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Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma
Author(s) -
Zhang Shuting,
Wu Xiang,
Diao Pengfei,
Wang Chenxing,
Wang Dongmiao,
Li Sheng,
Wang Yanling,
Cheng Jie
Publication year - 2020
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.29357
Subject(s) - proportional hazards model , survival analysis , oncology , carcinogenesis , multivariate statistics , univariate , receiver operating characteristic , alternative splicing , medicine , head and neck squamous cell carcinoma , log rank test , basal cell , biology , gene , cancer , head and neck cancer , genetics , computer science , exon , machine learning
Alternative splicing (AS) is critically associated with tumorigenesis and patient's prognosis. Here, we systematically analyzed survival‐associated AS signatures in oral squamous cell carcinoma (OSCC) and evaluated their prognostic predictive values. Survival‐related AS events were identified by univariate and multivariate Cox regression analyses using OSCC data from the TCGA head neck squamous cell carcinoma data set. The Percent Spliced In calculated by SpliceSeq from 0 to 1 was used to quantify seven types of AS events. A predictive model based on AS events was constructed by least absolute shrinkage and selection operator Cox regression assay and further validated using a training‐testing cohort design. Patient survival was estimated using the Kaplan–Meier method and compared with Log‐rank test. The receiver operating characteristics curve area under the curves was used to evaluate the predictive abilities of these predictive models. Furthermore, gene–gene interaction networks and the splicing factors (SFs)‐AS regulatory network was generated by Cytoscape. A total of 825 survival‐related AS events within 719 genes were identified in OSCC samples. The integrative predictive model was better at predicting outcomes of patients as compared to those models built with the individual AS event. The predictive model based on three AS‐related genes also effectively predicted patients’ survival. Moreover, seven survival‐related SFs were detected in OSCC including RBM4, HNRNPD, and HNRNPC, which have been linked to tumorigenesis. The SF‐AS network revealed a significant correlation between survival‐related AS genes and these SFs. Our findings revealed a systemic portrait of survival‐associated AS events and the splicing network in OSCC, suggesting that AS events might serve as novel prognostic biomarkers and therapeutic targets for OSCC.