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A comprehensive study of alternative splicing in malignant pleural mesothelioma identifies potential therapeutic targets in a new cluster with poor survival
Author(s) -
Shao MingMing,
Qiao Xin,
Chen QingYu,
Yi FengShuang
Publication year - 2022
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.14564
Subject(s) - rna splicing , alternative splicing , medicine , transcriptome , cancer research , computational biology , splicing factor , bioinformatics , gene , gene expression , biology , genetics , messenger rna , rna
Abstract Background Malignant pleural mesothelioma (MPM) is one of the most aggressive tumors with few effective treatments worldwide. It has been suggested that alternative splicing at the transcriptome level plays an indispensable role in MPM. Methods We analyzed the splicing profile of 84 MPM patients from the TCGA cohort by using seven typical splicing types. We classified MPM patients based on their splicing status and conducted a comprehensive analysis of the correlation between the splicing classification and clinical characteristics, genetic variation, pathway changes, immune heterogeneity, and potential therapeutic targets. Results The expression of the alternative splicing regulator SRPK1 is significantly higher in MPM tissues than in normal tissues, and correlates with poor survival. SRPK1 deficiency promotes MPM cell apoptosis and inhibits cell migration in vitro. We divided the MPM patients into four clusters based on their splicing profile and identified two clusters associated with the shortest (cluster 3) and longest (cluster 4) survival time. We present the different gene signatures of each cluster that are related to survival and splicing. Comprehensive analysis of data from the GDSC and TCGA databases revealed that cluster 3 MPM patients could respond well to the small‐molecule inhibitor CHIR‐99021, a small‐molecule inhibitor of GSK‐3. Conclusion We performed unsupervised clustering of alternative splicing data from 84 MPM patients from the TCGA database and identified a cluster associated with the worst prognosis that was sensitive to a GSK‐3 inhibitor.

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