A signature constructed with circadian rhythm-related genes to predict the prognosis and therapy response for low-grade gliomas

Recent studies suggest circadian rhythm-related genes are crucial for immune balance and potential therapeutic targets, but their role in low-grade gliomas (LGG) is not well understood. We analyzed their prognostic significance in glioma progression using predictive models. TCGA-LGG cohort data was analyzed utilizing univariate Cox regression and LASSO to create a strong prognostic model. Kaplan-Meier and ROC analyses validated the model's predictive accuracy, revealing significantly poorer survival outcomes in high-risk patients relative to those at low risk. Cox regression identified our gene signature as an independent prognosistic predictor. Decision curve analysis (DCA) and calibration supported the nomogram's predictive power. Further analyses, including gene alteration, functional enrichment, tumor immune microenvironment, and chemotherapy sensitivity, highlighted differences between risk groups. We also conducted immunohistochemical analyses on clinical samples and in vitro experiments with glioma cell lines. Our findings highlight the clinical importance of these genes and confirm the prognostic utility of the survival analysis model, suggesting its promise for guiding outcome prediction and immunotherapy strategies in LGG.