ArticleHuman genomics2016
Systematic analysis of the molecular mechanism underlying atherosclerosis using a text mining approach.
Article in Human genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- Quantitative proteomic landscape of unstable atherosclerosis identifies molecular signatures and therapeutic targets for plaque stabilization.Communications biology · 2023Article
- Atorvastatin and blood flow regulate expression of distinctive sets of genes in mouse carotid artery endothelium.Current topics in membranes · 2021Article
- Resveratrol Treatment Is Associated with Lipid Regulation and Inhibition of Lipoprotein-Associated Phospholipase A2 (Lp-PLA2) in Rabbits Fed a High-Fat Diet.Evidence-based complementary and alternative medicine : eCAM · 2020Article
- Conceptualization of Heterogeneity of Chronic Diseases and Atherosclerosis as a Pathway to Precision Medicine: Endophenotype, Endotype, and Residual Cardiovascular Risk.International journal of chronic diseases · 2020Review
- Atherosclerosis-associated differentially methylated regions can reflect the disease phenotype and are often at enhancers.Atherosclerosis · 2019Article
- Endothelial-Vascular Smooth Muscle Cells Interactions in Atherosclerosis.Frontiers in cardiovascular medicine · 2018Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtherosclerosis is one of the common health threats all over the world. It is a complex heritable disease that affects arterial blood vessels. Chronic inflammatory response plays an important role in atherogenesis. There has been little success in fully identifying functionally important genes in the pathogenesis of atherosclerosis.
resultsIn the present study, we performed a systematic analysis of atherosclerosis-related genes using text mining. We identified a total of 1312 genes. Gene ontology (GO) analysis revealed that a total of 35 terms exhibited significance (p < 0.05) as overrepresented terms, indicating that atherosclerosis invokes many genes with a wide range of different functions. Pathway analysis demonstrated that the most highly enriched pathway is the Toll-like receptor signaling pathway. Finally, through gene network analysis, we prioritized 48 genes using the hub gene method.
conclusionsOur study provides a valuable resource for the in-depth understanding of the mechanism underlying atherosclerosis.
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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.