4.6 Article

Systematic profiling of alternative splicing events and splicing factors in left- and right-sided colon cancer

Journal

AGING-US
Volume 11, Issue 19, Pages 8270-8293

Publisher

IMPACT JOURNALS LLC
DOI: 10.18632/aging.102319

Keywords

FIP1L1; prognostic signature; splicing factor; SATB2; TCGA

Funding

  1. Self-Financing Research Project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region [Z2015607]
  2. Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project [S2017098]
  3. Guangxi Science and Technology Department Project [Guike AB16380202]
  4. National Natural Science Foundation of China [81560454, 81973533]
  5. Guangxi Science and Technology Project [AB18126033]
  6. Scientific and Technological Development Plan of Qingxiu District, Nanning City [2017029]

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Left- and right-sided colon cancer (LC and RC) differ substantially in their molecular characteristics and prognoses, and are thus treated using different strategies. We systematically analyzed alternative splicing (AS) events and splicing factors in LC and RC. RNA-seq data were used for genome-wide profiling of AS events that could distinguish LC from RC. The Exon Skip splicing pattern was more common in RC, while the Retained Intron pattern was more common in LC. The AS events that were upregulated in RC were enriched for genes in the axon guidance pathway, while those that were upregulated in LC were enriched for genes in immune-related pathways. Prognostic models based on differentially expressed AS events were built, and a prognostic signature based on these AS events performed well for risk stratification in colon cancer patients. A correlation network of differentially expressed AS events and differentially expressed splicing factors was constructed, and RBM25 was identified as the hub gene in the network. In conclusion, large differences in AS events may contribute to the phenotypic differences between LC and RC. The differentially expressed AS events reported herein could be used as biomarkers and treatment targets for colon cancer.

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