Prognostic alternative splicing signatures and underlying regulatory network in esophageal carcinoma

Zu-Cheng Xie, Hua-Yu Wu, Fu-Chao Ma, Yi-Wu Dang, Zhi-Gang Peng, Hua-Fu Zhou, Gang Chen
2019 American journal of translational research  
Alternative splicing (AS) has been widely reported to play an important role in cancers, including esophageal carcinoma (ESCA). However, no study has comprehensively investigated the clinical use of combination of prognostic AS events and clinicopathological parameters. Therefore, we collected 165 ESCA patients including 83 esophageal adenocarcinoma (EAC) and 82 esophageal squamous cell carcinoma (ESCC) patients from The Cancer Genome Atlas to explore the survival rate associated with seven
more » ... s of AS events. Prognostic predictors for the clinical outcomes of ESCA patients were built. Predictive prognosis models of the alternative acceptor site in ESCA (area under the curve [AUC] = 0.83), alternative donor site in EAC (AUC = 0.99), and alternative terminator site in ESCC (AUC = 0.974) showed the best predictive efficacy. A novel combined prognostic model of AS events and clinicopathological parameters in ESCA was also constructed. Combined prognostic models of ESCA all showed better predictive efficacy than independent AS models or clinicopathological parameters model. Through constructing splicing regulatory network, the expression of AS factor was found to be negatively correlated with the most favorable AS events. Moreover, gene amplification, mutation, and copy number variation of AS genes were commonly observed, which may indicate the molecular mechanism of how the AS events influence survival. Conclusively, the constructed prognostic models based on AS events, especially the combined prognostic models of AS signatures and clinicopathological parameters could be used to predict the outcome of ESCA patients. Moreover, the splicing regulatory network and genomic alteration in ESCA could be used for illuminating the potential molecular mechanism.
pmid:31396315 pmcid:PMC6684923 fatcat:ntikkb6tyrcu7hi7dptrpws5ca