Publication: The MicroRNA interaction network of lipid diseases
Issued Date
2017-09-22
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16648021
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2-s2.0-85030116629
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Mahidol University
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SCOPUS
Bibliographic Citation
Frontiers in Genetics. Vol.8, No.SEP (2017)
Suggested Citation
Abdul H. Kandhro, Watshara Shoombuatong, Chanin Nantasenamat, Virapong Prachayasittikul, Pornlada Nuchnoi The MicroRNA interaction network of lipid diseases. Frontiers in Genetics. Vol.8, No.SEP (2017). doi:10.3389/fgene.2017.00116 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/41734
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Title
The MicroRNA interaction network of lipid diseases
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Abstract
© 2017 Kandhro, Shoombuatong, Nantasenamat, Prachayasittikul and Nuchnoi. Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets.
