Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
To explore the prognostic related factors and mechanisms of gastric cancer (GC), we performed the systematic analysis with integrated bioinformatics tools based on multiple on-line datasets. With uni-variate COX analysis, we screened out 37 survival hazardous genes in GC. Further GO assays disclosed that the signatures related with extracellular matrix and structure, and the functions of "cell adhesion molecule binding" and "integrin binding" were the vital mechanisms of disease progression, and tissue inhibitor of metalloproteinase-2 (TIMP2) was the potential biomarker for prognosis. Based on GSEA, GSVA and GCN, TIMP2 was demonstrated to interact with multiple integrin pathways and involve in the regulation of EMT, cell adhesion, and angiogenesis of GC. The associations of TIMP2 expression with reduced OS and RFS of patients were declared by Kaplan-Meier analysis, and further confirmed by 1000 internal bootstrap replications and external KM plotter analysis. With multi-variate COX regression and time-dependent ROC analysis, we validated the prediction independency and capacity of TIMP2 for prognosis. The relationships of TIMP2 with clinicopathological characteristics were also uncovered. Taken together, our findings identify TIMP2 as the novel candidate biomarker for poorer outcome of GC patients, and revealed the underlying functions of TIMP2 and the potential mechanisms for GC progression.
Pubmed ID: 29941993
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsWelcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
