ArticleBioMed research international2022
miRNA-Gene Interaction Network Construction Strategy to Discern Promising Traditional Chinese Medicine against Osteoporosis.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
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- Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis.BMC musculoskeletal disorders · 2024Article
- A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris.Computational intelligence and neuroscience · 2022Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a widespread bone disease that affects million cases annually. The underlying mechanisms behind the progress of osteoporosis remain enigmatic, which limits detections of biomarkers and therapeutic targets. Hence, this study was aimed at exploring hub molecules to better understand the mechanism of osteoporosis development and discover the traditional Chinese medicine potential drugs for osteoporosis. miRNA and gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Weighted correlation network analysis (WGCNA) was used to identify the key modules for osteoporosis. DIANA Tools was applied to perform pathway enrichment. A miRNA-gene interaction network was constructed, and hub miRNAs and genes were distinguished using Cytoscape software. Receiver operating characteristic (ROC) curves of hub miRNAs and genes were plotted, and correlations with hub genes and osteoporosis-associated factors were evaluated. Potential drugs for osteoporosis in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were screened, and molecular docking models between these drugs and target genes were showed by AutoDock tools. Two hub modules, 1 miRNA module and 1 gene module, were identified to be the most strongly correlated with osteoporosis by using WGCNA. Then, 3 KEGG pathways including focal adhesion, PI3K-Akt signaling pathway, and gap junction were shared pathways enriched with the miRNAs and genes screened out by WGCNA and differential expression analyses. Finally, after constructing a miRNA-gene interaction network, 6 hub miRNAs (hsa-miR-18b-3p, hsa-miR-361-3p, hsa-miR-484, hsa-miR-519e-5p, hsa-miR-940, and hsa-miR-1275) and 6 hub genes (THBS1, IFNAR2, ARHGAP5, TUBB2B, FLNC, and NTF3) were detected. ROC curves showed good performances of miRNAs and genes for osteoporosis. Correlations with hub genes and osteoporosis-associated factors suggested implicational roles of them for osteoporosis. Based on these hub genes, 3 natural compounds (kainic acid, uridine, and quercetin), which were the active ingredients of 192 herbs, were screened out, and a target-compound-herb network was extracted using TCMSP. Molecular docking models of kainic acid-NTF3, uridine-IFNAR2, and quercetin-THBS1 were exhibited with AutoDock tools. Our study sheds light on the pathogenesis of osteoporosis and provides promising therapeutic targets and traditional Chinese medicine drugs for osteoporosis.
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