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Conjoint analysis of lncRNA and mRNA expression in rotator cuff tendinopathy
BACKGROUND: Rotator cuff tendinopathy (RCT) is a common musculoskeletal disorder in the shoulder, whose underlying mechanism is unknown. Long non-coding RNAs (lncRNAs) are involved in the development of various diseases, but little is known about their potential roles in RCT. METHODS: In this study,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186612/ https://www.ncbi.nlm.nih.gov/pubmed/32355779 http://dx.doi.org/10.21037/atm.2020.02.149 |
Sumario: | BACKGROUND: Rotator cuff tendinopathy (RCT) is a common musculoskeletal disorder in the shoulder, whose underlying mechanism is unknown. Long non-coding RNAs (lncRNAs) are involved in the development of various diseases, but little is known about their potential roles in RCT. METHODS: In this study, we profiled lncRNAs and mRNAs involved in RCT in comparison with the normal tendon (NT) by RNA sequencing (RNA-Seq), to identify potential therapeutic targets. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway, competing endogenous RNA (ceRNA), and co-expression network construction were used to identify the potential functions of these RNAs. Three lncRNAs and three mRNAs were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). RESULTS: In total, 419 lncRNAs and 1,541 mRNAs were differentially expressed between the RCT and NT groups with a fold change of >2 and P of <0.01. The GO and KEGG pathway analyses showed that the differentially expressed mRNAs were mainly enriched in complement activation and involved in the citrate cycle. The ceRNA network showed the interaction of differentially expressed RNAs, comprising 139 lncRNAs, 126 mRNAs, and 35 miRNAs. NONHSAT209114.1, ENST00000577806, NONHSAT168464.1, PLK2, TMEM214, and IGF2 were validated by PCR. We constructed a co-expressed network of these validated RNAs. CONCLUSIONS: We preliminarily analyzed the profile of lncRNAs and mRNAs in RCT. The bioinformatic analysis revealed several potential therapeutic targets for RCT. |
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