1,721,167 research outputs found
Exposure to endocrine disrupting chemicals and other hormone-related variables, DNA methylation, and breast cancer
Full text linkIntroduction
Breast cancer is the most common cancer in the world and environmental factors such as endocrine disrupting chemicals, as well as reproductive and hormone-related factors play a crucial role in the development of this disease. In order to assess causal pathways between these exposures and disease initiation, biomarkers based on DNA methylation measurements can be used.
Methods
The potential association between global and locus-specific DNA methylation and breast cancer risk was investigated in two prospective European nested case-control studies. The HM450 array was used to generate epigenomic profiles of archived blood samples, collected from study participants before the onset of disease in 324 matched case-control pairs. The association between endocrine disrupting chemicals measured in blood samples (n=368), reproductive and hormone-related variables assessed by questionnaire (n=324), and hormone levels measured in blood(n=36), and DNA methylation was studied. The meet-in-the-middle approach was applied to identify DNA methylation markers related to both exposures and disease endpoint.
Results
Global hypomethylation was observed among breast cancer cases compared with controls and locus-specific analyses identified 26 CpGs whose DNA methylation was associated with breast cancer. Cadmium exposure was associated with DNA methylation at 62 CpGs but most associations did not survive adjustment for smoking status. In addition, numerous reproductive and hormone-related variables, as well as the hormones D4 and testosterone were associated with DNA methylation, and three potential meet-in-the-middle candidates were observed.
Discussion
Despite the relatively low power, results indicated that genome-wide hypomethylation among breast cancer cases may serve as a biomarker for disease risk. More research with bigger sample sizes is needed to disentangle the potential effect of cadmium and smoking on DNA methylation and to further explore possible effects of reproductive and hormone-related factors, as well as hormone levels, on DNA methylation. It is of interest to investigate what the biological consequences of these changes in methylation are.Open Acces
Statistical analysis of ‘–omics’ data: developments and applications
No full textIn recent years, increasingly efficient molecular biology techniques created new opportunities to harness large-scale repositories of biological material collected in epidemiological studies; however, methods to manipulate and analyse the wealth of information thus generated have lagged behind.
The introductory chapter of this thesis presents the multifaceted field of ‘computational epidemiology’ from the perspectives of molecular biology, measurement theory, and statistical modelling. Focusing on measurement of DNA methylation levels, the author also reviews the state of the art, proposes novel pre-processing methods and evaluation frameworks, and provides recommendations for genome-wide studies of DNA methylation levels using Illumina Infinium® HumanMethylation450 BeadChips.
The remaining chapters, in the form of three self-contained scientific articles, cover applications on the following topics: (i) DNA methylation differences associated with internal migration patterns within Italy; (ii) associations of DNA methylation profiles with adiposity measures, targeted gene expression, biomarkers of lipid and glucose metabolism, and risk of developing three obesity-associated diseases; (iii) associations of a dietary score with blood pressure, and with urinary metabolites as characterised by NMR spectroscopy.
The thesis is concluded with general remarks and the presentation of some open problems that offer potential for future research.Open Acces
Statistical nodels to explore the exposome: from OMICs profiling to ‘mechanome’ characterization
Full text linkOver the past decade, high-resolution molecular profiles using OMICS technologies have accumulated and have given rise to an unprecedented source of information to explore the effective biological effects of external stressors and to detect drivers of subsequent disease risk. Although the volume, dimensionality, and complexity of OMICs data are constantly increasing, several methods enabling their analysis are now available. The exploration of these data relies on statistical approaches including univariate models coupled with multiple testing correction, dimensionality reduction techniques, and variable selection approaches. While these methods are established, their application in an exposome context is raising specific methodological challenges. In addition, the isolated exploration of an OMIC profile offers the possibility to capture stressor-induced biological/biochemical alterations, potentially impacting individual risk profiles, but this may only yield a fractional picture of the complex molecular events involved, therefore limiting our understanding of the effective mechanisms mediating the effect of the exposome. Despite efficient developments over systems biological approaches, such integrations remain at best data-specific, usually disease-specific, and more systematically restricted to the exploration of (few) predefined hypotheses. The challenging task of exploring the ‘mechanome’ as defined by the ensemble of stressor-induced molecular mechanisms occurring throughout the life course and determining the individual’s risk of developing adverse conditions can be decomposed in three interdependent streams focusing on (1) OMICs profiling, (2) OMICs data integration, and (3) the exploration of molecular mechanisms involved in the exposure effect mediation towards (chronic) disease development
Novel strategies for the identification of biomarkers of non-Hodgkin lymphoma: evidence from the European Prospective Investigation into Cancer (EPIC)
Full text linkNon-Hodgkin’s Lymphomas (NHL) represent the eighth most common cancer in Western Europe. Yet despite their widespread prevalence and high mortality rate relatively little is known about the aetiology of these hematological malignancies. Consequently NHL represents an ideal candidate for the discovery of biomarkers lying along the causal pathway. Such biomarkers would allow the improved identification of risk factors and high risk individuals, as well as an enhanced understanding of lymphomageneisis. However, to date there has been little progress in determining validated predictive biomarkers of NHL.
This thesis attempts to address some of the issues that have previously hampered the study of NHL through novel strategies of biomarker identification utilising novel methodologies, technologies and statistical techniques. The thesis comprises a nested case-control study within the European Prospective investigation into Cancer (EPIC) cohort and is split into two parts: the ‘validation of biomarkers’ and the ‘integration of biomarkers’. The most exciting finding was the identification of a novel biomarker for Follicular lymphoma based on the t(14;18) translocation which comprises a previously unknown pre-disease condition. Although no other predictive biomarkers were identified this work represents a ‘proof-of-principle’ for the use profile regression in the study of highly dimensional complex datasets, and the possibility of using mass-spectrometry derived metabolic profiles in the study of lymphoma. Part two of the thesis confirmed that the use of the ‘meet-in-the-middle’ approach was a valuable and feasible method for studying the complete causal pathway from risk factor to disease.
Together these results highlight potential avenues for further study of NHL and confirm the utility of a number of novel strategies that can aid such work. Additionally it informs on some of the likely challenges that will be involved.Open Acces
A stability framework for variable selection, graphical modelling and clustering: applications in lung cancer research
No full textDecades of research have shown that various biological mechanisms are deregulated during carcinogenesis. By offering an agnostic view of individual molecular profiles, high-throughput technologies have enabled new avenues to investigate these molecular changes. These technological developments have also raised novel statistical challenges for the generation of interpretable and robust findings from high dimensional and heterogenous datasets. Regularisation has been instrumental in the analysis of such data by enabling estimations in high dimension and inducing sparsity in the results. Reproducibility issues can be circumvented by combining regularised models with resampling techniques in stability selection or consensus clustering. However, these stability approaches are still under-used, possibly due to the sensitivity of the results to the choice of hyper-parameters and the complexity of their calibration. In the present thesis, I propose to calibrate hyper-parameters of stability selection (for regression or graphical modelling) or consensus clustering by maximising novel scores measuring results stability. To evaluate the performance of this procedure, and compare it with state-of-the-art calibration techniques, I develop flexible simulation models based on the multivariate Normal distribution. Extensive simulation studies suggest that calibration using the novel scores outperforms existing approaches for no increase in computational cost. The proposed models are then used on real omics datasets in a lung cancer case-control study to identify molecular markers of lung carcinogenesis. The use of stability selection in a structural causal modelling framework enables the identification of both (i) potential biological mediators of the effect of tobacco smoking on lung carcinogenesis, and (ii) smoking-independent markers of lung cancer. To facilitate the use of these novel techniques, I have created two R packages, freely available on CRAN. Stability selection and consensus clustering, along with the novel calibration procedures, have been implemented in the R package sharp. Simulation functions are available in the R package fake.Open Acces
Molecular phenotyping of severe asthma using statistical and machine learning models
No full textDespite improvements in asthma management, patient response to treatment have stagnated, particularly for severe asthma cases. Current therapies have limited efficacy across all phenotypes, and many patients remain inadequately controlled. Advanced omics technologies offer potential insights into the molecular mechanisms underlying asthma and may therefore help in refining patient classification and developing targeted therapeutics.
The first chapter explores the relationship between 24 targeted proteins and asthma severity and clinical manifestations. First, associations between asthma-related outcomes and each individual protein are assessed. The study then adopts a more comprehensive approach by clustering patients based on multiple clinical factors, providing a composite measure of severity. This dual methodology allows for a broader understanding of the proteomic markers involved in asthma clinical presentation and its severity, offering potential insights into more effective diagnostic and therapeutic strategies.
Chapter 2 focuses on identifying proteomic signatures of eosinophilic and neutrophilic asthma by examining the inflammatory and immune pathways unique to each phenotype. After combining data from two large asthma cohorts, the study explores how proteomic markers in serum and sputum associated with asthma subtypes. Using stability selection models, the research identifies key proteins associated with each asthma phenotype, offering potentially alternative diagnostic biomarkers and new therapeutic targets tailored to distinct inflammatory pathways.
The final chapter builds on limitations of single-omic studies in characterising asthma phenotypes by using multi-omic approaches to capture the biological interactions underlying asthma phenotypes. Using genomic, transcriptomic, and proteomic data, the research aims to identify multi-omic biomarkers that distinguish asthma subtypes based on clinical blood eosinophil and neutrophil thresholds. The findings contribute to understanding asthma's complex molecular pathways, advancing beyond the limitations of single-omic studies.
The thesis concludes by summarising key insights, discussing limitations, and proposing avenues for future research.Open Acces
DNA methylation and exposure to ambient air pollution in two prospective cohorts
No full textLong-term exposure to air pollution has been associated with several adverse health effects including cardiovascular, respiratory diseases and cancers. However, underlying molecular alterations remain to be further investigated. The aim of this study is to investigate the effects of long-term exposure to air pollutants on (a) average DNA methylation at functional regions and, (b) individual differentially methylated CpG sites. An assumption is that omic measurements, including the methylome, are more sensitive to low doses than hard health outcomes. This study included blood-derived DNA methylation (Illumina-HM450 methylation) for 454 Italian and 159 Dutch participants from the European Prospective Investigation into Cancer and Nutrition (EPIC). Long-term air pollution exposure levels, including NO2, NOx, PM2.5, PMcoarse, PM10, PM2.5 absorbance (soot) were estimated using models developed within the ESCAPE project, and back-extrapolated to the time of sampling when possible. We meta-analysed the associations between the air pollutants and global DNA methylation, methylation in functional regions and epigenome-wide methylation. CpG sites found differentially methylated with air pollution were further investigated for functional interpretation in an independent population (EnviroGenoMarkers project), where (N=613) participants had both methylation and gene expression data available. Exposure to NO2 was associated with a significant global somatic hypomethylation (p-value=0.014). Hypomethylation of CpG island's shores and shelves and gene bodies was significantly associated with higher exposures to NO2 and NOx. Meta-analysing the epigenome-wide findings of the 2 cohorts did not show genome-wide significant associations at single CpG site level. However, several significant CpG were found if the analyses were separated by countries. By regressing gene expression levels against methylation levels of the exposure-related CpG sites, we identified several significant CpG-transcript pairs and highlighted 5 enriched pathways for NO2 and 9 for NOx mainly related to the immune system and its regulation. Our findings support results on global hypomethylation associated with air pollution, and suggest that the shores and shelves of CpG islands and gene bodies are mostly affected by higher exposure to NO2 and NOx. Functional differences in the immune system were suggested by transcriptome analyses.EPIC-Italy was financially supported by the Italian Association for Cancer Research (AIRC). Genome-wide DNA methylation profiling of EPIC-Italy samples was financially supported by the Human Genetics Foundation (HuGeF) and Compagnia di San Paolo. EPIC-Netherlands was financially supported by the Dutch Ministry of Public Health, Welfare, and Sports (VWS), by the Netherlands Cancer Registry, by LK Research Funds, by Dutch Prevention Funds, by the Netherlands Organisation for Health Research and Development (ZON), and by the World Cancer Research Fund (WCRF). Genome-wide DNA methylation profiling of EPIC-Netherlands samples was financially supported by internal Imperial College funds. The REGICOR study was supported by the Spanish Ministry of Economy and Innovation through the Carlos III Health Institute [Red HERACLES RD12/0042, PI12/00232, PI09/90506], European Funds for Development (ERDF-FEDER), and by the Catalan Research and Technology Innovation Interdepartmental Commission [SGR 1195]. Michelle Plusquin was supported by the People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7/2007-2013/ under REA grant agreement n [628858]. Florence Guida was supported by the COLT foundation. Support for this work was also provided by the project EXPOSOMICS, grant agreement 308610-FP7European Commission. Sergi Sayols-Baixeras was funded by a contract from Instituto de Salud Carlos III FEDER [IFI14/00007]
Epigenetic signatures of internal migration in Italy
Full text linkObservational studies have suggested that the risks of non-communicable diseases in voluntary migrants become similar to those in the host population after one or more generations, supporting the hypothesis that these diseases have a predominantly environmental (rather than inherited) origin. However, no study has been conducted thus far to identify alterations at the molecular level that might mediate these changes in disease risk after migration
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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