Identification and validation of key long non-coding RNAs in resveratrol protect against IL-1β-treated chondrocytes via integrated bioinformatic analysis

Background Long non-coding RNAs (lncRNAs) participate in regulation of gene transcription, but little is known about the correlation among resveratrol and lncRNAs. This study aimed to identify and validate the key lncRNAs in resveratrol protect against IL-1β-treated chondrocytes. Methods In this experiment, high-throughput sequencing technique was performed to identify the differentially expressed lncRNAs, miRNAs, and mRNAs between IL-1β-treated chondrocytes with or not resveratrol. Moreover, gene ontology and KEGG pathway of the differentially expressed genes were carried out by R software. Then, lncRNA-miRNA-mRNA network was constructed by Cytoscape software. Venn diagram was performed to identify the potentially target miRNAs of LINC00654. Then, real-time polymerase chain reaction (RT-PCR) was performed to validate the most significantly differentially expressed lncRNAs. Results Totally, 1016 differentially expressed lncRNAs were identified (493 downregulated) between control and resveratrol-treated chondrocytes. Totally, 75 differentially expressed miRNAs were identified (downregulated = 54, upregulated = 21). Totally, 3308 differentially expressed miRNAs were identified (downregulated = 1715, upregulated = 1593). GO (up) were as follows: skin development, response to organophosphorus. GO (down) mainly included visual perception, single fertilization, and sensory perception of smell. KEGG (up) were as follows: TNF signaling pathway and TGF-beta signaling pathway. KEGG (down) were as follows: viral protein interaction with cytokine and cytokine receptor. We identified that LINC00654 and OGFRL1 were upregulated in resveratrol-treated chondrocytes. However, miR-210-5p was downregulated in resveratrol-treated chondrocytes. Conclusion In sum, the present study for the first time detected the differential expressed lncRNAs involved in resveratrol-treated chondrocytes via employing bioinformatic methods.