ArticleFrontiers in genetics2020
A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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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
42 citing papers in PubMed, 85 citations in OpenAlex.
- RNA-Based Biomarkers for Diagnostic Discrimination of Ischemic and Hemorrhagic Stroke: A Systematic Review.Journal of clinical medicine · 2026Review
- Integrative Bioinformatics Analysis of hsa-miR-21 in Breast Cancer Reveals a Prognostic Hub-Gene Signature.International journal of molecular sciences · 2026Article
- Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network and Identification of Hub lncRNAs in Molecular Subtypes of Gastric Cancer as Potential Prognostic Indicators.Bioinformatics and biology insights · 2026Article
- Non-Coding RNAs in Asthma: Regulators of Eosinophil Biology and Airway Inflammation.Diagnostics (Basel, Switzerland) · 2025Review
- Altered miRNA expression in the lesions of cutaneous leishmaniasis caused by L. major and L. tropica with insights into apoptosis regulation.Scientific reports · 2025Article
- Role of Non-Coding RNAs in White and Brown Adipose Tissue Differentiation and Development.Non-coding RNA · 2025Review
- Role of the circular RNAs/microRNA/messenger RNA axis in renal cell carcinoma: From gene regulation to metabolism and immunity.iScience · 2025Review
- Candidate Biomarkers Associated With Circulating Tumor Cell Status in Metastatic Colorectal Cancer.Journal of clinical laboratory analysis · 2025Article
- Progress in understanding the role and mechanism of miRNAs in osteoporosis.Frontiers in endocrinology · 2025Review
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- GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.Bioinformatics advances · 2025Article
- Unlocking the potential of miRNAs in detecting pulmonary tuberculosis: prospects and pitfalls.Expert reviews in molecular medicine · 2024Review
- Heparin Differentially Regulates the Expression of Specific miRNAs in Mesenchymal Stromal Cells.International journal of molecular sciences · 2024Article
- Article
- Donor Sites and Harvesting Techniques Affect miRNA Cargos of Extracellular Vesicles Released by Human Adipose-Derived Mesenchymal Stromal Cells.International journal of molecular sciences · 2024Article
- Hypoxia-Inducible Factor-Dependent and Independent Mechanisms Underlying Chemoresistance of Hypoxic Cancer Cells.Cancers · 2024Review
- Integrative analysis in head and neck cancer reveals distinct role of miRNome and methylome as tumour epigenetic drivers.Scientific reports · 2024Article
- ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers.Non-coding RNA research · 2024Article
- Endothelin-1 and Its Role in Cancer and Potential Therapeutic Opportunities.Biomedicines · 2024Review
- Predict lncRNA-drug associations based on graph neural network.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are a class of important non-coding RNAs, which play important roles in tumorigenesis and development by targeting oncogenes or tumor suppressor genes. One miRNA can regulate multiple genes, and one gene can be regulated by multiple miRNAs. To promote the clinical application of miRNAs, two fundamental questions should be answered: what's the regulatory mechanism of a miRNA to a gene, and which miRNAs are important for a specific type of cancer. In this study, we propose a miRNA influence capturing (miRNAInf) to decipher regulation relations of miRNAs on target genes and identify critical miRNAs in cancers in a systematic approach. With the pair-wise miRNA/gene expression profiles data, we consider the assigning problem of a miRNA on target genes and determine the regulatory mechanisms by computing the Pearson correlation coefficient between the expression changes of a miRNA and that of its target gene. Furthermore, we compute the relative local influence strength of a miRNA on its target gene. Finally, integrate the local influence strength and target gene's importance to determine the critical miRNAs involved in specific cancer. Results on breast, liver and prostate cancers show that positive regulations are as common as negative regulations. The top-ranked miRNAs show great potential as therapeutic targets driving cancer to a normal state, and they are demonstrated to be closely related to cancers based on biological functional analysis, drug sensitivity/resistance analysis and survival analysis. This study will be helpful for the discovery of critical miRNAs and development of miRNAs-based clinical therapeutics.
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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.