Objective Exosomal long noncoding RNAs(lnc RNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lnc RNAs in white matter hyperintensiti...Objective Exosomal long noncoding RNAs(lnc RNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lnc RNAs in white matter hyperintensities(WMH).Methods We used high-throughput sequencing to determine the differential expression(DE) profiles of lnc RNAs in plasma exosomes from WMH patients and controls. The sequencing results were verified in a validation cohort using q RT-PCR. The diagnostic potential of candidate exosomal lnc RNAs was proven by binary logistic analysis and receiver operating characteristic(ROC) curves. The diagnostic value of DE exo-lnc RNAs was determined by the area under the curve(AUC). The WMH group was then divided into subgroups according to the Fazekas scale and white matter lesion site, and the correlation of DE exo-lnc RNAs in the subgroup was evaluated.Results In our results, four DE exo-lnc RNAs were identified, and ROC curve analysis revealed that exolnc_011797 and exo-lnc_004326 exhibited diagnostic efficacy for WMH. Furthermore, WMH subgroup analysis showed exo-lnc_011797 expression was significantly increased in Fazekas 3 patients and was significantly elevated in patients with paraventricular matter hyperintensities.Conclusion Plasma exosomal lnc RNAs have potential diagnostic value in WMH. Moreover, exolnc_011797 is considered to be a predictor of the severity and location of WMH.展开更多
AIM:To investigate the role of tumor microenvironment(TME)-related long non-coding RNA(lncRNA)in uveal melanoma(UM),probable prognostic signature and potential small molecule drugs using bioinformatics analysis.METHOD...AIM:To investigate the role of tumor microenvironment(TME)-related long non-coding RNA(lncRNA)in uveal melanoma(UM),probable prognostic signature and potential small molecule drugs using bioinformatics analysis.METHODS:UM expression profile data were downloaded from the Cancer Genome Atlas(TCGA)and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration.The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis(ss GSEA)method,and the immune cell infiltration of a single specimen was evaluated.Finally,the specimens were divided into high and low infiltration groups.The differential expression between the two groups was analyzed using the R package‘edge R’.Univariate,multivariate and Least Absolute Shrinkage and Selection Operator(LASSO)Cox regression analyses were performed to explore the prognostic value of TMErelated lncRNAs.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional analyses were also performed.The Connectivity Map(CMap)data set was used to screen molecular drugs that may treat UM.RESULTS:A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups.Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis.Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements.Among 269 differentially expressed lncRNAs,69 were up-regulated and 200 were down-regulated.Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age,TNM stage,tumor base diameter,and low and high risk indices had significant prognostic value.We screened the potential small-molecule drugs for UM,including W-13,AH-6809 and Imatinib.CONCLUSION:The prognostic markers identified in this study are reliable biomarkers of UM.This study expands our current understanding of the role of TME-related lncRNAs in UM genesis,which may lay the foundations for future treatment of this disease.展开更多
文摘Objective Exosomal long noncoding RNAs(lnc RNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lnc RNAs in white matter hyperintensities(WMH).Methods We used high-throughput sequencing to determine the differential expression(DE) profiles of lnc RNAs in plasma exosomes from WMH patients and controls. The sequencing results were verified in a validation cohort using q RT-PCR. The diagnostic potential of candidate exosomal lnc RNAs was proven by binary logistic analysis and receiver operating characteristic(ROC) curves. The diagnostic value of DE exo-lnc RNAs was determined by the area under the curve(AUC). The WMH group was then divided into subgroups according to the Fazekas scale and white matter lesion site, and the correlation of DE exo-lnc RNAs in the subgroup was evaluated.Results In our results, four DE exo-lnc RNAs were identified, and ROC curve analysis revealed that exolnc_011797 and exo-lnc_004326 exhibited diagnostic efficacy for WMH. Furthermore, WMH subgroup analysis showed exo-lnc_011797 expression was significantly increased in Fazekas 3 patients and was significantly elevated in patients with paraventricular matter hyperintensities.Conclusion Plasma exosomal lnc RNAs have potential diagnostic value in WMH. Moreover, exolnc_011797 is considered to be a predictor of the severity and location of WMH.
基金Supported by Shanghai Key Laboratory of Fundus Diseases,2017(No.01030)Luzhou Southwest Medical University,Municipal Department Level(No.2017LZXNYD-J01)。
文摘AIM:To investigate the role of tumor microenvironment(TME)-related long non-coding RNA(lncRNA)in uveal melanoma(UM),probable prognostic signature and potential small molecule drugs using bioinformatics analysis.METHODS:UM expression profile data were downloaded from the Cancer Genome Atlas(TCGA)and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration.The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis(ss GSEA)method,and the immune cell infiltration of a single specimen was evaluated.Finally,the specimens were divided into high and low infiltration groups.The differential expression between the two groups was analyzed using the R package‘edge R’.Univariate,multivariate and Least Absolute Shrinkage and Selection Operator(LASSO)Cox regression analyses were performed to explore the prognostic value of TMErelated lncRNAs.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional analyses were also performed.The Connectivity Map(CMap)data set was used to screen molecular drugs that may treat UM.RESULTS:A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups.Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis.Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements.Among 269 differentially expressed lncRNAs,69 were up-regulated and 200 were down-regulated.Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age,TNM stage,tumor base diameter,and low and high risk indices had significant prognostic value.We screened the potential small-molecule drugs for UM,including W-13,AH-6809 and Imatinib.CONCLUSION:The prognostic markers identified in this study are reliable biomarkers of UM.This study expands our current understanding of the role of TME-related lncRNAs in UM genesis,which may lay the foundations for future treatment of this disease.
