Integrative multi-omics database (iMOMdb) of Asian pregnant women.

Asians are underrepresented across many omics databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. As such, there is a pressing need for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in populations of Asian ancestry. Here, we provide the first blood-based multi-omics analysis of Asian pregnant women, constituting high-resolution genotyping (N = 1079), DNA methylation (N = 915) and transcriptome profiling (N = 238). Integrative omics analysis identified 219 154 CpGs associated with cis-DNA methylation QTLs (meQTLs) and 3703 RNAs associated with cis-RNA expression QTLs (eQTLs). Ethnicity was the largest contributor of inter-individual variation across all omics datasets, with 2561 genes identified as hotspots of this variation; 395 of these hotspot genes also contained both ethnicity-specific eQTLs and meQTLs. Gene set enrichment analysis of these ethnicity QTL hotspots showed pathways involved in lipid metabolism, adaptive immune system and carbohydrate metabolism. Pathway validation by profiling the lipidome (~480 lipids) of antenatal plasma (N = 752) and placenta (N = 1042) in the same cohort showed significant lipid differences among Chinese, Malay and Indian women, validating ethnicity-QTL gene effects across different tissue types. To develop deeper insights into the complex traits and benefit future precision medicine research in Asian pregnant women, we developed iMOMdb, an open-access database.

Plasma lipidomic profiling reveals metabolic adaptations to pregnancy and signatures of cardiometabolic risk: a preconception and longitudinal cohort study.

BACKGROUND: Adaptations in lipid metabolism are essential to meet the physiological demands of pregnancy and any aberration may result in adverse outcomes for both mother and offspring. However, there is a lack of population-level studies to define the longitudinal changes of maternal circulating lipids from preconception to postpartum in relation to cardiometabolic risk factors. METHODS: LC-MS/MS-based quantification of 689 lipid species was performed on 1595 plasma samples collected at three time points in a preconception and longitudinal cohort, Singapore PREconception Study of long-Term maternal and child Outcomes (S-PRESTO). We mapped maternal plasma lipidomic profiles at preconception (N = 976), 26-28 weeks' pregnancy (N = 337) and 3 months postpartum (N = 282) to study longitudinal lipid changes and their associations with cardiometabolic risk factors including pre-pregnancy body mass index, body weight changes and glycaemic traits. RESULTS: Around 56% of the lipids increased and 24% decreased in concentration in pregnancy before returning to the preconception concentration at postpartum, whereas around 11% of the lipids went through significant changes in pregnancy and their concentrations did not revert to the preconception concentrations. We observed a significant association of body weight changes with lipid changes across different physiological states, and lower circulating concentrations of phospholipids and sphingomyelins in pregnant mothers with higher pre-pregnancy BMI. Fasting plasma glucose and glycated haemoglobin (HbA1c) concentrations were lower whereas the homeostatic model assessment of insulin resistance (HOMA-IR), 2-h post-load glucose and fasting insulin concentrations were higher in pregnancy as compared to both preconception and postpartum. Association studies of lipidomic profiles with these glycaemic traits revealed their respective lipid signatures at three physiological states. Assessment of glycaemic traits in relation to the circulating lipids at preconception with a large sample size (n = 936) provided an integrated view of the effects of hyperglycaemia on plasma lipidomic profiles. We observed a distinct relationship of lipidomic profiles with different measures, with the highest percentage of significant lipids associated with HOMA-IR (58.9%), followed by fasting insulin concentration (56.9%), 2-h post-load glucose concentration (41.8%), HbA1c (36.7%), impaired glucose tolerance status (31.6%) and fasting glucose concentration (30.8%). CONCLUSIONS: We describe the longitudinal landscape of maternal circulating lipids from preconception to postpartum, and a comprehensive view of trends and magnitude of pregnancy-induced changes in lipidomic profiles. We identified lipid signatures linked with cardiometabolic risk traits with potential implications both in pregnancy and postpartum life. Our findings provide insights into the metabolic adaptations and potential biomarkers of modifiable risk factors in childbearing women that may help in better assessment of cardiometabolic health, and early intervention at the preconception period. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03531658.

Population-based plasma lipidomics reveals developmental changes in metabolism and signatures of obesity risk: a mother-offspring cohort study.

BACKGROUND: Lipids play a vital role in health and disease, but changes to their circulating levels and the link with obesity remain poorly characterized in expecting mothers and their offspring in early childhood. METHODS: LC-MS/MS-based quantitation of 480 lipid species was performed on 2491 plasma samples collected at 4 time points in the mother-offspring Asian cohort GUSTO (Growing Up in Singapore Towards healthy Outcomes). These 4 time points constituted samples collected from mothers at 26-28 weeks of gestation (n=752) and 4-5 years postpartum (n=650), and their offspring at birth (n=751) and 6 years of age (n=338). Linear regression models were used to identify the pregnancy and developmental age-specific variations in the plasma lipidomic profiles, and their association with obesity risk. An independent birth cohort (n=1935), the Barwon Infant Study (BIS), comprising mother-offspring dyads of Caucasian origin was used for validation. RESULTS: Levels of 36% of the profiled lipids were significantly higher (absolute fold change > 1.5 and Padj < 0.05) in antenatal maternal circulation as compared to the postnatal phase, with phosphatidylethanolamine levels changing the most. Compared to antenatal maternal lipids, cord blood showed lower concentrations of most lipid species (79%) except lysophospholipids and acylcarnitines. Changes in lipid concentrations from birth to 6 years of age were much higher in magnitude (log2FC=-2.10 to 6.25) than the changes observed between a 6-year-old child and an adult (postnatal mother) (log2FC=-0.68 to 1.18). Associations of cord blood lipidomic profiles with birth weight displayed distinct trends compared to the lipidomic profiles associated with child BMI at 6 years. Comparison of the results between the child and adult BMI identified similarities in association with consistent trends (R2=0.75). However, large number of lipids were associated with BMI in adults (67%) compared to the children (29%). Pre-pregnancy BMI was specifically associated with decrease in the levels of phospholipids, sphingomyelin, and several triacylglycerol species in pregnancy. CONCLUSIONS: In summary, our study provides a detailed landscape of the in utero lipid environment provided by the gestating mother to the growing fetus, and the magnitude of changes in plasma lipidomic profiles from birth to early childhood. We identified the effects of adiposity on the circulating lipid levels in pregnant and non-pregnant women as well as offspring at birth and at 6 years of age. Additionally, the pediatric vs maternal overlap of the circulating lipid phenotype of obesity risk provides intergenerational insights and early opportunities to track and intervene the onset of metabolic adversities. CLINICAL TRIAL REGISTRATION: This birth cohort is a prospective observational study, which was registered on 1 July 2010 under the identifier NCT01174875 .

Umbilical Cord Plasma Lysophospholipids and Triacylglycerols Associated with Birthweight Percentiles.

Dysregulated transplacental lipid transfer and fetal-placental lipid metabolism affect birthweight, as does maternal hyperglycemia. As the mechanisms are unclear, we aimed to identify the lipids in umbilical cord plasma that were most associated with birthweight. Seventy-five Chinese women with singleton pregnancies recruited into the GUSTO mother-offspring cohort were selected from across the glycemic range based on a mid-gestation 75 g oral glucose tolerance test, excluding pre-existing diabetes. Cord plasma samples collected at term delivery were analyzed using targeted liquid-chromatography tandem mass-spectrometry to determine the concentrations of 404 lipid species across 17 lipid classes. The birthweights were standardized for sex and gestational age by local references, and regression analyses were adjusted for the maternal age, BMI, parity, mode of delivery, insulin treatment, and fasting/2 h glucose, with a false discovery-corrected p < 0.05 considered significant. Ten lysophosphatidylcholines (LPCs) and two lysophosphatidylethanolamines were positively associated with the birthweight percentiles, while twenty-four triacylglycerols were negatively associated with the birthweight percentiles. The topmost associated lipid was LPC 20:2 [21.28 (95%CI 12.70, 29.87) percentile increase in the standardized birthweight with each SD-unit increase in log10-transformed concentration]. Within these same regression models, maternal glycemia did not significantly associate with the birthweight percentiles. Specific fetal circulating lysophospholipids and triacylglycerols associate with birthweight independently of maternal glycemia, but a causal relationship remains to be established.

Maternal serum protein profile and immune response protein subunits as markers for non-invasive prenatal diagnosis of trisomy 21, 18, and 13.

OBJECTIVES: To use proteomics to identify and characterize proteins in maternal serum from patients at high-risk for fetal trisomy 21, trisomy 18, and trisomy 13 on the basis of ultrasound and maternal serum triple tests. METHODS: We performed a comprehensive proteomic analysis on 23 trisomy cases and 85 normal cases during the early second trimester of pregnancy. Protein profiling along with conventional sodium dodecyl sulfate polyacrylamide gel electrophoresis/Tandem mass spectrometry analysis was carried out to characterize proteins associated with each trisomy condition and later validated using Western blot. RESULTS: Protein profiling approach using surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF/MS) spectrometry resulted in the identification of 37 unique hydrophobic proteomic features for three trisomy conditions. Using sodium dodecyl sulfate polyacrylamide gel electrophoresis followed by Matrix Assisted Laser Desorption Ionization - Time of Flight/Time of Flight (MALDI-TOF/TOF) and western blot, glyco proteins such as alpha-1-antitrypsin, apolipoprotein E, apolipoprotein H, and serum carrier protein transthyretin were identified as potential maternal serum markers for fetal trisomy condition. The identified proteins showed differential expression at the subunit level. CONCLUSIONS: Maternal serum protein profiling using proteomics may allow non-invasive diagnostic testing for the most common trisomies and may complement ultrasound-based methods to more accurately determine pregnancies with fetal aneuploidies.

Activated brown adipose tissue releases exosomes containing mitochondrial methylene tetrahydrofolate dehydrogenase (NADP dependent) 1-like protein (MTHFD1L).

Brown adipose tissue (BAT) is a promising weapon to combat obesity and metabolic disease. BAT is thermogenic and consumes substantial amounts of glucose and fatty acids as fuel for thermogenesis and energy expenditure. To study BAT function in large human longitudinal cohorts, safe and precise detection methodologies are needed. Although regarded a gold standard, the foray of PET-CT into BAT research and clinical applications is limited by its high ionizing radiation doses. Here, we show that brown adipocytes release exosomes in blood plasma that can be utilized to assess BAT activity. In the present study, we investigated circulating protein biomarkers that can accurately and reliably reflect BAT activation triggered by cold exposure, capsinoids ingestion and thyroid hormone excess in humans. We discovered an exosomal protein, methylene tetrahydrofolate dehydrogenase (NADP+ dependent) 1-like (MTHFD1L), to be overexpressed and detectable in plasma for all three modes of BAT activation in human subjects. This mitochondrial protein is packaged as a cargo within multivesicular bodies of the endosomal compartment and secreted as exosomes via exocytosis from activated brown adipocytes into the circulation. To support MTHFD1L as a conserved BAT activation response in other vertebrates, we examined a rodent model and also proved its presence in blood of rats following BAT activation by cold exposure. Plasma concentration of exosomal MTHFD1L correlated with human BAT activity as confirmed by PET-MR in humans and supported by data from rats. Thus, we deduce that MTHFD1L appears to be overexpressed in activated BAT compared to BAT in the basal nonstimulated state.

Database for exploration of functional context of genes implicated in ovarian cancer.

Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics. Access is free for academic and non-profit users and database can be accessed at http://apps.sanbi.ac.za/ddoc/.

Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma.

Cancer metabolism is associated with the enhanced lipogenesis required for rapid growth and proliferation. However, the magnitude of dysregulation of diverse lipid species still requires significant characterization, particularly in ovarian clear cell carcinoma (OCCC). Here, we have implemented a robust sample preparation workflow together with targeted LC-MS/MS to identify the lipidomic changes in formalin-fixed paraffin-embedded specimens from OCCC compared to tumor-free ovarian tissue. We quantitated 340 lipid species, representing 28 lipid classes. We observed differential regulation of diverse lipid species belonging to several glycerophospholipid classes and trihexosylceramide. A number of unsaturated lipid species were increased in OCCC, whereas saturated lipid species showed a decrease in OCCC compared to the controls. We also carried out total fatty acid analysis and observed an increase in the levels of several unsaturated fatty acids with a concomitant increase in the index of stearoyl-CoA desaturase (SCD) in OCCC. We confirmed the upregulation of SCD (the rate-limiting enzyme for the synthesis of monounsaturated fatty acids) by immunohistochemistry (IHC) assays. Hence, by carrying out a mass spectrometry analysis of archival tissue samples, we were able to provide insights into lipidomic alterations in OCCC.

Proteomic technologies for prenatal diagnostics: advances and challenges ahead.

Proteomics-based identification of biomarkers for fetal abnormalities in maternal plasma, amniotic fluid and reproductive fluids has made significant progress in the past 5 years. This is attributed mainly to advances in various technology platforms associated with mass spectrometry-based techniques. As these techniques are highly sensitive and require only small quantities of body fluids, it is hoped that they will pave the way for the development of effective noninvasive approaches, without subjecting the developing fetus to the same degree of harm as current invasive procedures (e.g., amniocentesis). It is possible that these developments will include same-day analyses, thereby permitting rapid intervention when necessary. To date, a host of body fluids, such as maternal serum and plasma, amniotic fluid, cervical fluid, vaginal fluid, urine, saliva or fetal material, such as placental trophoblast, fetal membranes or cord blood, have been used successfully in the quest to develop markers for a number of pregnancy-related pathologies. In the current review update we focus on the emergence of proteomics as a major platform technology in studying various types of fetal conditions and developing markers for pregnancy-related disorders, such fetal aneuploidy, preterm birth, preeclampsia, intra-amniotic infection and fetal stress. Should the development of these markers be successful, then it is to be envisaged that proteomic approaches will become standard of care for a number of disease conditions associated with feto-maternal health.

Application of micro-solid-phase extraction for the determination of persistent organic pollutants in tissue samples.

A novel, simple and efficient method for determining persistent organic pollutants (POPs) in tissue samples has been developed. This technique involves the use of simultaneous microwave-assisted digestion (MAD) and micro-solid-phase extraction (micro-SPE), in which the sorbent is held within a propylene membrane envelope, with gas chromatographic-mass spectrometric (GC-MS) analysis. The POPs studied included eleven organochlorine pesticides and five polychlorinated biphenyl congeners. Optimization of the MAD-micro-SPE parameters was performed. The relative standard deviations (RSDs) of the method ranged from 0.14 to 12.7%. Correlation coefficients up to 0.9999 were obtained across a concentration range of 1.25-50 ng g(-1). The method detection limits for POPs ranged from 0.002 to 0.009 ng g(-1). A preliminary study applying the MAD-micro-SPE procedure to human ovarian cancer tissue showed that it was capable of detecting the presence of a wide spectrum of different POPs in benign and malignant tumors.

Development of natural sorbent based micro-solid-phase extraction for determination of phthalate esters in milk samples.

In the present study, a natural sorbent based micro-solid phase extraction (µ-SPE) was developed for determination of phthalate esters in milk samples. For the first time, an efficient and cost effective natural material (seed powder of Moringa oleifera) was employed as sorbent in µ-SPE. The sorbent was found to be naturally enriched with variety of functional groups and having a network of interconnected fibers. This method of extraction integrates different steps such as removal of proteins and fatty stuff, extraction and pre-concentration of target analytes into a single step. Thirteen phthalate esters were selected as target compounds for the development and evaluation of method. Some key parameters affecting the extraction efficiency were optimized, including selection of membrane, selection and amount of sorbent, extraction time, desorption solvent, volume of desorption solvent, desorption time and effect of salt addition. Under the optimum conditions, very good linearity was achieved for all the analytes with coefficient of determinations (R(2)) ranging between 0.9768 and 0.9977. The limits of detection ranged from 0.01 to 1.2 µg L(-1). Proposed method showed satisfactory reproducibility with relative standard deviations ranging from 3.6% to 10.2% (n = 7). Finally, the developed method was applied to tetra pack and bottled milk samples for the determination of phthalate esters. The performance of natural sorbent based µ-SPE was better or comparable to the methods reported in the literature.

Application of microwave-assisted micro-solid-phase extraction for determination of parabens in human ovarian cancer tissues.

Parabens (alkyl esters of p-hydroxybenzoic acid) are widely used as preservatives in food, cosmetics and pharmaceutical products. However, weak estrogenicity of some parabens has been reported in several studies, which provided the impetus for this work. Here, a simple and efficient analytical method for quantifying parabens in cancer tissues has been developed. This technique involves the simultaneous use of microwave-assisted solvent extraction (MASE) and micro-solid phase extraction (µ-SPE), in tandem with high performance liquid chromatography (HPLC/UV) analysis for the determination of parabens. The pollutants studied included four parabens (methyl, ethyl, propyl and butyl parabens). Optimization of the experimental parameters for MASE and µ-SPE was performed. Good relative standard deviation (%RSD) ranged from 0.09 to 2.81% and high enrichment factors (27-314) were obtained. Coefficients of determination (r(2)) up to 0.9962 were obtained across a concentration range of 5.0-200ngg(-1). The method detection limits for parabens ranged from 0.005 to 0.0244ngg(-1). The procedure was initially tested on prawn samples to demonstrate its feasibility on a complex biological matrix. Preliminary studies on human ovarian cancer (OC) tissues showed presence of parabens. Higher levels of parabens were detected in malignant ovarian tumor tissues compared to benign tumor tissue samples.

Incidence and potential causative factors associated with chronic benign neutropenia in the Kingdom of Saudi Arabia.

BACKGROUND: Benign neutropenia often presents in certain populations without any genotype nor phenotype. Middle East countries are among the regions where endemic cases of chronic benign neutropenia are reported in the general population with an incidence of approximately between 10-15%. Not many studies have been performed to ascertain the cause or burden associated with this condition. The objective of the current study was to identify the frequency and characterize the consequences of chronic benign neutropenia in the country of Saudi Arabia. RESULTS: Benign neutropenia was found to be high in the Saudi Arabia general population (up to 20%), with an average neutrophil count of 1.48 (range 0.99 - 1.95 × 10(9)cells/L), with Saudis having a higher incidence of chronic benign neutropenia compared to non-Saudis (p = <0.05). Complete blood count analyses showed significant difference in the total white cell count of neutrophils (p < 0.0001), WBC (p < 0.0001), lymphocytes (p < 0.001), monocytes (p < 0.001), eosinophils (p = 0.013) as well as the CD19 B cells (p = 0.008). CONCLUSIONS: Our study is the first to carefully quantitate benign neutropenia in Saudi Arabia. We identified that this condition is prevalent in the middle aged population (18 years to 55 years). These individuals not only had lower neutrophil counts, but also reduced peripheral blood cells types, especially the B-lymphocyte population (CD19 subset). As B-lymphocytes are involved in antibody production and antigen recognition, a decrease might easily predispose the individuals to infectious agents. As such more mechanistic studies need to be undertaken to understand the cause and potential long-term consequences of benign neutropenia.

Diabetes of the brain: computational approaches and interventional strategies.

Diabetes mellitus (DM) is characterized by hyperglycemia either due to deficient insulin production (Type 1 Diabetes mellitus) or peripheral insulin resistance of the cells (Type 2 Diabetes mellitus). Both Type 1 Diabetes mellitus and Type 2 Diabetes mellitus are more prevalent and efforts are directed to actively control these metabolic syndromes. Currently, Alzheimer's disease (AD), is gaining popularity as 'Type 3 diabetes' or 'Diabetes of the brain' and it is now evident that this neurodegenerative disease has multiple shared pathology with DM. Alarming is the fact that the incidence of AD might double within the next two decades, and this is certain to cause devastating effects not only to the afflicted individual or the family, but also to the global economy. Methods to either delay the onset or inhibit the progression of AD are therefore necessary. Progressive dementia, increased deposition of amyloid- ß protein, neurofibrillary tangles and neuritic plaques in the brain are some of the hallmarks of AD. More understanding of the disease at the cellular and molecular level will enable identifying the possible targets for intervention and pave way for either development of novel or modification of the existing therapeutic options. In this work we have performed semantic data mining analysis on a large collection of most recently published data and identified an updated list of common genes expressed in DM and AD. Functional analysis of these genes revealed both existing and missing links involved in a bigger network associated with both disease conditions. Thus we argue that computational analysis methods help not only in understanding the mechanistic links but also in narrowing down precise targets (genes, proteins, metabolites and signalling pathways) and provide the base for both disease intervention and development of therapeutic options.

Common cellular and molecular mechanisms underlying Alzheimer's disease and type 2 diabetes: a knowledge-driven approach.

The relationship between the two age-related diseases namely, Alzheimer's disease (AD) and type II diabetes mellitus (T2DM), is gaining much attention in research because of the alarming forecast on both increasing incidence and economic burden. Recent research studies have identified some of the existing links, between AD and T2DM, such as the dysfunctional glucose metabolism and insulin signaling, stress and inflammation, defective protein processing and the role of advanced glycation end products. It is, therefore, crucial to understand the cellular and molecular mechanisms to identify the common linking mechanisms involved in the pathogenesis of both AD and T2DM. Genome wide association studies may lead to identification of novel targets and provide clues for possible interventional strategies to limit the progression of these two age-related diseases. Hence, the purpose of the present review is to provide an update, on the various possible linking cellular and molecular mechanisms, including our experience on the use of high throughput applications to investigate the molecular mechanisms underneath the neurodegeneration in animal models. Besides, using this knowledge-driven approach, we discuss how the current technological advancements can effectively be used to identify possible associations between these age-related diseases.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer.

BACKGROUND: Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone. RESULTS: The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers. CONCLUSIONS: We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.

Application of porous membrane protected micro-solid-phase-extraction combined with gas chromatography-mass spectrometry for the determination of estrogens in ovarian cyst fluid samples.

A cost effective and environmentally friendly extraction technique using porous membrane protected micro-solid phase extraction (µ-SPE) is described for the extraction of estrogens in cyst fluid samples obtained from cancer patients. A sorbent (ethylsilane (C2) modified silica) (20 mg) was packed in a porous polypropylene envelope (2 cm×1.5 cm) whose edges were heat sealed to secure the contents. The µ-SPE device was conditioned with acetone and placed in a stirred (1:5) diluted cyst fluid sample solution (10 mL) to extract estrogens for 60 min. After extraction, the analytes were desorbed and simultaneously derivatized with a 5:1 mixture of acetone and N,O-bis(trimethylsilyl)-trifluoroacetamide. The extract (2 µL) was analyzed by gas chromatography-mass spectrometry. Various extraction, desorption and derivatization conditions were optimized for µ-SPE. With this simple technique, low limits of detection of between 9 and 22 ng L(-1) and linear range from the detection limits up to 50 µg L(-1) were achieved. The optimized method was used to extract estrogens from cyst fluid samples obtained from patients with malignant and benign ovarian tumors.

Aurora-A expression, hormone receptor status and clinical outcome in hormone related cancers.

AIMS: We investigated the correlation between protein expression of Aurora-A with hormone receptor expression and clinicopathological parameters in ovarian, breast and prostate cancer. METHODS: Subcellular expression of Aurora-A, and androgen receptor (AR), oestrogen receptor (ER) and progesterone receptor (PR) expression, were examined by immunohistochemistry in human tissue microarrays of the three cancer types and by Western blot in cancer cell lines and selected patient tissues. RESULTS: Subgroups of all three cancer types exhibited both nuclear and cytoplasmic expression of Aurora-A. Nuclear presence of Aurora-A was observed in ER positive and negative breast cancer cell lines and tissues. Eighteen of the 126 (14%) tumour tissues that showed nuclear expression of Aurora-A were strongly associated with ER and PR positive breast tumours (p = 0.001). Cytoplasmic expression of AR and Aurora-A was strongly associated in prostate cancer tissues (45% versus 0, p = 0.015). Ovarian tumours (n = 45) with Aurora-A nuclear expression had decreased patient survival (mean survival, 29.5 versus 106.7 months; p < 0.0005) and showed a significant association with recurrence-free survival (mean survival 19.7 versus 95.9 months; p = 0.002). CONCLUSION: Association between nuclear Aurora-A with hormone receptors in breast cancer and with poor clinical outcome in ovarian cancer suggests the significance of active Aurora-A in disease initiation and progression.

Mining to find the lipid interaction networks involved in Ovarian Cancers.

The role of lipids in cancer during the genesis, progression and subsequent metastasis stages is increasingly discussed in the scientific literature. This information is discussed in a wide range of journals making it difficult for researchers to track the latest developments. A comprehensive assessment and translation of the lipidome of ovarian cancer, originating from literature, has yet to be made. We illustrate the deployment of semantic technologies; lipid ontology and text mining, in the aggregation and coordination of lipid literature. We provide the first report on the roles and types of lipids involved in ovarian cancer based on the mining of literature and identify key lipid-protein interactions that may point to potential drug discovery targets.

Ovarian cancer proteomics: Many technologies one goal.

The last decade has seen major changes in the technologies used to identify markers for diagnosing cancer. This review focuses on recent developments on the evolving field of biomarker discovery, and validation techniques using proteomics platforms for ovarian cancer. It is possible now to diagnose various disease conditions using microliter quantities of body fluids. Currently the major developments were made in three distinct areas: (i) protein profiling, (ii) high-throughput validation techniques, and (iii) solid and liquid phase protein microarray platforms for analyzing candidate markers across subclasses and stages of cancers. The recent addition to the long list of technologies is metabolomics using metabolite profiling and informatics-based filtering of information for biomarker discovery of ovarian cancer. Emerging technologies need to address ways to eliminate the limitations posed by the complex dynamic nature of body fluids as well as ways to enrich low-abundance tumor markers if they were to become a successful biomarker discovery tool. These new technologies hold significant promise in identifying more robust markers for ovarian cancer. Since the prevalence of this disease in the population is low, the test must have a high specificity.