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The mesenchymal (MES) subtype of glioblastoma (GBM) suggested worse prognosis and a more malignant phenotype in comparison with their proneural (PN) counterpart. The plasticity between PN and MES transcriptome signatures provided clinical intervention with an manner. Few LncRNA, however, have been discovered to take part in the shift between subtypes. Here, we used transcriptomic data and experimental evidences to demonstrate that silencing LncRNA LOXL1-AS1 was a new regulator of NF-κB signaling pathway through repressing RELB directly, resulting in increased marker genes of PN subtype and decreased those of MES.GBM cell proliferation was functionally suppressed by LOXL1-AS1's knockdown expression,. Furthermore, RELB's rescue could reverse LOXL1-AS1's effects partially in GBM malignant behaviors. LOXL1-AS1 could clinically serve as a poor prognostic indicator for GBM patients. In conclusion, our results suggest that LOXL1-AS1 contributes to aggressive biological processes that are related with MES phenotype via NF-κB signaling, which expand our perceptions into the underlying mechanisms in LOXL1-AS1-based and subtype transition adapted medicine for GBM management.
Pubmed ID: 29678575
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Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.
View all literature mentionsProject exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsSoftware package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
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