Genome-Wide Profiling of Prognostic Alternative Splicing Signature in Colorectal Cancer
| Autor: | He Wang, Zhen Zong, Chenghao Yi, Zhengming Zhu, Hui Li, Houqun Ying |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Oncology Cancer Research medicine.medical_specialty Colorectal cancer Genome wide profiling colorectal cancer Biology survival lcsh:RC254-282 alternative splicing 03 medical and health sciences Splicing factor 0302 clinical medicine Internal medicine medicine Gene Original Research Receiver operating characteristic Alternative splicing lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease 030104 developmental biology splicing factor 030220 oncology & carcinogenesis RNA splicing Biomarker (medicine) prognosis |
| Zdroj: | Frontiers in Oncology, Vol 8 (2018) Frontiers in Oncology |
| ISSN: | 2234-943X |
| DOI: | 10.3389/fonc.2018.00537 |
| Popis: | Background: This study was to explore differential RNA splicing patterns and elucidate the function of the splice variants served as prognostic biomarkers in colorectal cancer (CRC). Methods: Genome-wide profiling of prognostic alternative splicing (AS) events using RNA-seq data from The Cancer Genome Atlas (TCGA) program was conducted to evaluate the roles of seven AS patterns in 330 colorectal cancer cohort. The prognostic predictors models were assessed by integrated Cox proportional hazards regression. Based on the correlations between survival associated AS events and splicing factors, splicing networks were built. Results: A total of 2,158 survival associated AS events in CRC were identified. Interestingly, most of these top 20 survival associated AS events were adverse prognostic factors. The prognostic models were built by each type of splicing patterns, performing well for risk stratification in CRC patients. The area under curve (AUC) of receiver operating characteristic (ROC) for the combined prognostic predictors model could reach 0.963. Splicing network also suggested distinguished correlation between the expression of splicing factors and AS events in CRC patients. Conclusion: The ideal prognostic predictors model for risk stratification in CRC patients was constructed by differential splicing patterns of 13 genes. Our findings enriched knowledge about differential RNA splicing patterns and the regulation of splicing, providing generous biomarker candidates and potential targets for the treatment of CRC. |
| Databáze: | OpenAIRE |
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