Development of a Nomogram With Alternative Splicing Signatures for Predicting the Prognosis of Glioblastoma: A Study Based on Large-Scale Sequencing Data

Abstract

Purpose: Alternative splicing (AS) was reported to play a vital role in development and progression of glioblastoma (GBM), the most common and fatal brain tumor. Systematic analysis of survival-associated AS event profiles and prognostic prediction model based on multiple AS events in GBM was needed. Methods: Genome-wide AS and RNA sequencing profiles were generated in 152 patients with GBM in the cancer genome atlas (TCGA). Prognosis-associated AS events were screened by integrated Cox regression analysis to construct the prognostic risk score model in the training cohort (n = 101). The AS-based signature and clinicopathologic parameters were applied to construct a prognostic nomogram for 0.5-, 1-, and 3-year OS prediction. Finally, the regulatory networks between prognostic AS events and splicing factors (SFs) were constructed. Results: A total of 1,598 prognosis-related AS events from 1,183 source genes were determined. Eight prognostic risk score model based on integrated AS events and 7 AS types were established, respectively. Concordance index (C-index) and receiver operating characteristic (ROC) curve analysis demonstrated powerful ability in distinguishing patients' outcomes. Only Alternate Donor site (AD) and Exon Skip (ES) signature out of the eight types of AS signature were identified as independent prognostic factors for GBM, which was validated in the internal validation cohort. The nomogram with age, new event, pharmaceutical therapy, radiation therapy, AD signature and ES signature were constructed, with C-index of 0.892 (95% CI, 0.853-0.931; P = 5.13 × 10 −15). Calibration plots, ROC, and decision curve analysis suggested excellent predictive performance for the nomogram in both TCGA training cohort and validation cohort. Splicing network indicated distinguished correlations between prognostic AS events and SFs in GBM patients. Conclusions: AS-based prediction model could serve as a promising prognostic predictor and potential therapeutic target for GBM, facilitating better treatment strategies in clinical practice.

Development of a Novel Prognostic Model of Glioblastoma Based on m6A-Associated Immune Genes and Identification of a New Biomarker

“…Biomedical big data research provides unprecedented opportunities for biomedical development, bringing many new technologies and methods for disease diagnosis and treatment, such as gene diagnosis, gene therapy, and targeted drugs. Regarding glioblastoma, various prognostic models now exist (43)(44)(45). We developed the first high-performance prognostic model based on m6A-associated immune genes; it successfully passed internal and external validation tests and has some clinical translation value.…”

Identification of a five-immune gene model as an independent prognostic factor in hepatocellular carcinoma

“…A risk model was developed with the training group, and this model was established with published methodologies [ 17 , 18 ]. Univariate analysis was performed with the “survival” package, and the genes with p < 0.05 were identified as survival-related.…”

Interaction, immune infiltration characteristics and prognostic modeling of efferocytosis-related subtypes in glioblastoma

“…The widespread heterogeneity within and between individuals is the root cause of GBM treatment failure, making it one of the most aggressive and treatment-resistant malignancies [ 5 ]. Therefore, discovering new biomarkers and establishing effective molecular staging systems to select appropriate treatments for GBM patients is crucial, as molecular alterations are increasingly important in glioma classification and grading [ 6 , 7 ].…”