肩袖腱病环状RNA表达特征与内源竞争RNA调控网络构建的研究

Circular RNA expression pattern and competing endogenous RNA network involved in rotator cuff tendinopathy

目的探究肩袖腱病中差异表达的环状RNA(circular RNA,circRNA)及其调控靶基因的预测和生物信息学分析。方法取2018年6月—2019年6月因冈上肌腱损伤行手术治疗的10例患者自愿捐赠肩袖组织,经MRI、关节镜检查以及组织学染色,确定为正常肩袖或肩袖腱病组织,再依据患者年龄、体质量以及Bonar评分进行配对分组。通过高通量测序对肩袖组织进行circRNA检测,筛选差异表达的circRNA,对circRNA靶基因进行预测,并进行富集分析和内源竞争RNA(competing endogenous RNA,ceRNA)网络构建。结果在P<0.05、log2差异表达倍数(fold change,FC)绝对值>2条件下,共筛选出94条在正常肩袖及肩袖腱病组织中差异表达的circRNA,其中肩袖腱病组织中有31条表达上调、63条表达下调。基因本体(gene ontology,GO)分析发现差异基因最显著富集于环磷酸腺苷应答;京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路富集分析发现,差异基因主要富集在细胞外基质-受体交互、蛋白消化及吸收、细胞循环、NF-κB信号通路等。ceRNA网络揭示了差异表达的circRNA和miRNA、mRNA之间可能的相互作用,其中自由能最高的ceRNA调控轴为circRNA.8951-has-miR-6089-DNMT3B。结论肩袖腱病中存在差异表达的circRNA,可能与肩袖腱病发生发展密切相关,有望为临床诊断、治疗提供潜在靶点。

Objective To detect the differentially expressed circular RNA(circRNA)in rotator cuff tendinopathy and analyze the potential molecular mechanism of these parental genes.Methods Ten supraspinatus tendons donated from patients who underwent tendon repair surgery between June 2018 and June 2019 were used for RNA-sequence.All rotator cuff tendinopathy and normal tendon samples were confirmed by MRI,histological staining,and observation by arthroscopy.All pathological tendons were matched with tendon samples for patients’age,gender,body mass index,and Bonar score.The bioinformatic analysis was performed based on the differentially expressed circRNA and their parental genes,including gene ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG),and competing endogenous RNA(ceRNA)network construction.Results There were 94 differentially expressed circRNAs,including 31 up-regulated and 63 down-regulated,detected between the rotator cuff tendinopathy and normal tendon samples with|log2 fold change(FC)|>2,P<0.05.GO analysis showed that the genes were mostly enriched in response to cyclic adenosine monophosphate(cAMP).KEGG pathway analysis showed that the most genes were enriched in extracellular matrix-receptor interaction,protein digestion and absorption,cell cycle,and nuclear factorκB signaling pathway.ceRNA networks showed the interactions among circRNAs,mRNAs,and miRNAs.And circRNA.8951-has-miR-6089-DNMT3B was the most sum max energy.Conclusion This bioinformatic study reveals several potential therapeutic targets for rotator cuff tendinopathy,which paves the way to better treatment and prevention of this disorder.