Identification of Serum Exosome-Derived circRNA-miRNA-TF-mRNA Regulatory Network in Postmenopausal Osteoporosis Using Bioinformatics Analysis and Validation in Peripheral Blood-Derived Mononuclear Cells
Dong Q., Han Z., Tian L.
Laboratory Study on Chronic Inflammation, Autoimmune Research, published in Front Endocrinol (Lausanne) (2022) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Front Endocrinol (Lausanne) (2022)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 35757392
- PMCID
- PMC9218277
- DOI
- 10.3389/fendo.2022.899503
- Citations
- 27
Abstract (original English)
Background Osteoporosis is one of the most common systemic metabolic bone diseases, especially in postmenopausal women. Circular RNA (circRNA) has been implicated in various human diseases. However, the potential role of circRNAs in postmenopausal osteoporosis (PMOP) remains largely unknown. The study aims to identify potential biomarkers and further understand the mechanism of PMOP by constructing a circRNA-associated ceRNA network. Methods The PMOP-related datasets GSE161361, GSE64433, and GSE56116 were downloaded from the Gene Expression Omnibus (GEO) database and were used to obtain differentially expressed genes (DEGs). Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to determine possible relevant functions of differentially expressed messenger RNAs (mRNAs). The TRRUST database was used to predict differential transcription factor (TF)-mRNA regulatory pairs. Afterwards, combined CircBank and miRTarBase, circRNA-miRNA as well as miRNA-TF pairs were constructed. Then, a circRNA-miRNA-TF-mRNA network was established. Next, the correlation of mRNAs, TFs, and PMOP was verified by the Comparative Toxicogenomics Database. And expression levels of key genes, including circRNAs, miRNAs, TFs, and mRNAs in the ceRNA network were further validated by quantitative real-time PCR (qRT-PCR). Furthermore, to
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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