Searching across hundreds of databases
BACKGROUND Aberrant expression of long non-coding RNAs (lncRNAs) is associated with prognosis of gastric cancer, some of which could be further evaluated as potential biomarkers. In this study, we attempted to identify a specific lncRNA signature to predict the prognosis of gastric cancer. MATERIAL AND METHODS The genome-wide lncRNA expression in the high-throughput RNA-sequencing data was retrieved from the Cancer Genome Atlas (TCGA). Differential expression of lncRNAs was identified using the Limma package. Survival analysis was conducted by use of univariate and multivariate Cox regression models. Functional enrichment analysis of lncRNAs was based on co-expressed mRNAs. DAVID was used to perform gene ontology and KEGG pathway analysis. RESULTS A total of 452 differentially expressed lncRNAs between gastric cancer and matched normal tissues were screened, of which 76 lncRNAs were identified to be gastric cancer-specific from a pan-cancer analysis of 12 types of human cancer. Among these 76 gastric cancer-specific lncRNAs, 5 lncRNAs (CTD-2616J11.14, RP1-90G24.10, RP11-150O12.3, RP11-1149O23.2, and MLK7-AS1) were significantly associated with the overall survival of patients with gastric cancer. A gastric cancer-specific 5-lncRNA signature was deduced to divide the patients into high- and low-risk groups with significantly different survival times (P<0.0001). Multivariate Cox regression analysis showed that this 5-lncRNA signature was an independent predictor of prognosis. Functional enrichment analysis of the 5 lncRNAs showed that they were mainly involved in DNA replication, mitotic cell cycle, programmed cell death, and RNA splicing. CONCLUSIONS Our results suggest that this tumor-specific lncRNA signature may be clinically useful in the prediction of gastric cancer prognosis.
Pubmed ID: 27727196 RIS Download
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Project 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 mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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