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

Abstract

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 screen out signaling pathways related to key mRNAs of the ceRNA network, Gene Set Enrichment Analysis (GSEA) was performed.

Results: A total of 1201 DE mRNAs, 44 DE miRNAs, and 1613 DE circRNAs associated with PMOP were obtained. GO function annotation showed DE mRNAs were mainly related to inflammatory responses. KEGG analysis revealed DE mRNAs were mainly enriched in osteoclast differentiation, rheumatoid arthritis, hematopoietic cell lineage, and cytokine-cytokine receptor interaction pathways. We first identified 26 TFs and their target mRNAs. Combining DE miRNAs, miRNA-TF/mRNA pairs were obtained. Combining DE circRNAs, we constructed the ceRNA network contained 6 circRNAs, 4 miRNAs, 4 TFs, and 12 mRNAs. The expression levels of most genes detected by qRT-PCR were generally consistent with the microarray results. Combined with the qRT-PCR validation results, we eventually identified the ceRNA network that contained 4 circRNAs, 3 miRNAs, 3 TFs, and 9 mRNAs. The GSEA revealed that 9 mRNAs participate in many important signaling pathways, such as "olfactory transduction", "T cell receptor signaling pathway", and "neuroactive ligand-receptor interaction". These pathways have been reported to the occurrence and development of PMOP. To sum up, key mRNAs in the ceRNA network may participate in the development of osteoporosis by regulating related signal pathways.

Conclusions: A circRNA-associated ceRNA network containing TFs was established for PMOP. The study may help further explore the molecular mechanisms and may serve as potential biomarkers or therapeutic targets for PMOP.

Keywords: bioinformatics analysis; circular RNA (circRNA); competing endogenous network (ceRNA) network; postmenopausal osteoporosis (PMOP); transcription factor (TF).