Methodological deficits in diagnostic research using '-omics' technologies: evaluation of the QUADOMICS tool and quality of recently published studies

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis.

METHODS

This study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database.

RESULTS

Eleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1).

CONCLUSIONS

Four possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

'Nocturomics': transition to omics-driven biomarkers of nocturia, a systematic review and future prospects

OBJECTIVE

To systematically review studies that investigated different biomarkers of nocturia, including omics-driven biomarkers or 'Nocturomics'.

MATERIALS AND METHODS

PubMed® , Scopus® , and Embase® were searched systematically in May 2022 for research papers on biomarkers in physiological fluids and tissues from patients with nocturia. A distinction was made between biomarkers or candidates discovered by omics techniques, referred to as omics-driven biomarkers, and classical biomarkers, measured by standard laboratory techniques and mostly thought from pathophysiological hypothesis.

RESULTS

A total of 13 studies with 18 881 patients in total were included, eight of which focused on classical biomarkers including: atrial natriuretic peptide (ANP), B-type natriuretic peptide (BNP), C-reactive protein (CRP), aldosterone, and melatonin. Five were 'Nocturomics', including one that assessed the microbiome and identified 27 faecal and eight urinary bacteria correlated with nocturia; and four studies that identified candidate metabolomic biomarkers, including fatty acid metabolites, serotonin, glycerol, lauric acid, thiaproline, and imidazolelactic acid among others. To date, no biomarker is recommended in clinical practice. Nocturomics are in an embryonic phase of conception but are developing quickly. Although candidate biomarkers are being identified, none of them are yet validated on a large sample, although some preclinical studies have shown a probable role of fatty acid metabolites as a possible biomarker of circadian rhythm and chronotherapy.

CONCLUSION

Further research is needed to validate biomarkers for nocturia within the framework of a diagnostic and therapeutic precision medicine perspective. We hope this study provides a summary of the current biomarker discoveries associated with nocturia and details future prospects for omics-driven biomarkers.

Systematic Review and Meta-Analysis on MS-Based Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Bipolar Disorder

Bipolar disorder (BD) is a clinically heterogeneous condition, presenting a complex underlying etiopathogenesis that is not sufficiently characterized. Without molecular biomarkers being used in the clinical environment, several large screen proteomics studies have been conducted to provide valuable molecular information. Mass spectrometry (MS)-based techniques can be a powerful tool for the identification of disease biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids to assess BD biomarkers and identify relevant networks of biological pathways. Following PRISMA guidelines, we searched for studies using MS proteomics to identify proteomic differences between BD patients and healthy controls (PROSPERO database: CRD42021264955). Fourteen articles fulfilled the inclusion criteria, allowing the identification of 266 differentially expressed proteins. Gene ontology analysis identified complement and coagulation cascades, lipid and cholesterol metabolism, and focal adhesion as the main enriched biological pathways. A meta-analysis was performed for apolipoproteins (A-I, C-III, and E); however, no significant differences were found. Although the proven ability of MS proteomics to characterize BD, there are several confounding factors contributing to the heterogeneity of the findings. In the future, we encourage the scientific community to use broader samples and validation cohorts, integrating omics with bioinformatics tools towards providing a comprehensive understanding of proteome alterations, seeking biomarkers of BD, and contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

The Metabolic Signature of Cardiorespiratory Fitness: A Systematic Review

Background

Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies.

Objective

This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids.

Methods

PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS.

Results

Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15).

Conclusions

Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches.

PROSPERO Registration Number

CRD42020214375.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40279-021-01590-y.

The metabolic signature of cardiorespiratory fitness: a protocol for a systematic review and meta-analysis

INTRODUCTION

A low cardiorespiratory fitness (CRF) is a strong and independent predictor of cardiometabolic, cancer and all-cause mortality. To date, the mechanisms linking CRF with reduced mortality remain largely unknown. Metabolomics, which is a powerful metabolic phenotyping technology to unravel molecular mechanisms underlying complex phenotypes, could elucidate how CRF fosters human health.

METHODS AND ANALYSIS

This study aims at systematically reviewing and meta-analysing the literature on metabolites of any human tissue sample, which are positively or negatively associated with CRF. Studies reporting estimated CRF will not be considered. No restrictions will be placed on the metabolomics technology used to measure metabolites. PubMed, Web of Science and EMBASE will be searched for relevant articles published until the date of the last search. Two authors will independently screen full texts of selected abstracts. References and citing articles of included articles will be screened for additional relevant publications. Data regarding study population, tissue samples, analytical technique, quality control, data processing, metabolites associated to CRF, cardiopulmonary exercise test protocol and exercise exhaustion criteria will be extracted. Methodological quality will be assessed using a modified version of QUADOMICS. Narrative synthesis as well as tabular/charted presentation of the extracted data will be included. If feasible, meta-analyses will be used to investigate the associations between identified metabolites and CRF. Potential sources of heterogeneity will be explored in meta-regressions.

ETHICS AND DISSEMINATION

No ethics approval is required. The results will be published in a peer-reviewed journal and as conference presentation.

PROSPERO REGISTRATION NUMBER

CRD42020214375.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Alzheimer's disease and type 2 diabetes mellitus: A systematic review of proteomic studies

Similar to dementia, the risk for developing type 2 diabetes mellitus (T2DM) increases with age, and T2DM also increases the risk for dementia, particularly Alzheimer's disease (AD). Although T2DM is primarily a peripheral disorder and AD is a central nervous system disease, both share some common features as they are chronic and complex diseases, and both show involvement of oxidative stress and inflammation in their progression. These characteristics suggest that T2DM may be associated with AD, which gave rise to a new term, type 3 diabetes (T3DM). In this study, we searched for matching peripheral proteomic biomarkers of AD and T2DM based in a systematic review of the available literature. We identified 17 common biomarkers that were differentially expressed in both patients with AD or T2DM when compared with healthy controls. These biomarkers could provide a useful workflow for screening T2DM patients at risk to develop AD.

Systematic Review: Clinical Metabolomics to Forecast Outcomes in Liver Transplantation Surgery

Liver transplantation is an effective intervention for end-stage liver disease, fulminant hepatic failure, and early hepatocellular carcinoma. Yet, there is marked patient-to-patient variation in liver transplantation outcomes. This calls for novel diagnostics to enable rational deployment of donor livers. Metabolomics is a postgenomic high-throughput systems biology approach to diagnostic innovation in clinical medicine. We report here an original systematic review of the metabolomic studies that have identified putative biomarkers in the context of liver transplantation. Eighteen studies met the inclusion criteria that involved sampling of blood (n = 4), dialysate fluid (n = 4), bile (n = 5), and liver tissue (n = 5). Metabolites of amino acid and nitrogen metabolism, anaerobic glycolysis, lipid breakdown products, and bile acid metabolism were significantly different in transplanted livers with and without graft dysfunction. However, criteria for defining the graft dysfunction varied across studies. This systematic review demonstrates that metabolomics can be deployed in identification of metabolic indicators of graft dysfunction with a view to implicated molecular mechanisms. We conclude the article with a horizon scanning of metabolomics technology in liver transplantation and its future prospects and challenges in research and clinical practice.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review

Colorectal cancer (CRC) remains an incurable disease. There are no effective noninvasive techniques that have achieved colorectal cancer (CRC) diagnosis, prognosis, survival and recurrence in clinic. To investigate colorectal cancer metabolism, we perform an electronic literature search, from 1998 to January 2016, for studies evaluating the metabolomic profile of patients with CRC regarding the diagnosis, recurrence, prognosis/survival, and systematically review the twenty-three literatures included. QUADOMICS tool was used to assess the quality of them. We highlighted the metabolism perturbations based on metabolites and pathway. Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in CRC. Altered metabolites were also related to prognosis, survival and recurrence of CRC. This review could represent the most comprehensive information and summary about CRC metabolism to date. It certificates that metabolomics had great potential on both discovering clinical biomarkers and elucidating previously unknown mechanisms of CRC pathogenesis.

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

Metabolomic biomarkers in diabetic kidney diseases--A systematic review

Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.

Beyond classical derivatization: analyte ‘derivatives’ in the bioanalysis of endogenous and exogenous compounds

The analysis of endogenous and exogenous analytes in biological matrices presents several challenges to the bioanalyst. These analytes are often present at low concentrations, typically in complex matrices, and may have physicochemical properties that are not amenable to LC–MS analysis. The bioanalyst thus relies heavily on the formation of analyte derivatives for the efficient quantification of these compounds. These derivatives are also critically employed to derive information on the biology of living systems, potential drug or disease targets, and biomarkers of drug efficacy, safety, or disease progression. In this perspective, we demonstrate how analyte derivatives are applied in modern bioanalytical workflows and we discuss the potential use of these derivatives in the future.

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Overlap of proteomics biomarkers between women with pre-eclampsia and PCOS: a systematic review and biomarker database integration

STUDY QUESTION

Do any proteomic biomarkers previously identified for pre-eclampsia (PE) overlap with those identified in women with polycystic ovary syndrome (PCOS).

SUMMARY ANSWER

Five previously identified proteomic biomarkers were found to be common in women with PE and PCOS when compared with controls.

WHAT IS KNOWN ALREADY

Various studies have indicated an association between PCOS and PE; however, the pathophysiological mechanisms supporting this association are not known.

STUDY DESIGN, SIZE, DURATION

A systematic review and update of our PCOS proteomic biomarker database was performed, along with a parallel review of PE biomarkers. The study included papers from 1980 to December 2013.

PARTICIPANTS/MATERIALS, SETTING, METHODS

In all the studies analysed, there were a total of 1423 patients and controls. The number of proteomic biomarkers that were catalogued for PE was 192.

MAIN RESULTS AND THE ROLE OF CHANCE

Five proteomic biomarkers were shown to be differentially expressed in women with PE and PCOS when compared with controls: transferrin, fibrinogen α, β and γ chain variants, kininogen-1, annexin 2 and peroxiredoxin 2. In PE, the biomarkers were identified in serum, plasma and placenta and in PCOS, the biomarkers were identified in serum, follicular fluid, and ovarian and omental biopsies.

LIMITATIONS, REASONS FOR CAUTION

The techniques employed to detect proteomics have limited ability in identifying proteins that are of low abundance, some of which may have a diagnostic potential. The sample sizes and number of biomarkers identified from these studies do not exclude the risk of false positives, a limitation of all biomarker studies. The biomarkers common to PE and PCOS were identified from proteomic analyses of different tissues.

WIDER IMPLICATIONS OF THE FINDINGS

This data amalgamation of the proteomic studies in PE and in PCOS, for the first time, discovered a panel of five biomarkers for PE which are common to women with PCOS, including transferrin, fibrinogen α, β and γ chain variants, kininogen-1, annexin 2 and peroxiredoxin 2. If validated, these biomarkers could provide a useful framework for the knowledge infrastructure in this area. To accomplish this goal, a well co-ordinated multidisciplinary collaboration of clinicians, basic scientists and mathematicians is vital.

STUDY FUNDING/COMPETING INTERESTS

No financial support was obtained for this project. There are no conflicts of interest.

Systems approaches in integrative cardiac biology: illustrations from cardiac heterocellular signalling studies

Understanding the complexity of cardiac physiology requires system-level studies of multiple cardiac cell types. Frequently, however, the end result of published research lacks the detail of the collaborative and integrative experimental design process, and the underlying conceptual framework. We review the recent progress in systems modelling and omics analysis of the heterocellular heart environment through complementary forward and inverse approaches, illustrating these conceptual and experimental frameworks with case studies from our own research program. The forward approach begins by collecting curated information from the niche cardiac biology literature, and connecting the dots to form mechanistic network models that generate testable system-level predictions. The inverse approach starts from the vast pool of public omics data in recent cardiac biological research, and applies bioinformatics analysis to produce novel candidates for further investigation. We also discuss the possibility of combining these two approaches into a hybrid framework, together with the benefits and challenges. These interdisciplinary research frameworks illustrate the interplay between computational models, omics analysis, and wet lab experiments, which holds the key to making real progress in improving human cardiac wellbeing.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

Proteomic biomarkers of endometrial cancer risk in women with polycystic ovary syndrome: a systematic review and biomarker database integration

There is a need for research studies into the molecular mechanisms underpinning the link between polycystic ovary syndrome (PCOS) and endometrial cancer (EC) to facilitate screening and to encourage the development of novel strategies to prevent disease progression. The objective of this review was to identify proteomic biomarkers of EC risk in women with PCOS. All eligible published studies on proteomic biomarkers for EC identified through the literature were evaluated. Proteomic biomarkers for EC were then integrated with an updated previously published database of all proteomic biomarkers identified so far in PCOS women. Nine protein biomarkers were similarly either under or over expressed in women with EC and PCOS in various tissues. These include transgelin, pyruvate kinase M1/M2, gelsolin-like capping protein (macrophage capping protein), glutathione S-transferase P, leucine aminopeptidase (cytosol aminopeptidase), peptidyl-prolyl cis-transisomerase, cyclophilin A, complement component C4A and manganese-superoxide dismutase. If validated, these biomarkers may provide a useful framework on which the knowledge base in this area could be developed and will facilitate future mathematical modelling to enhance screening and prevention of EC in women with PCOS who have been shown to be at increased risk.

Proteomic biomarkers of type 2 diabetes mellitus risk in women with polycystic ovary syndrome

Women with polycystic ovary syndrome (PCOS) are at increased risk of developing insulin resistance and type 2 diabetes mellitus (T2DM). In this study, we attempted to list the proteomic biomarkers of PCOS and T2DM that have been published in the literature so far. We identified eight common biomarkers that were differentially expressed in both women with PCOS and T2DM when compared with healthy controls. These include pyruvate kinase M1/M2, apolipoprotein A-I, albumin, peroxiredoxin 2, annexin A2, α-1-B-glycoprotein, flotillin-1 and haptoglobin. These biomarkers could help improve our understanding of the links between PCOS and T2DM and could be potentially used to identify subgroups of women with PCOS at increased risk of T2DM. More studies are required to further evaluate the role these biomarkers play in women with PCOS and T2DM.

Proteomic biomarkers of preterm birth risk in women with polycystic ovary syndrome (PCOS): a systematic review and biomarker database integration

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.

Relationships of hepatic and pancreatic biomarkers with the cholestatic syndrome and tumor stage in pancreatic cancer

We analyzed relationships of hepatic and pancreatic biomarkers with the cholestatic syndrome and tumor stage in exocrine pancreatic cancer (N = 183). Information on laboratory tests and on signs and symptoms was obtained from medical records and patient interviews. Bilirubin, aspartate aminotransferase (AST), γ-glutamyltransferase (GGT) and alkaline phosphatase were lower in tumor stage IV. The association was due to the relationship between cholestatic syndrome and earlier presentation of patients. There was no association between hepatic biomarkers and stage when adjusting by cholestatic syndrome. Relationships of hepatic and pancreatic biomarkers with pancreatic symptoms and tumor stage must be controlled in "-omics" and other studies using biomarkers.

Personalized medicine using DNA biomarkers: a review

Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers—DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.

Metabolomic biomarkers of impaired glucose tolerance and type 2 diabetes mellitus with a potential for risk stratification in women with polycystic ovary syndrome

There is a need to identify biomarkers of impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM) risk in women with PCOS to facilitate screening and the development of novel strategies to prevent disease progression. Metabolomic technologies may address this need. All published studies on metabolomic biomarkers of IGT and/or T2DM identified through MEDLINE (1966-December 2010), EMBASE (1980-December 2010) and Cochrane (1993-December 2010) were retrieved. Eligible studies were screened and specific study characteristics recorded including study design, number of participants, selection criteria, type of metabolomic technique used, site of sample collection, and a list of metabolites identified to have been altered in IGT and/or T2DM versus healthy controls was created. Nine metabolomic biomarkers that could potentially be used to identify women with PCOS at risk of developing IGT and/or T2DM were identified including leucine, isoleucine, citrate, glucose, creatinine, valine, glutamine, alanine and HDL. Of these biomarkers, a panel of four biomarkers were consistently either elevated or reduced including glucose (elevated), valine (reduced), HDL (reduced) and alanine (reduced) in IGT/T2DM compared with controls. These biomarkers may predict the development of IGT/T2DM in young women with PCOS. More studies are required to test this hypothesis and translate the findings into patient benefit by reducing the morbidity/mortality associated with IGT/T2DM in PCOS.

Clinical validity of detecting K-ras mutations for the diagnosis of exocrine pancreatic cancer: a prospective study in a clinically-relevant spectrum of patients

The diagnostic utility of detecting K-ras mutations for the diagnosis of exocrine pancreatic cancer (EPC) has not been properly studied, and few reports have analysed a clinically relevant spectrum of patients. The objective was to evaluate the clinical validity of detecting K-ras mutations in the diagnosis of EPC in a large sample of clinically relevant patients. We prospectively identified 374 patients in whom one of the following diagnoses was suspected at hospital admission: EPC, chronic pancreatitis, pancreatic cysts, and cancer of the extrahepatic biliary system. Mutations in the K-ras oncogene were analysed by PCR and artificial RFLP in 212 patients. The sensitivity and specificity of the K-ras mutational status for the diagnosis of EPC were 77.7% (95% CI: 69.2-84.8) and 78.0% (68.1-86.0), respectively. The diagnostic accuracy was hardly modified by sex and age. In patients with either mutated K-ras or CEA > 5 ng/ml, the sensitivity and specificity were 81.0% (72.9-87.6) and 62.6% (72.9-87.6), respectively. In patients with mutated K-ras and CEA > 5 ng/ml the sensitivity was markedly reduced. In comparisons with a variety of non-EPC patient groups sensitivity and specificity were both always greater than 75%. In this clinically relevant sample of patients the sensitivity and specificity of K-ras mutations were not sufficiently high for independent diagnostic use. However, it seems premature to rule out the utility of K-ras analysis in conjunction with other genetic and 'omics' technologies.