ArticleGenes & genomics2020
Two way network construction and analysis of mRNA, miRNA and lncRNA reveals critical regulators and regulatory modules in cardiovascular diseases.
Article in Genes & genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 5 citations in OpenAlex.
- Construction and validation of a lncRNA/circRNA-miRNA-mRNA ceRNA network in allergic rhinitis pathogenesis.BMC medical genomics · 2026Article
- 3'-UTR Polymorphisms in Thymidylate Synthase with Colorectal Cancer Prevalence and Prognosis.Journal of personalized medicine · 2021Article
- Association betweenJournal of personalized medicine · 2021Article
- The Preventive Effect of Cardiac Sympathetic Denervation Induced by 6-OHDA on Myocardial Ischemia-Reperfusion Injury: The Changes of lncRNA/circRNAs-miRNA-mRNA Network of the Upper Thoracic Spinal Cord in Rats.Oxidative medicine and cellular longevity · 2021Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular diseases contribute to the leading cause of deaths (31%) in the world population.
objectiveThe objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression.
methodWe applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used 'diseasome to interactome' and 'interactome to diseasome' approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition.
resultsThe top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions.
conclusionThe identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.
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.