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The present study aimed to establish a microRNA (miRNA/miR) signature to predict the prognosis of patients with pancreatic cancer (PC) at the early stage and to investigate the involvement of competing endogenous RNAs (ceRNAs) in PC. Using mature miRNA expression profiles from The Cancer Genome Atlas, differentially expressed miRNAs in tissues derived from patients exhibiting early PC and tissues from healthy individuals were compared. The least absolute shrinkage and selection operator regression method was used to construct a miRNA-based signature for predicting prognosis. The miRNet tool, gene set enrichment analysis (GSEA) and the LncRNADisease database were utilized to explore the mechanistic involvement of ceRNAs. A total of seven downregulated miRNAs in PC (miR-424-5p, miR-139-5p, miR-5586-5p, miR-126-3p, miR-3613-5p, miR-454-3p and miR-1271-5p) were selected to generate a signature. Based on this seven-miRNA signature, it was possible to stratify patients with PC into low- and high-risk groups. The overall survival of the low-risk group was significantly longer than that of the high-risk group (P<0.001). The seven-miRNA signature was able to predict the 2-year-survival rate of patients with early PC with an area under the curve of 0.750. Furthermore, as opposed to routine clinicopathological features, this seven-miRNA signature was an independent prognostic factor according to multivariate Cox regression analysis. GSEA indicated that the extracellular matrix receptor interaction pathway and the transforming growth factor-β signaling pathway were enriched in the high-risk group. A ceRNA network of the seven-miR signature was constructed. In conclusion, the present study provided a seven-miRNA signature, according to which patients with early PC may be divided into high- and low-risk groups. The ceRNA network of the prognostic signature was preliminarily explored.
Pubmed ID: 31410115
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
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 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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