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A comprehensive characterization of alternative splicing events related to prognosis and the tumor microenvironment in lung adenocarcinoma

BACKGROUND: Alternative splicing (AS) is a critical mechanism of post-transcriptional regulation and has been widely reported to be associated with the tumor progression and tumor microenvironment (TME) formation. However, the role of AS in lung adenocarcinoma (LUAD) has not been clearly elucidated....

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Detalles Bibliográficos
Autores principales: Ma, Shouzheng, Zhu, Jun, Wang, Mengmeng, Han, Tenghui, Zhu, Jianfei, Jiang, Runmin, Jiang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096378/
https://www.ncbi.nlm.nih.gov/pubmed/35571443
http://dx.doi.org/10.21037/atm-22-1531
Descripción
Sumario:BACKGROUND: Alternative splicing (AS) is a critical mechanism of post-transcriptional regulation and has been widely reported to be associated with the tumor progression and tumor microenvironment (TME) formation. However, the role of AS in lung adenocarcinoma (LUAD) has not been clearly elucidated. This study presents a comprehensive analysis exploring the impact of AS on prognosis and TME in LUAD. METHODS: The gene expression transcriptome profiles and survival data were obtained from The Cancer Genome Atlas (TCGA) database, and the splicing profiles were obtained from the TCGA SpliceSeq database. Base on prognostic AS events, a prognostic signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate Cox regression analysis. Survival outcomes was analyzed using the Kaplan-Meier method and the predictive performance of the signature was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the landscape of the TME was assessed by ESTIMATE, Microenvironment Cell Population (MCP)-counter, and single-sample Gene-Set Enrichment Analysis (ssGSEA) algorithms. RESULTS: A total of 127 prognostic AS events with P value <0.001 from 89 genes in LUAD were confirmed. A prognostic signature was constructed based on 20 prognostic AS events. Kaplan-Meier survival analysis demonstrated that higher risk scores were associated with poorer overall survival (OS). The area under the ROC curve of risk scores predicting the 1-, 3-, and 5-year survival probability were 0.791, 0.847, and 0.832, respectively. Furthermore, significant relationship was observed between the prognostic signature and the landscape of the TME. High-risk patients had lower stromal/immune scores, higher tumor purity, and significantly decreased abundance of majority immune cells, and immune-related signatures (P<0.05). Finally, a potential regulatory mechanism of the AS events is displayed in a regulatory network. CONCLUSIONS: This research highlights the prognostic value of AS events for patients with LUAD and provide new insight into the regulation of the TME by AS. Notably, AS may affect the patient’s prognosis by altering the TME. Our findings provide important guidance for the development of novel biomarkers and therapeutic targets in patients with LUAD.