A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma - Zhu, Liping; Wang, Zhiqiang; Sun, Yilan; Giamas, Georgios; Stebbing, Justin; Yu, Zhentao; Peng, Ling (Frontiers in Oncology, 2021)
Background: Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma. Methods: Prognosis related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan-Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Altas. Splicing factor (SFs) regulatory networks were visualized. Results: A total of 19,054 survival related AS events in lung adenocarcinoma were screened. Based on AS subtypes, eight AS prognostic signatures were constructed. A nomogram with good validity in prognostic prediction was generated. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. CDKN2A was found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks. Conclusion: SF networks provide information of regulatory mechanisms. Key players of AS events related to TIME were investigated, which contribute to prognosis of lung adenocarcinoma.