Screening Of LncRNAs Related To Pan-apoptosis In Glioblastoma And Construction Of Prognostic Risk Assessment Model Based On Bioinformatics

Objective: Glioblastoma(GBM)is the most malignant glioma among astrocytic tumors.Due to the location and invasive growth pattern of the tumor,it has the characteristics of fast growth,wide invasion and frequent recurrence.Limited effect of modern therapy,and poor prognosis,making the 5-year survival rate of glioblastoma patients still low.Therefore,early diagnosis and identification of prognostic biomarkers are crucial for improving patient survival and developing new personalized treatments.PANoptosis is a novel inflammatory programmed cell death that depends on the PANoptosome complex.When other programmed cell death pathways are inhibited or mutated,PANoptosis may provide another way to kill tumor cells and reduce the risk of acquiring drug resistance.Lnc RNA can regulate glioblastoma by affecting the growth of cancer cells,mediating metastasis,and chemosensitivity.Therefore,this study mines glioblastoma data through bioinformatics analysis,screens the pan-apoptosis-related Lnc RNAs of glioblastoma and constructs a pan-apoptosis-related Lnc RNAs risk assessment model,and evaluates the relationship between the model and immune response.The correlation between cell and immune function infiltration scores and the relationship with half-inhibitory concentration of drugs will provide reference for the future clinical treatment of glioblastoma.Methods: 1.Download the GBM transcriptome data and related clinical data through the TCGA database.At the same time,search keywords "PANoptosis" through various databases to obtain 35 pan-apoptosis-related genes,and extract 13,975GBM-related Lnc RNAs using the perl language,through the "limma" package to conduct co-expression analysis of pan-apoptosis-related genes and GBM-related Lnc RNAs,set the screening condition cor Filter > 0.4,P < 0.001,and determine the pan-apoptosis-related Lnc RNAs.2.Co-expression analysis of pan-apoptosis gene obtain from literature and Lnc RNAs to determine pan-apoptosis-related Lnc RNAs.Secondly,univariate and multivariate COX proportional hazards regression analysis and least absolute minimum shrinkage and selection operator(LASSO)regression analysis were performed on the identified pan-apoptotic Lnc RNAs.11 pan-apoptotic Lnc RNAs in glioblastoma were screened out and used to build a risk assessment model.3.patients were divided into high-risk and low-risk groups according to the median risk score of the training group,and then independent prognostic analysis,concordance index(C-index)analysis,Receiver operating characteristic(ROC)analysis,survival analysis,progression-free survival analysis,and Nomogram were used to verify the effectiveness of the model.4.We performed difference analysis and pathway enrichment analysis on high and low risk groups,observed the differential genes are enriched in.the difference between the risk score of the model and the infiltration score of immune cells and its immune function between high and low risk groups was evaluated by Single-sample enrichment analysis(ss GSEA)analysis,and the relationship between the model and the half-inhibitory concentration of drugs was predicted by drug sensitivity analysis,all of which were use Strawberry Perl and R software.Results: 1.We obtained 32 pan-apoptosis-related Lnc RNAs,which associated with the overall survival time of GBM patients by univariate Cox regression analysis.2.After constructing and optimizing the model using the LASSO-Cox method,11 lnc RNAs(LINC01299,OSMR-AS1,AC093627.7,ITGA6-AS1,AC131009.1,LINC01224,AC002070.1,LBX1-AS1,LINC02328,AC093627.1 and ZEB1-AS1)were used to construct the risk model.3.After verification,the model can effectively distinguish between high-risk and low-risk groups(P values are all statistically significant),and has good survival prediction ability(area under the ROC curve is 0.777).The differential genes of high and low risk groups are mainly concentrated in the signal pathways related to anion transmembrane transport,secretory granular membrane and osteoclast differentiation,lysosome,cysteine and methionine metabolism.Score the infiltration level of immune cells and immune function,and the results show that the expression levels of macrophages and neutrophils in high-risk groups are higher than those in low-risk groups,and the infiltration score of MHC-I reaction function in low-risk groups is higher than that in high-risk groups.With the increase of risk score,the half-inhibitory concentrations of SN-38,EX-527,Temozolomide and Salubrinal against GBM all increased.Conclusion: In this study,11 lnc RNAs of GBM pan-apoptosis were screened by bioinformatics analysis method to construct a prognostic risk assessment model,which verified the effectiveness and accuracy of the model in predicting the prognosis of GBM patients,and determined the risk score,immune infiltration level and chemical drug sensitivity.It is found that the model is helpful to evaluate the prognosis of GBM patients and provide suggestion for clinical treatment of patients.