ArticleBMC medical genomics2010
Predicting environmental chemical factors associated with disease-related gene expression data.
Article in BMC medical genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Extracellular Vesicles in Environmental Toxicological Studies: Association between Urinary Concentrations of Phthalate Metabolites and Exosomal miRNA Expression Profiles.International journal of molecular sciences · 2024Article
- Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases.Toxicology and applied pharmacology · 2019Article
- Epigenetics as a mechanism linking developmental exposures to long-term toxicity.Environment international · 2018Review
- BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening.Computational toxicology (Amsterdam, Netherlands) · 2018Article
- The role of cognitive activity in cognition protection: from Bedside to Bench.Translational neurodegeneration · 2017Review
- Changes in mammary histology and transcriptome profiles by low-dose exposure to environmental phenols at critical windows of development.Environmental research · 2017Article
- Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking.Scientific reports · 2016Article
- Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.PLoS computational biology · 2015Article
- Development of a stable cell line with an intact PGC-1α/ERRα axis for screening environmental chemicals.Biochemical and biophysical research communications · 2014Article
- The microRNAs as potential biomarkers for predicting the onset of aflatoxin exposure in human beings: a review.Frontiers in microbiology · 2014Review
- Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data.BMC medical genomics · 2013Article
- Annual fasting; the early calories restriction for cancer prevention.BioImpacts : BI · 2012Article
- Environmental chemicals and microRNAs.Mutation research · 2011Review
- Preferential regulation of miRNA targets by environmental chemicals in the human genome.BMC genomics · 2011Article
- NCBI GEO: archive for functional genomics data sets--10 years on.Nucleic acids research · 2011Article
- The Comparative Toxicogenomics Database: update 2011.Nucleic acids research · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMany common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.
methodsWe integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.
resultsWe were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.
conclusionsWe have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.