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Alternative splicing (AS) has a critical role in tumor progression and prognosis. Our study aimed to investigate pancreatic cancer-specific AS events using RNA-seq data, gaining systematic insights into potential prognostic predictors. We downloaded 10,623 genes with 45,313 pancreatic cancer-specific AS events from the Cancer Genome Atlas (TCGA) and SpliceSeq database. Cox univariate analyses of overall survival suggested there was a remarkable association between 6,711 AS events and overall survival in pancreatic cancer patients (P < 0.05). The area under the curves (AUC) of the receiver operator characteristic curves (ROC) of risk score was 0.89 for final prognostic predictor. Results indicated that AS events of DAZAP1, RBM4, ESRP1, QKI, and SF1 were significantly associated with overall survival. The results of FunRich showed that transcription factors KLF7, GABPA, and SP1 were the most highly related to survival-associated AS genes. Furthermore, using DriverDBv2, we identified 13 driver genes associated with survival-associated AS events, including TP53 and CDC27. Thus, we concluded that the aberrant AS patterns in pancreatic cancer patients might serve as prognostic predictors.
Pubmed ID: 31552163
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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.
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View all literature mentionsA Java application to investigate alternative mRNA splicing patterns in data from high-throughput mRNA sequencing studies. Sequence reads are mapped to splice graphs that unambiguously quantify the inclusion level of each exon and splice junction. The graphs are then traversed to predict the protein isoforms that are likely to result from the observed exon and splice junction reads. UniProt annotations are mapped to each protein isoform to identify potential functional impacts of alternative splicing. This tool may be used on a single RNASeq sample to identify genes with multiple spliceforms, on a pair of samples to identify differential splicing between the two, or on groups of samples to identify statistically significant group level differences in splicing patterns. SpliceSeq can be run from the install page as a java web start application to explore the sequencing data on their server or can be installed locally to analyze your own mRNA-Seq data.
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