Comprehensive analysis of the m6A-related molecular patterns and diagnostic biomarkers in osteoporosis - Bai, Qiong; Shi, Min; Sun, Xinli; Lou, Qiu; Peng, Hangya; Qu, Zhuan; Fan, Jiashuang; Dai, Lifen (Frontiers in Endocrinology, 2022)
Background N6-methyladenosine (m6A) modification is a critical epigenetic modification in eukaryotes and involves several biological processes and occurrences of diseases. However, the roles and regulatory mechanisms of m6A regulators in Osteoporosis (OP) remain unclear. Thus, the purposes of this study are exploration the roles and mechanisms of m6A regulators in OP. Methods The mRNA and microRNA (miRNA) expression profiles were respectively obtained from GSE56815, GSE7158, and GSE93883 datasets in Gene Expression Omnibus (GEO). The differential expression of 21 m6A regulators between high-bone mineral density (BMD) and low-BMD women was identified. Then, a consensus clustering of low-BMD women was performed based on differentially expressed (DE)-m6A regulators. The m6A-related differentially expressed genes (DEGs) the differentially expressed miRNAs (DE-miRNAs), and biological functions were investigated. Moreover, A weighted gene co-expression network analysis (WGCNA) was constructed to identify the OP-related hub modules, hub genes, and the functional pathways. Then, an m6A regulator-targets-pathway network and the competing endogenous RNA (ceRNA) network in key modules were constructed. A least absolute shrinkage and selection operation (LASSO) Cox regression model and a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model were constructed to identify the candidate genes for OP prediction. And the receiver operator characteristic curves (ROCs) were used to validate the performances of predictive models and candidate genes. Results A total of 10,520 DEGs, 13 DE-m6A regulators, and 506 DE-miRNAs between high-BMD and low-BMD women were identified. And two m6A-related subclusters with 13 DE-m6A regulators were classified for OP. There were 5260 m6A-related DEGs identified between two m6A-related subclusters, the PI3K-Akt, MAPK, immune-related pathways, and bone metabolism was mainly enriched in cluster 2. And cell cycle-related pathways, RNA methylation, and cell death-related pathways were significantly involved in cluster 1. Five modules were identified as key modules based on WGCNA, and an m6A regulator-target gene-pathway network and the ceRNA network were constructed in module brown. Besides, three m6A regulators (FTO, YTHDF2, and CBLL1) were selected as the candidate genes for OP. Conclusion M6A regulators play an important role in the occurrences and diagnosis of OP.