A previous study described the important regulatory roles of microRNAs (miRNAs) in ischemic stroke. However, the functional significance of long non-coding RNA (lncRNAs) in ischemic stroke was largely unknown. This study aimed to identify lncRNA profiling and elucidate the regulatory mechanisms in the pathophysiology of stroke. RNA sequencing was performed on the blood of three ischemic stroke patients and three normal controls. Differential expression analysis was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). After further correlation and co-expression analysis, the corresponding co-expression networks and miRN-lncRNA-mRNA interaction network were then constructed. The expression of DElncRNAs and DEmRNAs was verified in Gene Expression Omnibus. RNA sequencing and subsequent bioinformatics analysis produced a total of 61 DElncRNAs (14 upregulated and 47 downregulated) and 673 DEmRNAs (432 upregulated and 241 downregulated). LOC105372881 and LOC101929707 were the most highly increased and decreased lncRNAs in ischemic stroke. LncRNA-mRNA co-expression networks were constructed according to 3,008 positively co-expressed and 607 negatively co-expressed lncRNA-mRNA pairs. The DElncRNAs may play roles in the pathways of glycolysis/gluconeogenesis, arrhythmogenic right ventricular cardiomyopathy, adherens junction, lysosome, and hematopoietic cell lineage by regulating their co-expressed mRNAs. Combined with previous data, a miRNA-lncRNA-mRNA interaction network for ischemic stroke was constructed. Based on GSE22255, the expression of six DElncRNAs (CEBPA-AS1, LINC00884, HCG27, MATN1-AS1, HCG26, and LINC01184) and 11 DEmRNAs (TREML4, AHSP, PI3, TESC, ANXA3, OAS1, OAS2, IFI6, ISG15, IFI44L, and LY6E) was similar to the current sequencing data. This study is the first to identify blood lncRNAs in human ischemic stroke using RNA sequencing. The findings may be the foundation for understanding the potential role of lncRNAs in ischemic stroke.

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Introduction Ischemic stroke (IS) causes severe neurological impairments and physical disabilities and has a high economic burden. Our study aims to identify the key genes and upstream regulators in IS by integrated microarray analysis. Methods An integrated analysis of microarray studies of IS was performed to identify the differentially expressed genes (DEGs) in IS compared to normal control. Based on these DEGs, we performed the functional annotation and transcriptional regulatory network constructions. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was performed to verify the expression of DEGs. Results From two Gene Expression Omnibus datasets obtained, we obtained 1526 DEGs (534 up‐regulated and 992 down‐regulated genes) between IS and normal control. The results of functional annotation showed that Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in IS. Top four transcription factors (TFs) with the most downstream genes including PAX4, POU2F1, ELK1, and NKX2‐5. The expression of six genes (ID3, ICAM2, DCTPP1, ANTXR2, DUSP1, and RGS2) was detected by qRT‐PCR. Except for DUSP1 and RGS2, the other four genes in qRT‐PCR played the same pattern with that in our integrated analysis. Conclusions The dysregulation of these six genes may involve with the process of ischemic stroke (IS). Four TFs (PAX4, POU2F1, ELK1 and NKX2‐5) were concluded to play a role in IS. Our finding provided clues for exploring mechanism and developing novel diagnostic and therapeutic strategies for IS.

The established model might be useful for predicting the risk of distant metastasis in patients with locally advanced NPC who underwent CCRT and may identify the patients' need for intensified adjuvant chemotherapy.