Bingxi Lei1,2, Lei Yu1, Thapa Ashish Jung2, Yuefei Deng2, Wei Xiang1, Yawei Liu1, Songtao Qi1. 1. Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China. 2. Department of Neurosurgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Abstract
OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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. Georg Thieme Verlag KG Stuttgart · New York.
OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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. Georg Thieme Verlag KG Stuttgart · New York.