Open AccessPrognostic risk assessment model for alternative splicing events and splicing factors in malignant pleural mesothelioma
Author(s) -
Jiang Yue,
Zhang Chengda,
Chen Yang,
Zhao Shiyu,
He Yipeng,
He Jun
Publication year - 2023
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.5174
Subject(s) - proportional hazards model , lasso (programming language) , oncology , multivariate statistics , medicine , multivariate analysis , survival analysis , machine learning , computer science , world wide web
Background Malignant pleural mesothelioma (MPM) is a rare and highly malignant thoracic tumor. Although alternative splicing (AS) is associated with tumor prognosis, the prognostic significance of AS in MPM is unknown. Methods Transcriptomic data, clinical information, and splicing percentage values for MPM were obtained from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq databases. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analyses were performed to establish a model affecting the prognosis of MPM. Survival and ROC analyses were used to test the effects of the prognostic model. LASSO/multivariate Cox analysis was used to construct the MPM prognostic splicing factor (SF) model. The SF–AS interaction network was analyzed using Spearman correlation and visualized using Cytoscape. The association between the MPM prognostic SF model and drug sensitivity to chemotherapeutic agents such as cisplatin was analyzed using pRRophetic.R. Results The LASSO/multivariate Cox analysis identified 41 AS events and 2 SFs that were mostly associated with survival. Nine prognostic prediction models (i.e., seven types of AS model, total AS model, and SF model) were developed. An MPM prognostic SF–AS regulatory network was subsequently constructed with decreased drug sensitivity in the SF model high‐risk group ( p = 0.025). Conclusion This study provides the first comprehensive analysis of the prognostic value of AS events and SFs in MPM. The SF–AS regulatory network established in this study and our drug sensitivity analysis using the SF model may provide novel targets for pharmacological studies of MPM.