Dataset Information

Systematic Profiling of Alternative mRNA Splicing Signature for Predicting Glioblastoma Prognosis.

ABSTRACT: Emerging evidence suggests that alternative splicing (AS) is modified in cancer and is associated with cancer progression. Systematic analysis of AS signature in glioblastoma (GBM) is lacking and is greatly needed. We profiled genome-wide AS events in 498 GBM patients in TCGA using RNA-seq data, and splicing network and prognostic predictor were built by integrated bioinformatics analysis. Among 45,610 AS events in 10,434 genes, we detected 1,829 AS events in 1,311 genes, and 1,667 AS events in 1,146 genes that were significantly associated with overall survival and disease-free survival of GBM patients, respectively. Five potential feature genes, S100A4, ECE2, CAST, ASPH, and LY6K, were discovered after network mining as well as correlation analysis between AS and gene expression, most of which were related to carcinogenesis and development. Multivariate survival model analysis indicated that these five feature genes could classify the prognosis at AS event and gene expression level. This report opens up a new avenue for exploration of the pathogenesis of GBM through AS, thus more precisely guiding clinical treatment and prognosis judgment.

SUBMITTER: Chen X

PROVIDER: S-EPMC6769083 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Systematic Profiling of Alternative mRNA Splicing Signature for Predicting Glioblastoma Prognosis.

Chen Xueran X Zhao Chenggang C Guo Bing B Zhao Zhiyang Z Wang Hongzhi H Fang Zhiyou Z

Frontiers in oncology 20190924

Emerging evidence suggests that alternative splicing (AS) is modified in cancer and is associated with cancer progression. Systematic analysis of AS signature in glioblastoma (GBM) is lacking and is greatly needed. We profiled genome-wide AS events in 498 GBM patients in TCGA using RNA-seq data, and splicing network and prognostic predictor were built by integrated bioinformatics analysis. Among 45,610 AS events in 10,434 genes, we detected 1,829 AS events in 1,311 genes, and 1,667 AS events in ...[more]

PMID: 31608231

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Project description:Alternative splicing (AS) is a pervasive and vital mechanism involved in the progression of cancer by expanding genomic encoding capacity and increasing protein complexity. However, the systematic analysis of AS in hepatocellular carcinoma (HCC) is lacking and urgently required. In the present study, genome?wide AS events with corresponding clinical information were profiled in 290 patients with HCC from the Cancer Genome Atlas and SpliceSeq software. Functional enrichment analyses revealed the pivotal biological process of AS regulation. Univariate Cox regression analyses were performed, followed by stepwise forward multivariate analysis to develop the prognostic signatures. Spearman's correlation analyses were also used to construct potential regulatory network between the AS events and aberrant splicing factors. A total of 34,163 AS events were detected, among which 1,805 AS events from 1,314 parent genes were significantly associated with the overall survival (OS) of patients with HCC, and their parent genes serve crucial roles in HCC?related oncogenic processes, including the p53 signaling pathway, AMPK signaling pathway and HIF?1 signaling pathway. A prognostic AS signature was established that was found to be an independent prognostic factor for OS in stratified cohorts, harboring a noteworthy ability to distinguish between the distinct prognoses of patients with HCC (high?risk vs. low?risk, 827 vs. 3,125 days, P<2e?16). Time?dependent receiver?-operator characteristic curves confirmed its robustness and clinical efficacy, with the area under the curves maintained >0.9 for short?term and long?term prognosis prediction. The splicing correlation network suggested a trend in the interactions between splicing factors and prognostic AS events, further revealing the underlying mechanism of AS in the oncogenesis of HCC. In conclusion, the present study provides a comprehensive portrait of global splicing alterations involved in the progression and HCC in addition to valuable prognostic factors for patients, which may represent as underappreciated hallmark and provide novel clues of therapeutic targets in HCC.

Project description:Purpose: Alternative splicing (AS) was reported to play a vital role in development and progression of glioblastoma (GBM), the most common and fatal brain tumor. Systematic analysis of survival-associated AS event profiles and prognostic prediction model based on multiple AS events in GBM was needed. Methods: Genome-wide AS and RNA sequencing profiles were generated in 152 patients with GBM in the cancer genome atlas (TCGA). Prognosis-associated AS events were screened by integrated Cox regression analysis to construct the prognostic risk score model in the training cohort (n = 101). The AS-based signature and clinicopathologic parameters were applied to construct a prognostic nomogram for 0.5-, 1-, and 3-year OS prediction. Finally, the regulatory networks between prognostic AS events and splicing factors (SFs) were constructed. Results: A total of 1,598 prognosis-related AS events from 1,183 source genes were determined. Eight prognostic risk score model based on integrated AS events and 7 AS types were established, respectively. Concordance index (C-index) and receiver operating characteristic (ROC) curve analysis demonstrated powerful ability in distinguishing patients' outcomes. Only Alternate Donor site (AD) and Exon Skip (ES) signature out of the eight types of AS signature were identified as independent prognostic factors for GBM, which was validated in the internal validation cohort. The nomogram with age, new event, pharmaceutical therapy, radiation therapy, AD signature and ES signature were constructed, with C-index of 0.892 (95% CI, 0.853-0.931; P = 5.13 × 10-15). Calibration plots, ROC, and decision curve analysis suggested excellent predictive performance for the nomogram in both TCGA training cohort and validation cohort. Splicing network indicated distinguished correlations between prognostic AS events and SFs in GBM patients. Conclusions: AS-based prediction model could serve as a promising prognostic predictor and potential therapeutic target for GBM, facilitating better treatment strategies in clinical practice.

Dataset Information

Systematic Profiling of Alternative mRNA Splicing Signature for Predicting Glioblastoma Prognosis.

Publications

Systematic Profiling of Alternative mRNA Splicing Signature for Predicting Glioblastoma Prognosis.

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