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MiRNA-Gene Interaction Network Construction Strategy to Discern Promising Traditional Chinese Medicine against Osteoporosis.

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.

SUBMITTER: Li L

PROVIDER: S-EPMC9217536 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Traditional, complementary, and alternative medicine (TCAM) has attracted increasing attention in developed countries, but its mainstream status in China, the home of TCAM, is unclear. Over the period of 2004-2016, we analyze the health resources and health resource utilization of traditional medicine in traditional Chinese medicine (TCM) hospitals in China. Methods:Over 2004-2016, we obtained data from all TCM hospitals in all Chinese provinces to create a hospital-based, longitudinal dataset. TCM health resources and their utilization were measured by two outcome variables: (1) primary outcome variables comprising the proportion of TCM physicians, TCM pharmacists, revenue from TCM drugs, and TCM prescriptions and (2) the secondary outcome variables, as proxies of westernization for TCM hospitals, comprising the number of medical equipment above RMB 10,000 and the proportion of surgery in inpatient visits. We used linear regression models with hospital-fixed effects to analyze time trends for the outcome variables. Results:The number of public TCM hospitals remained stable from 2004 to 2016, while the number of private TCM hospitals increased from 294 in 2004 to 1560 in 2016. There was a small percentage increase in the proportion of TCM physicians (0.280%), TCM pharmacists (0.298%), and revenue from Chinese medicines (0.331%) and TCM prescriptions (1.613%) per hospital per year. Chinese drugs accounted for less than a half of the total drug prescriptions, and accordingly, just one-third of the drug revenue was from Chinese medicines at TCM hospitals. The proportions of physicians, pharmacists, revenue from Chinese drug sales, and traditional medicine prescriptions never reach the 60% benchmark target for mainstream in TCM hospitals. As proxies for Western medicine practices in TCM hospitals, the number of medical equipment above RMB 10,000 rapidly rose by over 13 percent per hospital per year, but the proportion of inpatient surgeries declined by 0.830 percentage points per hospital per year, reflecting a mixed trend in the use of Western medicine practices. Conclusion:For the 2004-2016 period, traditional medicine, although making progress towards the mainstream benchmark of 60% TCM services, was still not mainstream at TCM hospitals.

Project description:BackgroundDue to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of the TCM.MethodsTo address this challenge, we developed a large-scale simultaneous interaction prediction approach (SiPA) integrated one interaction network based simple inference model (SIM), focusing on 'logical relevance' between compounds, proteins or diseases, and another compound-target correlation space based interaction prediction model (CTCS-IPM) that was built on the basis of the canonical correlation analysis (CCA) to estimate the position of compounds (or targets) in compound-protein correlated space. Then SiPA was applied to discover reliable multiple interactions for interaction network expansion of a TCM, compound Salvia miltiorrhiza. By means of network analysis, potential active compounds and their related network synergy underlying cardiovascular diseases were evaluated between expanded and original interaction networks. Part of new interactions were validated with existing experimental evidence and molecular docking.ResultsAs evaluated with known test dataset, the established combination approach was proved to make highly accurate prediction, showing a well prediction performance for the SIM and a high recall rate of 85.2% for the CTCS-IPM. Then 710 pairs of new compound-target interactions, 24 pairs of new compound-cardiovascular disease interactions and 294 pairs of new cardiovascular disease-protein interactions were predicted for compound Salvia miltiorrhiza. Results of network analysis suggested the network expansion could dramatically improve the completeness and effectiveness of the network. Validation results of literature and molecular docking manifested that inferred interactions had good reliability.ConclusionsWe provided a practical and efficient way for large-scale inference of multiple interactions of TCM ingredients, which was not limited by the lack of negative samples, sample size and target 3D structures. SiPA could help researchers more accurately prioritize the effective compounds and more completely explore network synergy of TCM for treating specific diseases, indicating a potential way for effectively identifying candidate compound (or target) in drug discovery.

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MiRNA-Gene Interaction Network Construction Strategy to Discern Promising Traditional Chinese Medicine against Osteoporosis.

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