Introduction: Known carcinogens in the dust and fumes from the destruction of the World Trade Center (WTC) towers on 9 November 2001 included metals, asbestos, and organic pollutants, which have been shown to modify epigenetic status. Epigenome-wide association analyses (EWAS) using uniform (Illumina) methodology have identified novel epigenetic profiles of WTC exposure. Methods: We reviewed all published data, comparing differentially methylated gene profiles identified in the prior EWAS studies of WTC exposure. This included DNA methylation changes in blood-derived DNA from cases of cancer-free "Survivors" and those with breast cancer, as well as tissue-derived DNA from "Responders" with prostate cancer. Emerging molecular pathways related to the observed DNA methylation changes in WTC-exposed groups were explored and summarized. Results: WTC dust exposure appears to be associated with DNA methylation changes across the genome. Notably, WTC dust exposure appears to be associated with increased global DNA methylation; direct dysregulation of cancer genes and pathways, including inflammation and immune system dysregulation; and endocrine system disruption, as well as disruption of cholesterol homeostasis and lipid metabolism. Conclusion: WTC dust exposure appears to be associated with biologically meaningful DNA methylation changes, with implications for carcinogenesis and development of other chronic diseases.
Pubmed ID: 38131903 RIS Download
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Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware R package. Methods for Cluster analysis. Performs variety of types of cluster analysis and other types of processing on large microarray datasets.
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