Assessing the Role of DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a disease of chronic systemic inflammation (SI). In the present study, we used four datasets to explore whether methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) might be a marker of SI in new onset, untreated, and treated prevalent RA cases and/or a marker of treatment response to the tumour necrosis factor inhibitor (TNFi) etanercept. mdNLR was associated with increased odds of being a new onset RA case (OR= 2.32, 95% CI = 1.95-2.80, P < 2 x 10(-16)) and performed better in distinguishing new onset RA cases from controls compared to covariates: age, gender, and smoking status. In untreated preclinical RA cases and controls, mdNLR at baseline was associated with diagnosis of RA in later life after adjusting for batch (OR= 4.30, 95% CI = 1.52-21.71, P = 0.029) although no association was observed before batch correction. When prevalent RA cases were treated, there was no association with mdNLR in samples before and after batch correction (OR = 0.34, 95% CI = 0.05-1.82, P = 0.23), and mdNLR was not associated with treatment response to etanercept (OR = 1.10, 95% CI = 0.75-1.68, P = 0.64). Our results indicate that SI measured by DNA methylation data is indicative of the recent onset of RA. Although preclinical RA was associated with mdNLR, there was no difference in the mean mdNLR between preclinical RA cases and controls. mdNLR was not associated with RA case status if treatment for RA has commenced, and it is not associated with treatment response. In the future, mdNLR estimates may be used as a valuable research tool to reliably estimate SI in the absence of freshly collected blood samples

Chromosomal Alterations and Gene Expression Changes Associated with the Progression of Leukoplakia to Advanced Gingivobuccal Cancer

We present an integrative genome-wide analysis that can be used to predict the risk of progression from leukoplakia to oral squamous cell carcinoma (OSCC) arising in the gingivobuccal complex (GBC). We find that the genomic and transcriptomic profiles of leukoplakia resemble those observed in later stages of OSCC and that several changes are associated with this progression, including amplification of 8q24.3, deletion of 8p23.2, and dysregulation of DERL3, EIF5A2, ECT2, HOXC9, HOXC13, MAL, MFAP5 and NELL2. Comparing copy number profiles of primary tumors with and without lymph-node metastasis, we identify alterations associated with metastasis, including amplifications of 3p26.3, 8q24.21, 11q22.1, 11q22.3 and deletion of 8p23.2. Integrative analysis reveals several biomarkers that have never or rarely been reported in previous OSCC studies, including amplifications of 1p36.33 (attributable to MXRA8), 3q26.31 (EIF5A2), 9p24.1 (CD274), and 12q13.2 (HOXC9 and HOXC13). Additionally, we find that amplifications of 1p36.33 and 11q22.1 are strongly correlated with poor clinical outcome. Overall, our findings delineate genomic changes that can be used in treatment management for patients with potentially malignant leukoplakia and OSCC patients with higher risk of lymph-node metastasis

Chromosomal Alterations and Gene Expression Changes Associated with the Progression of Leukoplakia to Advanced Gingivobuccal Cancer

We present an integrative genome-wide analysis that can be used to predict the risk of progression from leukoplakia to oral squamous cell carcinoma (OSCC) arising in the gingivobuccal complex (GBC). We find that the genomic and transcriptomic profiles of leukoplakia resemble those observed in later stages of OSCC and that several changes are associated with this progression, including amplification of 8q24.3, deletion of 8p23.2, and dysregulation of DERL3, EIF5A2, ECT2, HOXC9, HOXC13, MAL, MFAP5 and NELL2. Comparing copy number profiles of primary tumors with and without lymph-node metastasis, we identify alterations associated with metastasis, including amplifications of 3p26.3, 8q24.21, 11q22.1, 11q22.3 and deletion of 8p23.2. Integrative analysis reveals several biomarkers that have never or rarely been reported in previous OSCC studies, including amplifications of 1p36.33 (attributable to MXRA8), 3q26.31 (EIF5A2), 9p24.1 (CD274), and 12q13.2 (HOXC9 and HOXC13). Additionally, we find that amplifications of 1p36.33 and 11q22.1 are strongly correlated with poor clinical outcome. Overall, our findings delineate genomic changes that can be used in treatment management for patients with potentially malignant leukoplakia and OSCC patients with higher risk of lymph-node metastasis

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors

Made available in DSpace on 2018-12-11T17:23:43Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-11-01Context: Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis. Objective: To identify a prognostic epigenetic signature in thyroid cancer. Design: Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database. Results: A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High-risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001). Conclusions: The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC.International Research Center, CIPE, A.C. Camargo Cancer Center and National Institute of Science and Technology in Oncogenomics, São Paulo 01509-010, SP, BrazilDepartment of Urology, Faculty of Medicine, UNESP, São Paulo State University, Botucatu 18618-970, SP, BrazilDepartment of Pathology, A.C. Camargo Cancer Center, São Paulo 01509-010, SP, BrazilEpigenetics Group; International Agency for Research on Cancer (IARC), Lyon 69372, FranceMRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, United KingdomDepartment of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo 01509-010, SP, BrazilDepartment of Clinical Genetics, Vejle Hospital and Institute of Regional Health Research, University of Southern Denmark, Vejle, 7100, Denmar

Evaluating Employee Satisfaction in a Production Firm

Import 04/11/2015Tématem mé bakalářské práce je hodnocení spokojenosti zaměstnanců ve výrobní firmě. Teoretická část je věnována pracovní spokojenost, dále pak pracovním podmínkám, komunikaci a systému odměňování. V praktické části bakalářské práce je vyhodnocení dotazníku z let 2011-2013.Topic of my thesis is to evaluate employee satisfaction in the company of production. The theoretical part is devoted to the job satisfaction, as well as working conditions, communications and remunerations system. In the practical part of the thesis is the evaluation of a questionnaire from the years 2011-2013.115 - Katedra managementudobř

Identifying and correcting epigenetics measurements for systematic sources of variation

Abstract Background Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features. In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results A sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation

Roadmap for investigating epigenome deregulation and environmental origins of cancer: Epigenetics and cancer

The interaction between the (epi)genetic makeup of an individual and his/her environmental exposure record (exposome) is accepted as a determinant factor for a significant proportion of human malignancies. Recent evidence has highlighted the key role of epigenetic mechanisms in mediating gene–environment interactions and translating exposures into tumorigenesis. There is also growing evidence that epigenetic changes may be risk factor‐specific (“fingerprints”) that should prove instrumental in the discovery of new biomarkers in cancer. Here, we review the state of the science of epigenetics associated with environmental stimuli and cancer risk, highlighting key developments in the field. Critical knowledge gaps and research needs are discussed and advances in epigenomics that may help in understanding the functional relevance of epigenetic alterations. Key elements required for causality inferences linking epigenetic changes to exposure and cancer are discussed and how these alterations can be incorporated in carcinogen evaluation and in understanding mechanisms underlying epigenome deregulation by the environment

Roadmap for investigating epigenome deregulation and environmental origins of cancer.

The interaction between the (epi)genetic makeup of an individual and his/her environmental exposure record (exposome) is accepted as a determinant factor for a significant proportion of human malignancies. Recent evidence has highlighted the key role of epigenetic mechanisms in mediating gene-environment interactions and translating exposures into tumorigenesis. There is also growing evidence that epigenetic changes may be risk factor-specific ('fingerprints') that should prove instrumental in the discovery of new biomarkers in cancer. Here, we review the state of the science of epigenetics associated with environmental stimuli and cancer risk, highlighting key developments in the field. Critical knowledge gaps and research needs are discussed as well as advances in epigenomics that may help an understanding of the functional relevance of epigenetic alterations. Key elements required for causality inferences linking epigenetic changes to exposure and cancer are discussed as well as how these alterations can be incorporated in carcinogen evaluation and in understanding mechanisms underlying epigenome deregulation by the environment