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Research Paper|Volume 13, Issue 4|pp 5824—5844

Development and validation of an individual alternative splicing prognostic signature in gastric cancer

Shenghan Lou1, Jian Zhang2, Zhao Zhai1, Xin Yin1, Yimin Wang1, Tianyi Fang1, Yingwei Xue1
  • 1Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
  • 2Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
* Equal contribution
Received: October 15, 2020Accepted: December 23, 2020Published: February 17, 2021

Copyright: © 2021 Lou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Gastric cancer (GC) is a heterogeneous disease with different clinical manifestations and prognoses. Alternative splicing (AS) is a determinant of gene expression and contributes to protein diversity from a rather limited gene transcript in metazoans. AS events are associated with different aspects of cancer biology, including cell proliferation, apoptosis, invasion, etc. Here, we present a comprehensive analysis of the prognostic AS profile in GC. GC-specific AS (GCAS) events were analyzed, and overall survival-associated GCAS (OS-GCAS) events were verified among the genome-wide AS events identified in The Cancer Genome Atlas (TCGA) database. In total, 1,287 GCAS events of 837 genes and 173 OS-GCAS events of 130 genes were identified. The parental genes of OS-GCAS events were significantly enriched in the development of GC. Protein-protein interaction (PPI) and OS-GCAS-associated splicing factor (SF) interaction networks were constructed. Multivariate Cox regression analysis with least absolute shrinkage and selection operator (LASSO) penalty was performed to establish a prognostic risk formula, representing 23 OS-GCAS events. The low-risk group had better OS than the high-risk group and lower immune and stromal scores. Cox proportional hazard regression was applied to generate an AS-clinical integrated prognostic model with a considerable area under the curve (AUC) value in both the training and validation datasets. Our study provides a profile of OS-GCAS events and an AS-clinical nomogram to predict the prognosis of GC.