A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

Abstract

BackgroundAlternative 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.MethodsWe analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. 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 Atlas. Splicing factor (SF) regulatory networks were visualized.ResultsA total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.ConclusionTaken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

“…Many prognostic models for LUAD based on gene features have been developed, such as a tumor microenvironment-associated prognostic signature introduced by Zhao et al (47), a sevenimmune-related-gene signature constructed by Zhan et al (59), and an alternative splicing-related prognostic model proposed by Zhu et al (60). Compared with these models, our study reported a prognostic model based on IARGI, which showed good prognostic accuracy for LUAD.…”

“…( 47 ), a seven-immune-related-gene signature constructed by Zhan et al ( 59 ), and an alternative splicing-related prognostic model proposed by Zhu et al. ( 60 ). Compared with these models, our study reported a prognostic model based on IARGI, which showed good prognostic accuracy for LUAD.…”

Genome-wide investigation of differentially expressed alternative splicing and RNA-binding protein genes association with AML drug resistance

“…RAS signalling is an important mechanism for regulating the diversities of gene expression and the proteome amongst eukaryotes [39]. However, abnormal expression of RAS in cells may lead to the development of various diseases, such as atherosclerosis as well as lung and pancreatic adenocarcinoma [40,41]. Research has also shown that aberrant RAS is one of the leading factors involved in the regulation of AML drug resistance, and it is usually closely associated with poor prognosis and drug resistance in AML [42].…”