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Although the American Joint Commission on Cancer (AJCC) staging has been widely used to predict the survival of cancer patients, there are still some limitations. The high accuracy of lncRNA-based signature prediction has attracted widespread attention. The data were obtained from the RNA sequencing data of nonsmall cell lung cancer (NSCLC) in the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) were identified. Using univariate Cox proportional hazard regression (CPHR) analysis, least absolute shrinkage and selection operator method, and multivariate CPHR, 5 lncRNAs (LINC00460, LINC00857, LINC01116, RP11-253E3.3, and RP11-359E19.2) related to patient survival were successfully screened. Combined with age, gender, AJCC staging, and 5 lncRNAs, a nomogram with a better prognosis prediction ability than traditional parameters was constructed. Prognostic accuracy was evaluated using the receiver operating characteristic (ROC) curve and area under the ROC value. In addition, through co-expression analysis, we found that 5 lncRNA target genes have 34 DEMs. Gene ontology function analysis showed that these DEMs were mainly enriched in enzyme inhibitor activity and other aspects. Finally, these DEMs were found to be involved in the formation of the tumor immune microenvironment. In short, the nomogram based on 5 lncRNAs can effectively predict the overall survival rate of NSCLC and may guide the formulation of treatment plans for NSCLC.
Pubmed ID: 35491725
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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 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 mentionsWeb server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.
View all literature mentionsWeb server for comprehensive analysis of tumor infiltrating immune cells. Web tool for systematical analysis of immune infiltrates across diverse cancer types. Allows users to input function specific parameters, with resulting figures dynamically displayed to access tumor immunological, clinical, and genomic features.
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