Study Of The Relationship Between Oxidative Stress-Related Genes And Prognosis Of Glioma Patients

Background:Gliomas are primary malignant brain tumors.High-grade gliomas are highly malignant,aggressive,and have a poor prognosis.Oxidative stress plays a crucial role in the tumorigenesis and drug resistance of gliomas.Objectives:The aim of the present study was to use integrated bioinformatics analyses to evaluate the prognostic value of oxidative stress-related genes(OSRGs)in glioma.Methods:All OSRGs were retrieved from the Gene Set Enrichment Analysis(GSEA)database.Gene expression profiles data of glioma patients and normal control samples were obtained from the Chinese Glioma Genome Atlas database(CGGA)to screen disease-associated OSRGs.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional enrichment analyses were performed on these genes.Gene expression data from glioma patients and corresponding clinical data were integrated to screen prognosis-associated OSRGs using univariate and multivariate Cox regression analyses.Pearson’s correlation test,gene interaction analysis,and protein-protein interaction analysis were performed on the screened OSRGs.The protein expression profile was verified by the Human Protein Atlas database.A risk score model was constructed and Kaplan-Meier survival analysis was performed for high-and low-risk patients.Subgroup survival analyses were also performed based on other important clinicopathological parameters.The independent prognostic value of the risk score model was assessed using receiver operating characteristic(ROC)curve analysis and principal component analysis(PCA).Stratified analysis was used to explore the distribution of risk scores in patients with glioma under different clinical parameters.External validation of the risk score model was performed using The Cancer Genome Atlas(TCGA)database.A nomogram scoring system was constructed using the risk score and other clinical parameters to predict the survival rate of glioma patients for clinical application.Results:A total of 453 OSRGs were included in the study.In this study,21 disease-associated OSRGs were identified.Functional enrichment analyses showed that these OSRGs were involved in oxidative stress processes and cancer-related pathways.After that,14 prognosis-associated OSRG genes were screened in this study and a risk score model was constructed.Kaplan-Meier analysis demonstrated a significant difference(P<0.001)between high-and low-risk patients in terms of overall survival(OS).Survival analyses in different subtypes also showed similar results.ROC analysis of 1-year(AUC=0.751),3-year(AUC=0.850),and 5-year(AUC=0.864)survival and multivariate Cox regression analysis(hazard ratio,HR=1.296,P<0.001)both confirmed that the model had good independent prognostic value.The PCA indicated that the model had a good discrimination ability in patients.In external validation using the TCGA database,the model continued to demonstrate good predictive results.Kaplan-Meier survival analysis revealed a significant difference(P<0.001)in OS between high-and low-risk groups.The AUCs of ROC analysis for 1-year,3-year,and 5-year survival were 0.799,0.839,and 0.802,respectively.And the HR was 1.288(P<0.001)in multivariate Cox regression in the TCGA cohort.Finally,a nomogram was constructed in this study,which can be used to calculate and predict the survival rate of glioma patients at 1,3,and 5 years.Conclusions:The risk score model,which was constructed based on the prognosis-associated OSRG genes,could effectively predict the prognosis of glioma patients.And the risk score was an independent risk factor for the clinical prognosis of glioma patients.Therefore,the OSRG genes have essential clinical application value for prognosis prediction of glioma,which provides a novel insight into the relationship between oxidative stress and glioma and a potential therapeutic strategy for glioma patients.