this journal and not just inflammatory biomarkers [51]. We do not propose at this stage that the biomarkers identified in our study are used as definitive biomarkers of PTB and PCOS rather that our results inform further mechanistic and validation studies and can be used to better understand the pathophysiological mechanisms linking PCOS and PTB.
Although proteomic and other “-omic” technologies offer a great potential for the generation of new insights into disease aetiology, concerns have been expressed about the relatively slow pace at which research findings have been translated into clinical care [52]–[53]. In addition, proteomic techniques have limited ability in detecting low-abundance proteins, some of which may have diagnostic potential. There has been a call for greater focus on data integration from primary proteomic studies in order to improve translation of research findings and prospective validation [54]. The sample sizes and number of biomarkers identified following these studies runs the risk of false positives and this is a limitation of all biomarker studies [55]. These issues again emphasize the need for collaboration, data synthesis and integration (as done in this review) in order to identify a shortlist of replicated biomarkers which can be validated in subsequent hypothesis-driven research [53]. We therefore see great value in informing the scientific community about these research findings at this stage as in the area of “omic” research, data sharing and collaboration is vital for progress. For example, an independent research group with access to stored tissue samples from women with PCOS who have had PTB may, based on this review, decide to independently validate the biomarkers identified in their cohort which would save time. For improved accuracy, it is essential that the same definition of biomarker and selection criteria are employed by future validation studies.
In summary, by integrating data from proteomic studies in PTB with data from proteomic studies in PCOS, we have for the first time identified a panel of 6 promising biomarkers of PTB in women with PCOS. If validated, these biomarkers could provide a useful framework on which the knowledge base in this area could be developed, and will facilitate future mathematical modeling to enhance screening and prevention of PTB in women with PCOS who have been shown to be at increased risk. A well coordinated multidisciplinary collaboration of basic scientists, clinicians and mathematicians is vital to achieve this goal.
Author Contributions
Conceived and designed the experiments: NG ND KN WA. Performed the experiments: NG ND KN WA. Analyzed the data: NG ND KN WA. Wrote the paper: NG WA.
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