Identification of CRF-related molecular subtypes and a prognostic CRRS model in glioma: insights into immunotherapy response prediction and candidate biomarkers

Cancer-related fatigue (CRF) is a common symptom in glioma patients, significantly affecting their quality of life. The underlying mechanisms of fatigue in these patients are not fully understood. Using CRF-associated genes (CAGs), we identified two CRF-related subtypes in glioma patients. We evaluated differences in clinicopathological features, immune characteristics, and prognostic stratification between these subtypes. A CRF-related gene-based risk score (CRRS) model was developed using LASSO Cox regression analysis. A nomogram was created to facilitate clinical use. Expression assessment of CAGs was conducted utilizing tissue microarray (TMA) and single-cell sequencing data. Computer-aided screening of potential small-molecule compounds was performed via molecular docking (MD) and molecular dynamics simulation (MDS) to obtain candidate agents for glioma treatment, and their anti-glioma activities were subsequently assessed through in vitro experiments.‌ We identified two CRF-related clusters and constructed a CRRS model based on 10 key CAGs. CRRS was significantly associated with age, WHO grade, IDH mutation status, and 1p/19q codeletion status. High-risk and low-risk groups differed in prognosis, immune cell infiltration, expression of immune checkpoint inhibitors, major histocompatibility complex and T-cell stimulators, computational prediction of immunotherapy response, and drug sensitivity. TMA analysis revealed upregulated expression of IRF7 in glioma. Docetaxel and SNX-2112 can inhibit the proliferation and migration of glioma cells in vitro and promote their apoptosis.‌ CRRS is a potentially reliable prognostic biomarker for glioma and may be correlated with CRF symptoms in patients. IRF7 can serve as a potential biomarker for glioma. Docetaxel and SNX-2112 demonstrate promising prospects as candidate therapeutic compounds for glioma.