Dataset Information

Characterization of alternative spliceoforms and the RNA splicing machinery in pancreatic cancer.

ABSTRACT: Alternative splicing provides proteomic diversity that can have profound effects. The extent, pattern, and roles of alternative splicing in pancreatic cancer have not been systematically investigated. We have utilized a spliceoform-specific microarray and polymerase chain reaction to evaluate all known splice variants in human pancreatic cancer cell lines representing a spectrum of differentiation, from near-normal HPDE6 to Capan-1 and poorly differentiated MiaPaCa2 cells. Validation of altered spliceoforms was verified in primary cancer specimens and normal pancreatic ductal cells. In addition, expression of 92 spliceosomal genes was examined to better understand the mechanism for observed differences in mRNA splicing.A statistically significant reduction in alternative splicing was found in the pancreatic cancer cell lines compared with HPDE6 cells. Many splice variants identified in Capan-1 and MiaPaCa2 cells were observed in grades 3 and 4 tumors. Analysis of genes encoding spliceosomal proteins revealed that 28 of 92 genes had significantly decreased expression in cancer compared with normal pancreas.Pancreatic cancer has reduced alternative splicing diversity compared with normal pancreas. This is demonstrated in both cell lines and primary tumors, with the loss in splicing diversity correlated with relative reduction in expression of spliceosomal genes.

SUBMITTER: Carrigan PE

PROVIDER: S-EPMC3038180 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Characterization of alternative spliceoforms and the RNA splicing machinery in pancreatic cancer.

Carrigan Patricia E PE Bingham Jonathan L JL Srinvasan Subha S Brentnall Teresa A TA Miller Laurence J LJ

Pancreas 20110301 2

<h4>Objectives and methods</h4>Alternative splicing provides proteomic diversity that can have profound effects. The extent, pattern, and roles of alternative splicing in pancreatic cancer have not been systematically investigated. We have utilized a spliceoform-specific microarray and polymerase chain reaction to evaluate all known splice variants in human pancreatic cancer cell lines representing a spectrum of differentiation, from near-normal HPDE6 to Capan-1 and poorly differentiated MiaPaCa ...[more]

PMID: 21178653

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Project description:Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

Dataset Information

Characterization of alternative spliceoforms and the RNA splicing machinery in pancreatic cancer.

Publications

Characterization of alternative spliceoforms and the RNA splicing machinery in pancreatic cancer.

Similar Datasets

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