| 1. |
- Berger, Eloise, et al.
(författare)
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Association between low-grade inflammation and Breast cancer and B-cell Myeloma and Non-Hodgkin Lymphoma : Findings from two prospective cohorts
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Chronic inflammation may be involved in cancer development and progression. Using 28 inflammatory-related proteins collected from prospective blood samples from two case-control studies nested in the Italian component of the European Prospective Investigation into Cancer and nutrition (n = 261) and in the Northern Sweden Health and Disease Study (n = 402), we tested the hypothesis that an inflammatory score is associated with breast cancer (BC) and Β-cell Non-Hodgkin Lymphoma (B-cell NHL, including 68 multiple myeloma cases) onset. We modelled the relationship between this inflammatory score and the two cancers studied: (BC and B-cell NHL) using generalised linear models, and assessed, through adjustments the role of behaviours and lifestyle factors. Analyses were performed by cancer types pooling both populations, and stratified by cohorts, and time to diagnosis. Our results suggested a lower inflammatory score in B-cell NHL cases (β = -1.28, p = 0.012), and, to lesser, extent with BC (β = -0.96, p = 0.33) compared to controls, mainly driven by cancer cases diagnosed less than 6 years after enrolment. These associations were not affected by subsequent adjustments for potential intermediate confounders, notably behaviours. Sensitivity analyses indicated that our findings were not affected by the way the inflammatory score was calculated. These observations call for further studies involving larger populations, larger variety of cancer types and repeated measures of larger panel of inflammatory markers.
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| 2. |
- Chatziioannou, Aristotelis, et al.
(författare)
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Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases
- 2017
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- We recently reported that differential gene expression and DNA methylation profiles in blood leukocytes of apparently healthy smokers predicts with remarkable efficiency diseases and conditions known to be causally associated with smoking, suggesting that blood-based omic profiling of human populations may be useful for linking environmental exposures to potential health effects. Here we report on the sex-specific effects of tobacco smoking on transcriptomic and epigenetic features derived from genome-wide profiling in white blood cells, identifying 26 expression probes and 92 CpG sites, almost all of which are affected only in female smokers. Strikingly, these features relate to numerous genes with a key role in the pathogenesis of cardiovascular disease, especially thrombin signaling, including the thrombin receptors on platelets F2R (coagulation factor II (thrombin) receptor; PAR1) and GP5 (glycoprotein 5), as well as HMOX1 (haem oxygenase 1) and BCL2L1 (BCL2-like 1) which are involved in protection against oxidative stress and apoptosis, respectively. These results are in concordance with epidemiological evidence of higher female susceptibility to tobacco-induced cardiovascular disease and underline the potential of blood-based omic profiling in hazard and risk assessment.
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| 3. |
- Georgiadis, Panagiotis, et al.
(författare)
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Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis
- 2017
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance.RESULTS: We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0-15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p.CONCLUSIONS: Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential.
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| 4. |
- Georgiadis, Panagiotis, et al.
(författare)
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Omics for prediction of environmental health effects : Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking.
- 2016
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.
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| 5. |
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| 6. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
- 2016
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Ingår i: AIMS Bioengineering. - Springfield, USA : AIMS press. - 2375-1487 .- 2375-1495. ; 3:4, s. 552-565
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiles of non-neural tissues through microarray technology could be used in schizophrenia studies, adding more information to the results from similar studies on postmortem brain tissue. The ultimate goal of such studies is to develop accessible biomarkers. Supervised machine learning methodologies were used, in order to examine if the gene expression from skin fibroblast cells could be exploited for the classification of schizophrenic subjects. A dataset of skin fibroblasts gene expression of schizophrenia patients was obtained from Gene Expression Omnibus database. After applying statistical criteria, we concluded to genes that present a differential expression between the schizophrenic patients and the healthy controls. Based on those genes, functional profiling was performed with the BioInfoMiner web tool. After the statistical analysis, 63 genes were identified as differentially expressed. The functional profiling revealed interesting terms and pathways, such as mitogen activated protein kinase and cyclic adenosine monophosphate signaling pathways, as well as immune-related mechanisms. A subset of 16 differentially expressed genes from fibroblast gene expression profiling that occurred after Support Vector Machines Recursive Feature Elimination could efficiently separate schizophrenic from healthy controls subjects. These findings suggest that through the analysis of fibroblast based gene 553 expression signature and with the application of machine learning methodologies we might conclude to a diagnostic classification model in schizophrenia.
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| 7. |
- Logotheti, Marianthi, et al.
(författare)
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Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder
- 2015
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Ingår i: Journal of Neuropsychopharmacology & Mental Health. - : OMICS International. - 2472-095X. ; 1:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder is a severe, lifelong psychiatric disease. The main underlying pathophysiology of the disease is still incomprehensible. Various studies have suggested that many genes of small impact in combination with environmental factors contribute to the expression of the disease. In this study comparative transcriptomic profiling to characterize skin fibroblasts’ gene expression of bipolar disorder patients compared to healthy controls has been performed. Skin fibroblast cells from bipolar disorder patients (n=10) and marched healthy controls (n=5) have been cultured. RNA was extracted and then hybridized onto Illumina Human HT-12 v4 Expression BeadChips. Differentially expressed genes between bipolar disorder samples and healthy controls were identified by performing unequal t-test on log 2 transformed expression values. The resulting gene list was obtained by setting the p-value threshold to 0.05 and by removing genes that presented a fold change ≥ |0.5| (in log 2 scale). We concluded to 457 differentially expressed genes. Among them 127 showed an upregulation and 330 were downregulated. Τhe expression alterations of selected genes were validated by quantitative real-time polymerase chain reaction. In order to derive better insight into the biological mechanisms related to the differentially expressed genes, the lists of significant genes were subjected to pathway analysis and target prioritization indicating various processes such as calcium ion homeostasis, positive regulation of apoptotic process and cellular response to retinoic acid.
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| 8. |
- Logotheti, Marianthi, et al.
(författare)
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Recent Advancements in Bipolar Disorder studies through Genomic, Epigenomic and Metagenomic Approaches
- 2019
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Ingår i: Journal of Psychiatry and Psychology Research. - : SciTech Central Inc.. - 2640-6136. ; 2:1, s. 56-66
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Forskningsöversikt (refereegranskat)abstract
- Bipolar disorder is a complex and highly heritable psychiatric disorder characterized by severe mood alterations. The precise geneticunderpinnings of the disease have not been identified so far, despite numerous genome-wide association findings. This review describes thecurrent state of genetic studies based on next generation sequencing technologies including whole exome and whole genome sequencing, aswell as RNA-sequencing and highlights the fact that the integration of these studies can reveal novel knowledge such as the functional roleof gene variants. However, due to the complexity of bipolar disorder, it is a compelling candidate for studies beyond DNA and RNAsequencing. Epigenetic alterations, defined as heritable but reversible modifications including DNA methylation, DNAhydroxymethylation, histone modifications and non-coding RNAs may be the link between genome and environment interactions.Additionally, a possible source of the reported immune activation in bipolar disorder is the micro biome of gastrointestinal tract, due torecent studies that indicate its pivotal role in brain function through the ‘gut-brain’ axis. The identification of methods able to modulate themicro biome emerges as a promising path for novel diagnostic and treatment options in bipolar disorder, thus the number of metagenomicstudies in bipolar disorder has substantially increased the last years. Overall, the paper aims to review the most recent literature ongenomic, epigenomic and metagenomic studies that have contributed to our understanding of the pathophysiology of bipolar disorder sofar. The paper also focuses on the exploitation of recent advancements in high-throughput technologies for the elucidation of bipolardisorder through different approaches that may provide complementary knowledge and concludes to the need for merging the gap betweenall the gathered knowledge from the analysis of high-throughput data.
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| 9. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
- 2016
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Ingår i: Biometrics & Biostatistics International Journal. - : MedCrave. - 2378-315X. ; 4:5
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Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia is a complex psychiatric disease that is affected by multiple genes, some of which could be used as biomarkers for specific diagnosis of the disease. In this work, we explore the power of machine learning methodologies for predicting schizophrenia, through the derivation of a biomarker gene signature for robust diagnostic classification purposes. Postmortem brain gene expression data from the anterior prefrontal cortex of schizophrenia patients were used as training data for the construction of the classifiers. Several machine learning algorithms, such as support vector machines, random forests, and extremely randomized trees classifiers were developed and their performance was tested. After applying the feature selection method of support vector machines recursive feature elimination a 21-gene model was derived. Using these genes for developing classification models, the random forests algorithm outperformed all examined algorithms achieving an area under the curve of 0.98 and sensitivity of 0.89, discriminating schizophrenia from healthy control samples with high efficiency. The 21-gene model that was derived from the feature selection is suggested for classifying schizophrenic patients, as it was successfully applied on an independent dataset of postmortem brain samples from the superior temporal cortex, and resulted in a classification model that achieved an area under the curve score of 0.91. Additionally, the functional analysis of the statistically significant genes indicated many mechanisms related to the immune system.
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| 10. |
- Vermeulen, Roel, et al.
(författare)
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Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma : univariate and functionally informed multivariate analyses
- 2018
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Ingår i: International Journal of Cancer. - : John Wiley & Sons. - 0020-7136 .- 1097-0215. ; 143:6, s. 1335-1347
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Tidskriftsartikel (refereegranskat)abstract
- Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.
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