Differentially expressed LncRNAs as potential prognostic biomarkers for glioblastoma.

Glioblastoma (GBM) is the most common and aggressive brain tumor with the poor clinical outcome. LncRNAs (Long non-coding RNAs) play an important role in the occurrence and development of glioblastoma. We aimed to explore the role that lncRNAs play in regulating glioblastoma and the pathways they are enriched in. The expression data of a total of 516 GBM samples were downloaded from TCGA (The Cancer Genome Atlas). We identified the differentially expressed lncRNAs between cancer and normal tissues and performed annotation of differentially expressed lncRNAs to figure out the functions and pathways they were enriched in. Finally, cluster analysis was performed on the expression data of lncRNA and the samples were divided into four kinds, which were then used in the survival analysis. A total of 90 down-regulated lncRNAs and 224 up-regulated lncRNAs were screened out, which were mostly enriched in pathways of Alzhermer's disease and apoptosis. Their neighborhood genes were mostly enriched in genes sets of RTN1 and MAPK10.The characterization of differentially expressed lncRNAs was found out and the mostly enriched pathways were obtained to figure out the regulation mechanism of lncRNA. Our findings may provide evidence of the potential role of lncRNA in the diagnosis, prognosis and target therapy of GBM.

Shao M ，Liu W ，Wang Y 《Cancer Genetics》

Construction of lncRNA-associated ceRNA networks to identify prognostic lncRNA biomarkers for glioblastoma.

Long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) that play significant regulatory roles in the pathogenesis of tumors. However, the role of lncRNAs, especially the lncRNA-related ceRNA regulatory network, in glioblastoma (GBM) has not been fully elucidated. The goal of the current study was to construct lncRNA-microRNA-mRNA-related ceRNA networks for further investigation of their mechanism of action in GBM. We downloaded data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases and identified differential lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) associated with GBM. A ceRNA network was constructed and analyzed to examine the relationship between lncRNAs and patients' overall survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were used to analyze the related mRNAs to indirectly explain the mechanism of action of lncRNAs. The potential effective drugs for the treatment of GBM were identified using the connectivity map (CMap). After integrated analysis, we obtained a total of 210 differentially expressed lncRNAs, 90 differentially expressed miRNAs, and 2508 differentially expressed mRNAs (DEmRNAs) from the TCGA and GEO databases. Using these differential genes, we constructed a lncRNA-associated ceRNA network. Six lncRNAs in the ceRNA network were associated with the overall survival of patients with GBM. Through KEGG analysis, it was found that the DEmRNAs involved in the network are related to cancer-associated pathways, for instance, mitogen-activated protein kinase and Ras signaling pathways. CMap analysis revealed four small-molecule compounds that could be used as drugs for the treatment of GBM. In this study, a multi-database joint analysis was used to construct a lncRNA-related ceRNA network to help identify the regulatory functions of lncRNAs in the pathogenesis of GBM.

Liu Z ，Wang X ，Yang G ，Zhong C ，Zhang R ，Ye J ，Zhong Y ，Hu J ，Ozal B ，Zhao S ... -  《-》

Prospective Series of Nine Long Noncoding RNAs Associated with Survival of Patients with Glioblastoma.

 To analyze the long noncoding RNA (lncRNA) expression profile of glioblastoma multiforme (GBM) and identify prognosis-related lncRNAs, as well as their related protein-coding genes and functions.  The lncRNA expression profiles were obtained by microarray in six samples each of GBM and normal brain tissue. The lncRNAs expressed were significantly different between the two groups and used to detect their associations with patient survival time by downloading the related data from The Cancer Genome Atlas (TCGA). The total RNA-sequencing data of 152 patients diagnosed GBM level 3 with complete clinic information was downloaded. The survival time-dependent lncRNAs were identified by multivariate Cox regression analysis. For the survival time-dependent lncRNAs, we used the Pearson correlation coefficient and z test to search their associated protein-coding genes downloaded from TCGA. Functions of these genes were annotated by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.  More than 1,000 antisense lncRNAs and enhancer lncRNAs were selected for analysis in this study. Data from 152 cases with RNA-seq of GBM level 3 with complete information on GBM were downloaded from the TCGA database. Univariate Cox regression analysis revealed 19 lncRNAs with survival time dependency. These nine lncRNAs were used to construct our survival model via multivariate Cox regression analysis: TP73-AS1, AC078883.3, RP11-944L7.4, HAR1B, RP4-635E18.7, HOTAIR, SAPCD1-AS1, AC104653.1, and RP5-1172N10.2. The nine lncRNAs associated with them were inputted into the DAVID database for gene ontology and KEGG function enrichment analysis. The result showed these genes were enriched with ion binding, transport, cell-cell signaling, plasma membrane parts, and more, and they were mainly related to neuroactive ligand-receptor interaction pathway, calcium signaling pathway, and the mitogen-activated protein kinase signaling pathway.  The nine lncRNAs were a set of biomarkers for the prognosis of patients with GBM, enabling a more accurate prediction of survival and revealing more biological functions.

Lei B ，Yu L ，Jung TA ，Deng Y ，Xiang W ，Liu Y ，Qi S ... -  《-》

Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma.

Li M ，Long S ，Hu J ，Wang Z ，Geng C ，Ou S ... -  《Aging-US》

An eight-mRNA signature outperforms the lncRNA-based signature in predicting prognosis of patients with glioblastoma.

Gong Z ，Hong F ，Wang H ，Zhang X ，Chen J ... -  《BMC Medical Genetics》