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基于转录组分析构建骨折愈合过程中成骨作用的动态ceRNA调控网络。

Construction of dynamic ceRNA regulatory networks in osteogenesis during fracture healing based on transcriptomic analysis.

作者信息

Guo Shuhang, Wang Shen, Yuan Shaoxun, Gu Xinyi, Deng Jin, Huang Chen, Zeng Xinyi, Lu Qingguo, Yin Xiaofeng

机构信息

Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, China.

Key Laboratory of Trauma and Neural Regeneration (Peking University), Beijing, China.

出版信息

Sci Rep. 2025 Jul 24;15(1):26946. doi: 10.1038/s41598-025-12505-6.

Abstract

Fracture healing is a complex biological process. This study aimed to investigate the key molecules involved in fracture healing and their potential competing endogenous RNA (ceRNA) regulatory mechanisms within the first 28 days post-fracture using bioinformatics methods. The experiment was conducted on 15 adult male SD rats, with tibia callus tissue samples collected at days 0, 3, 7, 14, and 28 (n = 3) post-fracture. RNA-Seq was used for high-throughput transcriptome sequencing, followed by differential expression analysis to identify differentially expressed genes (DEGs), long non-coding RNA (DELs), and microRNA (DEMs) at different stages. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized with Cytoscape. GO and KEGG enrichment analyses were performed to explore potential biological mechanisms. miRNA-mRNA interactions were predicted using TargetScan, miRWalk, and miRDB, while RNA22 v2 was used for lncRNA-miRNA interactions. These interactions were integrated into ceRNA networks. Finally, qRT-PCR validated key molecules within the ceRNA network. We identified 4,997 DEGs, 315 DELs, and 89 DEMs at day 3; 5,087 DEGs, 300 DELs, and 84 DEMs at day 7; 3,073 DEGs, 235 DELs, and 68 DEMs at day 14; and 2,609 DEGs, 197 DELs, and 90 DEMs at day 28. Further analysis revealed hub osteogenic genes and their ceRNA regulatory networks at each time point. The networks consisted of 2 mRNAs, 3 miRNAs, and 9 lncRNAs at day 3; 2 mRNAs, 3 miRNAs, and 8 lncRNAs at days 7 and 14; and 1 mRNA, 3 miRNAs, and 10 lncRNAs at day 28. We validated two key lncRNAs (AABR07030366.1 and AABR07057997.1) along with their interacting miRNAs and mRNAs: rno-miR-9a-5p/Col9a1 (day 3), rno-miR-181c-5p/Comp (day 7), rno-miR-423-5p/Col1a1 (day 14), and rno-miR-185-5p/Ctsk (day 28). In summary, our study leveraged bioinformatics to construct ceRNA networks involved in osteogenesis post-fracture, offering insights into their dynamic regulatory role in healing and underlying molecular mechanisms.

摘要

骨折愈合是一个复杂的生物学过程。本研究旨在利用生物信息学方法,研究骨折后前28天内参与骨折愈合的关键分子及其潜在的竞争性内源RNA(ceRNA)调控机制。实验选取15只成年雄性SD大鼠,在骨折后第0、3、7、14和28天(n = 3)采集胫骨骨痂组织样本。采用RNA测序进行高通量转录组测序,随后进行差异表达分析,以鉴定不同阶段的差异表达基因(DEG)、长链非编码RNA(DEL)和微小RNA(DEM)。使用STRING数据库构建蛋白质-蛋白质相互作用(PPI)网络,并用Cytoscape进行可视化。进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,以探索潜在的生物学机制。使用TargetScan、miRWalk和miRDB预测miRNA与mRNA的相互作用,而使用RNA22 v2预测lncRNA与miRNA的相互作用。将这些相互作用整合到ceRNA网络中。最后,通过qRT-PCR验证ceRNA网络中的关键分子。我们在第3天鉴定出4997个DEG、315个DEL和89个DEM;在第7天鉴定出5087个DEG、300个DEL和84个DEM;在第14天鉴定出3073个DEG、235个DEL和68个DEM;在第28天鉴定出2609个DEG、197个DEL和90个DEM。进一步分析揭示了每个时间点的核心成骨基因及其ceRNA调控网络。该网络在第3天由2个mRNA、3个miRNA和9个lncRNA组成;在第7天和第14天由2个mRNA、3个miRNA和8个lncRNA组成;在第28天由1个mRNA、3个miRNA和10个lncRNA组成。我们验证了两个关键lncRNA(AABR07030366.1和AABR07057997.1)及其相互作用的miRNA和mRNA:rno-miR-9a-5p/Col9a1(第3天)、rno-miR-181c-5p/Comp(第7天)、rno-miR-423-5p/Col1a1(第14天)和rno-miR-185-5p/Ctsk(第28天)。总之,我们的研究利用生物信息学构建了参与骨折后成骨的ceRNA网络,深入了解了它们在愈合过程中的动态调控作用及其潜在的分子机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/811a/12289964/bca3aaafe356/41598_2025_12505_Fig1_HTML.jpg

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