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Objective:To explore the expression of long non-coding RNA (lncRNA) HAGLR in breast cancer and its effect on the prognosis of breast cancer, and to construct a competitive endogenous RNA (ceRNA) network.Methods:The Atlas of Genetics and Cytogenetics in Oncology and Haematology website was used to search for HAGLR chromosome gene mapping and transcript expression. The lnclocater website was used to predict the subcellular localization of HAGLR, and the differential expression of HAGLR in breast cancer tissues and adjacent tissues was analyzed by using lnCAR database. The patients in lnCAR database were divided into HAGLR high expression group and HAGLR low expression according to HAGLR expression. The Kaplan-Meier method was used to analyze the overall survival (OS) and metastasis-free survival, which was verified by using UCSC Xena database. lnCAR database was used to search the co-expressed genes of HAGLR. The top 200 co-expressed genes were submitted to the Metascape website for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis, and protein interaction network (PPI) was constructed. Starbase, a bioinformatics online analysis website, was used to predict HAGLR targeting mircoRNA (miRNA) and mRNA that directly encoded proteins. ceRNA network of HAGLR was constructed with Cytoscape3.8 software.Results:HAGLR gene was localized in 2q31.1 and mainly distributed in cytoplasm. The expression level of HAGLR in breast cancer tissues was higher than that in adjacent tissues, and the difference was statistically significant ( P < 0.001). lnCAR database and UCSC Xena database analysis showed that OS in HAGLR high expression group was worse than that in HAGLR low expression group (all P < 0.01). lnCAR database, the metastasis-free survival in HAGLR high expression group was worse than that in HAGLR low expression group ( P = 0.030). Among the top 200 HAGLR co-expressed genes, 129 genes were negatively correlated with HAGLR and 71 genes were positively correlated with HAGLR. KEGG pathway analysis showed that HAGLR was related to metabolic pathways, MAPK signaling pathway, JAK-STAT signaling pathway and cancer pathway. GO annotation analysis showed that HAGLR was mainly enriched in cell cycle, centromeric complex assembly, mitotic progression, protein kinase binding, kinase activity regulation, cell response to DNA damage stimulation and other functions. hsa-miR-130b-3p, hsa-miR-1245b-5p, hsa-miR-182b-5p, hsa-miR-512-3p, hsa-miR-302b-3p, hsa-miR-185b-5p, hsa-miR-106b-5p were HAGLR targeting miRNA. Conclusions:HAGLR is highly expressed in breast cancer tissues, and it may be a biomarker for predicting the prognosis of breast cancer.
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@#Objective To explore the correlations between serum hypoxia inducible factor-1α （HIF-1α），aquaporin-9 （AQP9） and hemorrhagic transformation （HT） in patients with acute ischemic stroke （AIS） and its clinical significance. Methods 183 newly diagnosed patients with acute AIS from October 2017 to April 2019 were divided into HT group （n=84） and non HT group （n=99），the baseline data of gender，age，body mass index （BMI），proportions of hypertension，diabetes，hyperlipidemia，the baseline NIHSS score，baseline systolic blood pressure，baseline diastolic blood pressure，baseline blood glucose，thrombolytic time window and serum samples were collected. The expressions of HIF-1α and AQP9 proteins were detected by Western blot （WB）；Pearson’s method was used to analyze the correlation between HIF-1α and AQP9 levels in patients with AIS in HT group；the risk factors of HT in AIS patients were analyzed by logistic regression；Using NIHSS score，thrombolysis time window，serum HIF-1α，AQP9 levels as independent variables，the working characteristic curve （ROC） of subjects was drawn to analyze the predictive value of HT in AIS patients. Results Compared with the non HT group，the baseline NIHSS score，baseline systolic blood pressure，baseline diastolic blood pressure，baseline blood glucose，thrombolytic time window，serum HIF-1α，AQP9 levels of AIS patients in HT group were significantly higher （P<0.05）. There was a positive correlation between the levels of HIF-1α and AQP9 proteins in patients with AIS in HT group （r=0.679，P<0.05）. NIHSS score，thrombolysis time window and serum HIF-1α，AQP9 protein levels were independent risk factors for HT in AIS patients after thrombolysis （P<0.05）. The AUCs of NIHSS score，thrombolysis time window，serum HIF-1α and AQP9 in the diagnosis of HT in AIS patients were 0.707，0.790，0.881 and 0.869 respectively，the cutoff values were 13.39 points，296.31 min，0.33 and 0.32 respectively，the sensitivities were 44%，66.70%，86.90% and 83.30% respectively，and the specificities were 91.90%，87.90%，87.90% and 88.90% respectively. The AUC of the combination of four methods in the diagnosis of HT in AIS patients was 0.980，the sensitivity and specificity were 94.00% and 94.90%，respectively，and the diagnostic efficiency of the four methods was significantly higher than that of single index. Conclusion The elevated levels of HIF-1α and AQP9 were closely related to the occurrence of HT in AIS patients，and the combination of NIHSS score and thrombolysis time window could significantly improve the predictive value of HT in AIS，which may have some clinical reference significance.
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Inflammation response is an important pathological process of neuronal cell death after stroke.Interleukin (IL)-33/ST2 system is involved in the inflammatory response process of ischemic stroke and has been proved to be a protective factor.This article reviews the role and mechanism of IL-33/ST2 system in the regulation of immune inflammation after ischemic stroke.