The prognostic model of low-grade glioma based on m6A-associated immune genes and functional study of FBXO4 in the tumor microenvironment.

Background: m6A plays a dual role in regulating the expression of oncogenes and tumor suppressor genes, and is crucial in tumorigenesis and progression. The immune system is closely involved in tumorigenesis and development, playing a key role in tumor therapy and resistance. However, research on m6A-related immune markers in low-grade gliomas is still limited and requires further investigation. Methods: All data was obtained from the Chinese Glioma Genome Atlas database and The Cancer Genome Atlas. The construction of the prognostic model and the online application of the dynamic nomogram relied on univariate Cox analysis, LASSO regression, and multivariate Cox analysis. Two different clustering analyses were performed on all samples, resulting in high, medium, and low expression groups of m6A regulatory and immune genes, followed by an analysis of the correlations between these scores. Finally, the biological role of FBXO4 in glioma cells was determined through quantitative reverse transcription polymerase chain reaction, cell proliferation assays, and cell migration experiments. Results: The prognostic model for low-grade glioma demonstrated strong performance, with an AUC over 0.9 in the training group. In the internal validation group, AUC values ranged from 0.831 to 0.894, while in the external validation group, the AUC ranged from 0.623 to 0.813. Additionally, the online application of the dynamic nomogram allowed for relatively accurate predictions of LGG patients' survival time. Further analysis revealed that the high-expression groups of m6A regulatory genes and m6A-related immune genes exhibited higher levels of immune cells and stromal cells, lower tumor purity, and poorer survival rates. GSEA enrichment analysis suggested that these findings might be related to the activation of multiple signaling pathways. This may explain the lower survival rates observed in this group. Furthermore, the m6A score was significantly associated with moderate to high expression of immune genes and high expression of m6A regulatory genes, and it showed a positive correlation with most immune cell types. Finally, Conclusion: Based on multi-dimensional clustering analysis and experimental validation, the prognostic model developed in this study can effectively assess the prognosis of LGG patients and their relationship with the immune microenvironment. Furthermore, the correlation analysis between m6A scores and the tumor microenvironment provides a foundation for further exploration of the disease's pathophysiology. Additionally, we suggest that FBXO4 may serve as an important biomarker for the diagnosis and prognosis of LGG.