| Objective: Glioblastoma(GBM)is considered the most malignant and devastating intracranial tumor without effective treatment.Autophagy,apoptosis,and necrosis,three classically known cell death pathways,can provide novel clinical and immunological insights,which may assist in designing personalized therapeutics for GBM.In this study,we aimed to develop a gene signature based on autophagy-,apoptosis-and necrosis-related genes for prognostic implications in GBM patients,explore the differences in biological functions among different risk groups and their relationship with the immune microenvironment,to provide better decision-making basis for prognostic risk assessment and individualized treatment of GBM patients.Methods: Variations in the expression of genes involved in autophagy,apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas(TCGA)database.Univariate Cox analysis,least absolute shrinkage and selection operator(LASSO)analysis,and multivariate Cox analysis were performed to construct a combined prognostic signature.Combined with clinical characteristics,multivariate Cox proportional hazards analysis was performed to evaluate the independent predictive effect of the prediction model on overall survival(OS).Kaplan–Meier survival analysis and receiver-operating characteristic(ROC)curves based on OS and progression-free survival(PFS)were conducted to assess the performance of the gene signature.The Chinese Glioma Genome Atlas(CGGA)dataset was used for external validation.Differences in immune microenvironment and immune checkpoint blockade gene expression levels between different prognostic groups were analyzed using the TIMER2.0 database.Finally,the Connectivity Map(CMap)database was used to explore potential therapeutic drugs and molecular pathways for GBM.Results: 4 apoptosis-related genes(BID,CFLAR,CHP1,PRKAR1B),8 autophagy-related genes(SREBF1,SERPINA1,PRKAG2,PRKAB2,MET,MAPK3,LAMTOR3,EEF1A2),and 4 necrosis-related genes(CASP3,NOL3,TRAF3,TRAP1)were screened out and the combined 16-gene cell death index(CDI)was generated.Multivariate analyses demonstrated that the high CDI group was significantly associated with OS(HR=2.850,95%CI:1.981-4.100,p<0.001),and could independently predict the OS of GBM patients.Patients were clustered into either the high risk or the low risk groups according to the CDI score,and Kaplan–Meier survival analysis showed that those in the low risk group presented significantly longer OS and PFS than the high CDI group in the TCGA cohort.The results illustrated that the AUCs of CDI for predicting the 1.5-,3-and 4.5-year OS were 0.727,0.833 and 0.844,respectively.Subsequently,the model was validated by the CGGA database,and the results were consistent with the TCGA group,indicating outstanding performance of the gene signature in both the training and validation groups.Furthermore,immune cell analysis identified higher infiltration of neutrophils,macrophages,Treg,T helper cells,and a DCs,and lower infiltration of B cells in the high CDI group.Interestingly,this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200,and higher expression levels of PDCD1LG2,CD86,CD48 and IDO1.Finally,9 potential therapeutic compounds targeting CDI in GBM patients were screened through the CMap database,including: Tipifarnib,Tofacitinib,GSK-1070916,Mestranol,Ruxolitinib,XMD-1150,TPCA-1,PPT and 7,4’-dihydroxyflavone.Conclusion: The CDI prediction model has good predictive performance and predictive value,applicated well in external cohorts,and can exert a stable predictive performance.CDI is significantly associated with immune cell infiltration in GBM and the key genes of immune checkpoint blockade therapy.It provides a decision-making basis for further improving the immunotherapy of GBM.Besides,the CMap database can be used to screen out potential small molecule compounds for the treatment of GBM,providing new ideas for the research of therapeutic drugs for GBM patients... |
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