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| Predictive analysis of key lncRNA and its regulatory effects in postmenopausal osteoporosis based on random forest algorithm#br# |
| CHEN Jingheng CHEN Kaiqi HE Dadong SHI Yuxiong |
| Department of Orthopedics, Panyu Hospital of Chinese Medicine, Guangdong Province, Guangzhou 511400, China |
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Abstract Objective To predictive analyze the regulatory effects of key long noncoding RNA (lncRNA) in postmenopausal osteoporosis (PMOP) by random forest algorithm. Methods The chip data of patients with PMOP was obtained by searching gene expression omnibus database, and the retrieval time was from the inception to December 2020. Key lncRNAs were screened by random forest algorithm, lncRNA target genes were predicted by starBase V2.0 database, and the lncRNA-target gene regulatory network was constructed. STRING V11 was used for proteinprotein interaction analysis of target genes, and R language and KOBAS database were used for GO and KEGG enrichment analysis. Results A total of 127 lncRNA expression data were obtained, ten key lncRNAs were screened by random forest model, and 98 target genes were predicted by starBase V2.0. The target genes were mainly enriched in 13 GO processes such as RNA polymerase activity and 12 KEGG signaling pathways such as galactose metabolism. Conclusion In this study, through mining PMOP gene chip data, the key lncRNAs are screened based on random forest algorithm and the biological process and signal pathway involved in their target genes are predicted. It is concluded that lncRNAs may be involved in the regulation of proliferation and differentiation of osteoblasts and osteoclasts, transcription and translation of genetic materials, and glucose and lipid metabolism of the body.
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