Invasion/metastasis-related differentially methylated genes predict prognosis in diffuse gliomas.
Summary
This study identified invasion/metastasis-related differentially methylated genes (DMGs) with prognostic relevance in diffuse gliomas. A risk model was constructed using two marker genes, ERRFI1 and MYO1G, demonstrating robust predictive performance in both training and independent validation cohorts. Patients classified as high-risk exhibited worse overall survival and altered immune infiltration. In vitro functional validation showed that knockdown of ERRFI1 or MYO1G inhibited glioma cell proliferation, invasion, and migration, with potential involvement of the EGFR/MAPK/ERK pathway.
Analysis
🔴 CLINIQUE: This retrospective study, based on the integration of public data, proposes a prognostic risk model for diffuse gliomas, based on DNA methylation of ERRFI1 and MYO1G. This model could improve patient stratification and complement existing prognostic information, particularly in relation to immune response. While a discovery and validation study on existing cohorts, it warrants prospective studies to assess its direct clinical impact. The time to significant clinical impact is likely 3-5 years, requiring further validation. 🟢 BIOMOL: The discovery of ERRFI1 and MYO1G as DNA methylation-related prognostic markers is a significant finding. The integration of gene expression and DNA methylation profiles from public databases represents a robust approach for biomarker identification. In vitro functional validation in glioma cell lines (T98G, U251) demonstrates a causal role for these genes in proliferation and invasion. The implication of the EGFR/MAPK/ERK pathway following ERRFI1 knockdown suggests a relevant molecular mechanism. These biomarkers could be integrated into molecular diagnostic panels for gliomas. 🔵 BIOINFO: The study employed standard bioinformatics methods for data integration (gene expression, DNA methylation) and model construction (univariate Cox regression, log-rank tests, time-dependent ROC curves). The model was evaluated on training cohorts and independently validated, enhancing its robustness. Functional enrichment analysis and immune infiltration assessment are common bioinformatics applications for interpreting genetic signatures. This type of model could be clinically deployed to aid in prognostic stratification of diffuse glioma patients, potentially complementing current histomolecular classifications.