ReviewCurrent atherosclerosis reports2016
Transcriptomic Signature of Atherosclerosis in the Peripheral Blood: Fact or Fiction?
Review in Current atherosclerosis reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed, 16 citations in OpenAlex.
- Differential Expression of Subsets of Genes Related to HDL Metabolism and Atherogenesis in the Peripheral Blood in Coronary Artery Disease.Current issues in molecular biology · 2023Article
- Circular RNAs Variously Participate in Coronary Atherogenesis.Current issues in molecular biology · 2023Review
- Identification of Transcriptomic Differences between Lower Extremities Arterial Disease, Abdominal Aortic Aneurysm and Chronic Venous Disease in Peripheral Blood Mononuclear Cells Specimens.International journal of molecular sciences · 2021Article
- The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk.Circulation research · 2021Review
- Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles.Frontiers in molecular biosciences · 2021Article
- Unveiling ncRNA regulatory axes in atherosclerosis progression.Clinical and translational medicine · 2020Review
- Expression of TLR4-PTGE2 signaling genes in atherosclerotic carotid plaques and peripheral blood.Molecular biology reports · 2019Article
- Dysregulation of MicroRNA Regulatory Network in Lower Extremities Arterial Disease.Frontiers in genetics · 2019Article
- Article
- Plasma proteomic analysis reveals altered protein abundances in cardiovascular disease.Journal of translational medicine · 2018Article
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
No grant is acknowledged in the PubMed record.
Abstract
The notion that gene expression signatures in blood can serve as biomarkers of disease states is not new. In the case of atherosclerosis, and coronary artery disease in particular, whether changes in gene expression in peripheral blood mononuclear cells reflects disease processes occurring in the vessel wall remains controversial. When comparing 15 studies that identified 706 differentially expressed genes, only 23 genes were replicated in 2 to 3 studies, at most. This low level of replication may reflect sample sizes too small to overcome heterogeneity in the response to disease. Genetic differences affect how each person responds to disease and what genes are altered. Recent studies with larger cohorts (over 5000 individuals) that considered the effect of common genetic variants still could not claim disease signature genes as biomarkers suggesting that even larger case-control studies will be required to achieve the required statistical power. On the other hand, out of 7 studies that identified 58 microRNAs, 12 were concordant in 2 or more studies, suggesting that microRNAs may be less affected by genetic differences and more accurately reflect the disease process. Here, we review the current state of knowledge on expression profiling and its utility for predicting coronary artery disease status and mortality.
Indexed as
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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.