Abstract
Background
Low-grade glioma (LGG) is a crucial pathological type of glioma. The present study aimed to explore multiple RNA methylation regulator-related AS events and investigate their prognostic values in LGG.
Methods
The prognostic model for low-grade glioma was established using the LASSO regression analysis. To validate prognostic value, we performed Kaplan–Maier survival analysis, ROC curves and nomograms. The ESTIMATE algorithm, the CIBERSORT algorithm and the ssGSEA algorithm were utilized to explore the role of the immune microenvironment in LGG. Subsequently, we then used GO, KEGG and GSEA enrichment analysis to explore the functional roles of these genes. In addition, we employed the GDSC database to screen potential chemotherapeutic agents.
Results
Eight RNA methylation related AS events were involved in construct a survival and prognosis model, which had good ability of independent prediction for patients with LGG. Patients in the high-risk group had shorter life expectancy and higher mortality, while patients in the low-risk group had a better prognosis. We constructed a nomogram which showed an excellent predictive performance for individual OS. The risk score exhibited a close correlation with some immune cells and expression of immune checkpoints. Patients in high-risk group were characterized by immunosuppressive microenvironment and poor response to immunotherapy, and were sensitive to more chemotherapeutic drugs. Pathway and functional enrichment analyses further confirmed that significant differences existed in immune landscape between the two subgroups.
Conclusion
The prognostic RNA methylation-related alternative splicing signature constructed could constitute a promising prognostic biomarker, which could serve to optimize treatment regimens.
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Data availability
The datasets presented in this study are available in the TCGA (https://portal.gdc.cancer.gov) and the TCGASpliceSeq (https://bioinformatics.mdanderson.org/TCGASpliceSeq/PSIdownload) repository.
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Acknowledgements
The authors thank the TCGA database, GTEx database and TCGASpliceSeq database for providing the valuable gene expression matrix, the alternative splicing data and clinical information.
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CM conceived and designed this work. CM conducted bioinformatic analyses. BX, YB and JX helped with data collection, analysis, and interpretation. CM, YB, JX and HL wrote the manuscript. HL and BX revised the manuscript. All the authors read and approved the final manuscript.
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Ma, C., Bao, Y., Xu, J. et al. Identification and validation of RNA methylation-related alternative splicing gene signature for low-grade glioma to predict survival and immune landscapes. J Cancer Res Clin Oncol 149, 47–62 (2023). https://doi.org/10.1007/s00432-022-04431-1
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DOI: https://doi.org/10.1007/s00432-022-04431-1