In glioma research, identifying key molecules for predicting patient prognosis is challenging due to high heterogeneity. This study explores the correlation between alternative splicing (AS) and glioma prognosis, aiming to identify molecular markers for potential treatments. Retrieve transcriptomic and clinical data of glioma samples from the cancer genome atlas, eQTLGen database, and GWAS Catalog database. Build a predictive model for patient prognosis using the lasso-cox method. Machine learning is employed to identify the most diagnostically and predictively valuable biomarkers or features. Mendelian randomization analysis is employed to evaluate the causal relationship between genes and diseases. gene ontology, Kyoto encyclopedia of genes and genomes are utilized to explore the potential pathways and mechanisms of genes. The lasso algorithm was employed to screen various AS-related genes across different types, resulting in distinct gene sets. Subsequently, predictive models for patient prognosis were constructed. Key genes, including “STEAP3,” “MLC1,” “ARHGAP19,” “LFNG,” “NRG1” and “KLC1” were identified through lasso screening. Mendelian randomization analysis highlighted the significance of NRG1 in glioma development. gene ontology analysis, encompassing biological processes, cellular components, and molecular functions, revealed enrichment in processes such as modulation of chemical synaptic transmission and ion channel activity. Moreover, Kyoto encyclopedia of genes and genomes pathways, such as Calcium signaling, MAPK signaling, cAMP signaling, and Rap1 signaling, were significantly enriched. In conclusion, our model, based on AS genes, effectively predicts glioma prognosis, identifying NRG1 as a significant marker.
Hai et al. (Fri,) studied this question.