MATERIAL and METHODS: The mRNA expression and clinical data of glioma were downloaded from the TCGA and CGGA databases. Coagulation-related genes were downloaded from the KEGG database. The expression model was constructed using LASSO regression. The GBM data were divided into high and low-risk expression groups based on the median risk score, and the differences in overall survival and progression-free survival between them were calculated. The prognostic model was further validated using the TCGA-LGG and CGGA glioma databases, respectively. The accuracy of the risk score was calculated by ROC analysis for 1 year and 3 years.

RESULTS: Four model genes, namely the SERPINA5, PLAUR, BDKRB1, and PTGIR, were identified, and the risk score was calculated as follows: risk score= SERPINA5*0.126264111304559 + PLAUR*0.288587629696211 + BDKRB1*0.349215422945011 + PTGIR*0.17334527969703, respectively. Based on glioma data from three groups, patients were divided into high and low-risk groups according to the median risk score. The overall survival, progression-free survival, and risk scores of the high-risk score group were worse than the low-risk group. The ROC curve analysis showed that the AUC values of the coagulation-related gene model at 1 year, 3 years, and 5 years were more than 0.65, validating the reliability of the prognostic model.

CONCLUSION: This study established the correlation between the coagulation-related gene model and glioma prognosis, providing deeper insight into the mechanism and treatment of glioma. Keywords : Glioma, Coagulation, Prognostic model, Bioinformatics