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The integrin α (ITGA) subfamily genes play a fundamental role in various cancers. However, the potential mechanism and application values of ITGA genes in colon adenocarcinoma (COAD) remain elusive. The present study investigated the significance of the expression of ITGA genes in COAD from the perspective of diagnosis and prognosis. A COAD RNA‑sequencing dataset was obtained from The Cancer Genome Atlas. The present study investigated the biological function of the ITGA subfamily genes through bioinformatics analysis. Reverse transcription‑quantitative polymerase chain reaction was applied to investigate the distribution of integrin α8 (ITGA8) expression in COAD tumors and adjacent normal tissues. Bioinformatics analysis indicated that ITGA genes were noticeably enriched in cell adhesion and the integrin‑mediated signaling pathway, and co‑expressed with each other. It was also revealed through observation that the majority of gene expression was significantly low in tumor tissues (P<0.05), and diagnostic receiver operating characteristic curves revealed that most of the genes could serve as significant diagnostic markers in COAD (P<0.05), especially ITGA8 which had a high diagnostic value with an area under curve (AUC) of 0.989 [95% confidence interval (CI) 0.980‑0.997] in COAD (P<0.0001). In addition, ITGA8 expression was verified in clinical samples and it was revealed that it was higher in adjacent normal tissues (P=0.041) compared to COAD tissues, and the AUC was 0.704 (95% CI, 0.577‑0.831; P<0.0085). Multivariate survival analysis indicated that integrin α (ITGA5) may be an independent prognostic indicator for COAD overall survival. Gene set enrichment analysis indicated that ITGA5 may participate in multiple biological processes and pathways. The present study revealed that ITGA genes were associated with the diagnosis and prognosis of COAD. The mRNA expression of ITGA8 may be a potential diagnosis biomarker and ITGA5 may serve as an independent prognosis indicator for COAD.
Pubmed ID: 31322253
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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 mentionsStatistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
View all literature mentionsSoftware package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
View all literature mentionsData analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
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 mentionsA unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.
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).
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