ArticleBMC genomics2016
Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Genome-wide association study of early ischaemic stroke risk in Brazilian individuals with sickle cell disease implicates ADAMTS2 and CDK18 and uncovers novel loci.British journal of haematology · 2023Pooled it
- Transcriptome Analysis of Myocardial Ischemic-Hypoxic Injury in Rats and Hypoxic H9C2 Cells.ESC heart failure · 2024Article
- Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression.Bioinformatics (Oxford, England) · 2023Article
- Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance.PloS one · 2022Article
- Crosstalk between Circulatory Microenvironment and Vascular Endothelial Cells in Acute Myocardial Infarction.Journal of inflammation research · 2021Article
- UBA6 and Its Bispecific Pathways for Ubiquitin and FAT10.International journal of molecular sciences · 2019Review
- IL-18/IL-37/IP-10 signalling complex as a potential biomarker for discriminating active and latent TB.PloS one · 2019Observational
- Whole Transcriptome Profiling: An RNA-Seq Primer and Implications for Pharmacogenomics Research.Clinical and translational science · 2018Review
- Estimation of Sobol's Sensitivity Indices under Generalized Linear Models.Communications in statistics: theory and methods · 2018Article
- Whole Transcriptome Sequencing Analyses Reveal Molecular Markers of Blood Pressure Response to Thiazide Diuretics.Scientific reports · 2017Article
- Erratum to: Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles.BMC genomics · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAlterations in gene expression are key events in disease etiology and risk. Poor reproducibility in detecting differentially expressed genes across studies suggests individual genes may not be sufficiently informative for complex diseases, such as myocardial infarction (MI). Rather, dysregulation of the 'molecular network' may be critical for pathogenic processes. Such a dynamic network can be built from pairwise non-linear interactions.
resultsWe investigate non-linear interactions represented in mRNA expression profiles that integrate genetic background and environmental factors. Using logistic regression, we test the association of individual GWAS-based candidate genes and non-linear interaction terms (between these mRNA expression levels) with MI. Based on microarray data in CATHGEN (CATHeterization in GENetics) and FHS (Framingham Heart Study), we find individual genes and pairs of mRNAs, encoded by 41 MI candidate genes, with significant interaction terms in the logistic regression model. Two pairs replicate between CATHGEN and FHS (CNNM2|GUCY1A3 and CNNM2|ZEB2). Analysis of RNAseq data from GTEx (Genotype-Tissue Expression) shows that 20 % of these disease-associated RNA pairs are co-expressed, further prioritizing significant interactions. Because edges in sparse co-expression networks formed solely by the 41 candidate genes are unlikely to represent direct physical interactions, we identify additional RNAs as links between network pairs of candidate genes. This approach reveals additional mRNAs and interaction terms significant in the context of MI, for example, the path CNNM2|ACSL5|SCARF1|GUCY1A3, characterized by the common themes of magnesium and lipid processing.
conclusionsThe results of this study support a role for non-linear interactions between genes in MI and provide a basis for further study of MI systems biology. mRNA expression profiles encoded by a limited number of candidate genes yield sparse networks of MI-relevant interactions that can be expanded to include additional candidates by co-expression analysis. The non-linear interactions observed here inform our understanding of the clinical relevance of gene-gene interactions in the pathophysiology of MI, while providing a new strategy in developing clinical biomarker panels.
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.