Construction of m6A-Related lncRNA Prognostic Signature Model and Immunomodulatory Effect in Glioblastoma Multiforme - Xie, Pan; Yan, Han; Gao, Ying; Li, Xi; Zhou, Dong-Bo; Liu, Zhao-Qian (Frontiers in Oncology, 2022)
Background Glioblastoma multiforme (GBM), the most prevalent and aggressive of primary malignant central nervous system tumors (grade IV), has a poor clinical prognosis. This study aimed to assess and predict the survival of GBM patients by establishing an m6A-related lncRNA signaling model and to validate its validity, accuracy and applicability. Methods RNA sequencing data and clinical data of GBM patients were obtained from TCGA data. First, m6A-associated lncRNAs were screened and lncRNAs associated with overall survival in GBM patients were obtained. Subsequently, the signal model was established using LASSO regression analysis, and its accuracy and validity are further verified. Finally, GO enrichment analysis was performed, and the influence of this signature on the immune regulation response and anticancer drug sensitivity of GBM patients was discussed. Results The signature constructed by four lncRNAs AC005229.3, SOX21-AS1, AL133523.1, and AC004847.1 is obtained. Furthermore, the signature proved to be effective and accurate in predicting and assessing the survival of GBM patients and could function independently of other clinical characteristics (Age, Gender and IDH1 mutation). Finally, Immunosuppression-related factors, including APC co-inhibition, T-cell co-inhibition, CCR and Check-point, were found to be significantly up-regulated in GBM patients in the high-risk group. Some chemotherapeutic drugs (Doxorubicin and Methotrexate) and targeted drugs (AZD8055, BI.2536, GW843682X and Vorinostat) were shown to have higher IC50 values in patients in the high-risk group. Conclusion We constructed an m6A-associated lncRNA risk model to predict the prognosis of GBM patients and provide new ideas for the treatment of GBM. Further biological experiments can be conducted on this basis to validate the clinical value of the model.