@ARTICLE{10.3389/fonc.2020.00087, AUTHOR={Li, Zhexuan and Chen, Xun and Wei, Ming and Liu, Guancheng and Tian, Yongquan and Zhang, Xin and Zhu, Gangcai and Chen, Changhan and Liu, Jiangyi and Wang, Tiansheng and Lin, Gongbiao and Wang, Juncheng and Cai, Gengming and Lv, Yunxia}, TITLE={Systemic Analysis of RNA Alternative Splicing Signals Related to the Prognosis for Head and Neck Squamous Cell Carcinoma}, JOURNAL={Frontiers in Oncology}, VOLUME={10}, YEAR={2020}, URL={https://www.frontiersin.org/articles/10.3389/fonc.2020.00087}, DOI={10.3389/fonc.2020.00087}, ISSN={2234-943X}, ABSTRACT={Alternative splicing (AS) is an important mechanism that is responsible for the production of protein diversity. An increasing body of evidence has suggested that out-of-control AS is closely related to the genesis and development of cancer. Systematic analysis of genome-wide AS in head and neck squamous cell carcinoma (HNSCC) has not yet been carried out, and consideration of this topic remains at the preliminary stage and requires further investigation. In this study, systemic bioinformatic analysis was carried out on the genome-wide AS events of 555 clinical HNSCC samples from the TCGA database. Firstly, we statistically analyzed the distributions of seven AS event types in HNSCC samples. Then, through univariate survival analysis, we observed the relationship between AS and the prognosis of the disease and found that 437 intersections of AS events were significantly related to overall survival. Among them, 335 cross-genes showed a high degree of consistency in the genes associated with overall survival and recurrence. The overall survival was significantly related to AS events. Besides, the frequency of overall survival-related ES events was evidently reduced, while the AP and the AT events were increased. In addition, AT events accounted for the largest proportion. Further, multiple regression model analysis proved that AS could become a new classification method for HNSCC, and KEGG enrichment analysis proved that most genes and proteins interacting with AS events had different biological functions and were associated with a variety of diseases. Finally, through the selection of characteristic HNSCC genes and the construction of a prognostic model, seven cross-genes related to survival and recurrence were screened out, and these characteristic genes were verified by multivariate survival model analysis so as to classify the prognosis at different splicing times and gene expression levels. These results have laid a solid foundation for our further research and play a decisive role in showing the correlation of AS with the prognosis of HNSCC.} }