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We attempted to investigate the relationship between hsa-let-7c and ANP32E, as well as their influence on the cells phenotype of lung adenocarcinoma. Expression of hsa-let-7c and prognostic values were assessed by bioinformatics analysis based on TCGA database. Quantitative real-time PCR and western blot was employed to measure relative expression of hsa-let-7c or ANP32E. The targeting relationship between let-7c and ANP32E was predicted by biological software and validated by dual luciferase reporter assay. With gene transfection technology, cell proliferation, invasion and migration were appraised by cell counting Kit-8, clone formation and Transwell assays. The results showed that hsa-let-7c was downregulated in lung adenocarcinoma. Downregulation of hsa-let-7c notably led to a poor survival. ANP32E was forecasted and confirmed as a directly target of hsa-let-7c, and was upregulated in lung adenocarcinoma. Furthermore, upregulation of ANP32E had a significant correlation with unsatisfactory survival. Meanwhile, the levels of ANP32E were negatively regulated by hsa-let-7c. Upregulation of hsa-let-7c remarkably suppressed the Calu-3 cell proliferation, invasion and migration, while ANP32E overexpression plasmids rescued the downtrend. Inversely, hsa-let-7c silencing in NCI-H209 cells presented the opposite outcomes. Collectively, hsa-let-7c shows an anti-tumor effect in lung adenocarcinoma by targeting ANP32E and is expected to be a potential therapeutic target for lung adenocarcinoma.
Pubmed ID: 32746998
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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 mentionsSoftware tool to store the predicted and the experimentally validated microRNA (miRNA)-target interaction pairs. Predictions within the complete sequence of genes of human, mouse, and rat genomes. Integrates a comparative platform of miRNA-binding sites resulting from ten different prediction datasets.
View all literature mentionsCell line NCI-H460 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line BEAS-2B is a Transformed cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line Calu-3 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line A-549 is a Cancer cell line with a species of origin Homo sapiens (Human)
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.
