Abstract
Aberrant alternative splicing and abnormal alternative splicing events (ASEs) in glioblastoma multiforme (GBM) remain largely elusive. The prognostic-associated ASEs in GBM were identified and summarized into 123 genes using GBM and LGG datasets from ASCancer Atlas and TCGA. The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. ASRS could be an independent prognostic factor for GBM patients (HR > 1.8 across three datasets) through multivariate Cox regression analysis. The high-risk group demonstrated poorer prognosis, elevated immune scores, increased levels of immune cell infiltration, and greater differences in drug sensitivity. We found that FN1, used for model construction, contained 4 abnormal ASEs resulting in high expression of non-canonical transcripts and the presence of premature termination codon. These abnormal ASEs may be regulated by tumour-related splicing factors according to the PPI network. Furthermore, both mRNA and protein levels of FN1 were highly expressed in GBM compared to LGG, correlating with poor prognosis in GBM. In conclusion, our findings highlight the role of ASEs in affecting the progression of GBM, and the model showed a potential application for prognostic risk of patients. FN1 may serve as a promising splicing biomarker for GBM, and mechanisms of processes of aberrant splicing need to be revealed in the future.