AUTHOR=Lin Shi , Wu Jianjun , Chen Baixing , Li Shaoshuo , Huang Hongxing TITLE=Identification of a Potential MiRNA–mRNA Regulatory Network for Osteoporosis by Using Bioinformatics Methods: A Retrospective Study Based on the Gene Expression Omnibus Database JOURNAL=Frontiers in Endocrinology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.844218 DOI=10.3389/fendo.2022.844218 ISSN=1664-2392 ABSTRACT=Introduction

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass, impairment of bone microstructure, and a high global morbidity rate. There is increasing evidence that microRNAs (miRNAs) are associated with the pathogenesis of OP. Weighted gene co-expression network analysis (WGCNA) is a systematic method for identifying clinically relevant genes involved in disease pathogenesis. However, the study of the miRNA–messenger RNA (mRNA) regulatory network in combination with WGCNA in OP is still lacking.

Methods

The GSE93883 and GSE7158 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs) and differentially expressed genes (DEGs) were analyzed with the limma package. OP-related miRNAs from the most clinically relevant module were identified by the WGCNA method. The overlap of DE-miRNAs and OP-related miRNAs was identified as OP-related DE-miRNAs. Both upstream transcription factors and downstream targets of OP-related DE-miRNAs were predicted by FunRich. An intersection of predicted target genes and DEGs was confirmed as downstream target genes of OP-related DE-miRNAs. With the use of clusterProfiler in R, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on target genes. Finally, both the protein–protein interaction (PPI) network and miRNA–mRNA network were constructed and analyzed.

Results

A total of 79 OP-related DE-miRNAs were obtained, most of which were predicted to be regulated by specificity protein 1 (SP1). Subsequently, 197 downstream target genes were screened out. The target genes were enriched in multiple pathways, including signaling pathways closely related to the onset of OP, such as Ras, PI3K-Akt, and ErbB signaling pathways. Through the construction of the OP-related miRNA–mRNA regulatory network, a hub network that may play a prominent role in the formation of OP was documented.

Conclusion

By using WGCNA, we constructed a potential OP-related miRNA–mRNA regulatory network, offering a novel perspective on miRNA regulatory mechanisms in OP.