66 research outputs found
Epigenetic Biomarkers for Environmental Exposures and Personalized Breast Cancer Prevention.
Environmental and lifestyle factors are believed to account for >80% of breast cancers; however, it is not well understood how and when these factors affect risk and which exposed individuals will actually develop the disease. While alcohol consumption, obesity, and hormone therapy are some known risk factors for breast cancer, other exposures associated with breast cancer risk have not yet been identified or well characterized. In this paper, it is proposed that the identification of blood epigenetic markers for personal, in utero, and ancestral environmental exposures can help researchers better understand known and potential relationships between exposures and breast cancer risk and may enable personalized prevention strategies
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Rationale, Study Design, and Cohort Characteristics for the Markers for Environmental Exposures (MEE) Study.
Environmental factors have been linked to many diseases and health conditions, but reliable assessment of environmental exposures is challenging. Developing biomarkers of environmental exposures, rather than relying on self-report, will improve our ability to assess the association of such exposures with disease. Epigenetic markers, most notably DNA methylation, have been identified for some environmental exposures, but identification of markers for additional exposures is still needed. The rationale behind the Markers for Environmental Exposures (MEE) Study was to (1) identify biomarkers, especially epigenetic markers, of environmental exposures, such as pesticides, air/food/water contaminants, and industrial chemicals that are commonly encountered in the general population; and (2) support the study of potential relationships between environmental exposures and health and health-related factors. The MEE Study is a cross-sectional study with potential for record linkage and follow-up. The well-characterized cohort of 400 postmenopausal women has generated a repository of biospecimens, including blood, urine, and saliva samples. Paired data include an environmental exposures questionnaire, a breast health questionnaire, dietary recalls, and a food frequency questionnaire. This work describes the rationale, study design, and cohort characteristics of the MEE Study. In addition to our primary research goals, we hope that the data and biorepository generated by this study will serve as a resource for future studies and collaboration
Recognition and Treatment of Cognitive Dysfunction in Major Depressive Disorder
Major Depressive Disorder (MDD) is a prevalent, chronic, disabling, and multidimensional mental disorder. Cognitive dysfunction represents a core diagnostic and symptomatic criterion of MDD, and is a principal determinant of functional non-recovery. Cognitive impairment has been observed to persist despite remission of mood symptoms, suggesting dissociability of mood and cognitive symptoms in MDD. Recurrent impairments in several domains including, but not limited to, executive function, learning and memory, processing speed, and attention and concentration, are associated with poor psychosocial and occupational outcomes. Attempts to restore premorbid functioning in individuals with MDD requires regular screenings and assessment of objective and subjective measures of cognition by clinicians. Easily accessible and cost-effective tools such as the THINC-integrated tool (THINC-it) are suitable for use in a busy clinical environment and appear to be promising for routine usage in clinical settings. However, antidepressant treatments targeting specific cognitive domains in MDD have been insufficiently studied. While select antidepressants, e.g., vortioxetine, have been demonstrated to have direct and independent pro-cognitive effects in adults with MDD, research on additional agents remains nascent. A comprehensive clinical approach to cognitive impairments in MDD is required. The current narrative review aims to delineate the importance and relevance of cognitive dysfunction as a symptomatic target for prevention and treatment in the phenomenology of MDD
Promoter DNA Methylation of Oncostatin M receptor-β as a Novel Diagnostic and Therapeutic Marker in Colon Cancer
In addition to genetic changes, the occurrence of epigenetic alterations is associated with accumulation of both genetic and epigenetic events that promote the development and progression of human cancer. Previously, we reported a set of candidate genes that comprise part of the emerging “cancer methylome”. In the present study, we first tested 23 candidate genes for promoter methylation in a small number of primary colon tumor tissues and controls. Based on these results, we then examined the methylation frequency of Oncostatin M receptor-β (OSMR) in a larger number of tissue and stool DNA samples collected from colon cancer patients and controls. We found that OSMR was frequently methylated in primary colon cancer tissues (80%, 80/100), but not in normal tissues (4%, 4/100). Methylation of OSMR was also detected in stool DNA from colorectal cancer patients (38%, 26/69) (cut-off in TaqMan-MSP, 4). Detection of other methylated markers in stool DNA improved sensitivity with little effect on specificity. Promoter methylation mediated silencing of OSMR in cell lines, and CRC cells with low OSMR expression were resistant to growth inhibition by Oncostatin M. Our data provide a biologic rationale for silencing of OSMR in colon cancer progression and highlight a new therapeutic target in this disease. Moreover, detection and quantification of OSMR promoter methylation in fecal DNA is a highly specific diagnostic biomarker for CRC
Novel polymorphisms in caspase-8 are associated with breast cancer risk in the California Teachers Study
Novel polymorphisms in caspase-8 are associated with breast cancer risk in the California Teachers Study.
Trends in Treatment Patterns and Clinical Outcomes in Young Women Diagnosed With Ductal Carcinoma In Situ
Clinical Implementation of a Breast Cancer Risk Assessment Program in a Multiethnic Patient Population: Which Risk Model to Use?
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