Effect Of Cuproptosis-Related LncRNAs On Prognosis And Immunotherapy Of Patients With Low-Grade Glioma

Objective:Glioma,the most common malignant primary tumor of the adult central nervous system,is a tumor originating from glial cells of the brain.Currently,the treatment for glioma patients is still standardized surgical resection combined with postoperative radiotherapy and chemotherapy.However,due to the heterogeneity of tumor cells,high invasiveness and the existence of blood-brain barrier,the overall survival time of glioma patients has not been significantly extended,and the prognosis is not optimistic.Therefore,this research mainly using bioinformatics methods through The Cancer Genome Atlas database to investigate the effect of cuproptosis-related LncRNAs on the prognosis and immunotherapy of patients with low grade glioma.Method:1.Data acquisition: Download RNA sequencing data set(RNA-SEq),patient clinical trait data set and tumor mutation data set(simple nucleotide variation(SNV)of LGG in brain tissue from TCGA.2.Identification of CR-LncRNAs in LGG: Firstly,the expression matrix of all LncRNAs was obtained according to the human reference genome annotation file.Next,19cuproptosis-related genes were obtained according to literature reports.Then,LGG gene expression matrix was intersected with 19 cuproptosis-related genes to obtain the expression levels of 19 cuproptosis-related related genes.Then Pearson correlation analysis was used to obtain the co-expression results between 19 cuproptosis-related genes and LncRNAs.If the Pearson correlation coefficient meets both |r| > 0.55 and P <0.001,the co-expression correlation coefficients of both were saved and exported,and481 Cr-ln Cr Nameeting the screening criteria were obtained.3.Construction and evaluation of prognostic model: Firstly,the expression data of CRLncRNAs and clinical survival data of LGG patients were intermingled to obtain survival expression data,and then randomly divided once to obtain Train group with 257 samples and Test group with 256 samples.The R software was used to conduct univariate Cox analysis,Lasso regression analysis and multivariate Cox analysis to establish the survival prognosis model.Then,the constructed risk model was evaluated by survival analysis,progression-free survival analysis,risk curve,risk heat map,independent prognostic analysis,ROC analysis,C-index analysis,line graph analysis,PCA analysis,etc.4.Functional enrichment analysis: First,according to the expression level of each gene in each sample of the high and low risk groups.The software analysis by R whether there was a significant difference between the two groups,selection criteria for | log FC | > 1,FDR < 0.05.The genes that met the conditions were risk differential genes,and then GO and KEGG enrichment analysis was conducted based on the differential genes.5.Tumor gene mutation analysis and mutation load survival analysis: Firstly,the gene mutation files downloaded from TCGA were processed to obtain the mutation files of high-risk group genes and low-risk group genes,and the mutation data of high and lowrisk groups were visualized to show the top 15 genes with mutation frequency.R software was run to analyze the tumor mutation load of the high-risk and low-risk groups,then surv＿cutpoint function in R software was run to obtain the optimal critical value of tumor mutation load.Patients were divided into high and low mutation load groups according to the optimal critical value,and then survival analysis was conducted for the high and low mutation load groups.6.Immune function analysis: Obtain the immune function set file through R software,and then conduct ss GSEA analysis on the sorted gene expression file and risk file,so as to obtain the score of ss GESA.The difference of immune function scores between the high and low risk groups was analyzed,and finally the correlation heat map was drawn.7.Drug sensitivity analysis: The p RRophetic package in R software was used to calculate the semi-inhibitory concentration(IC50)of 251 drugs in all samples to predict drug sensitivity,and then differential analysis was performed for drug sensitivity between the high and low risk groups.8.Statistical methods: R software was used for statistical analysis and mapping.The paired T-test was used to compare the two groups.Chi-square test was used for comparison between counting data groups.Survival analysis was presented by KaplanMeier curve,and the significance of differences was tested by Log-rank method.P <0.05 was significant.*** means P <0.001,** means P <0.01,* means P <0.05.Results:1.Construction of survival model: Through univariate Cox analysis,Lasso regression analysis and multivariate Cox analysis,we established a risk score prognostic model composed of 10 cuproptosis-related LncRNAs.2.To evaluate the predictive efficacy of cuproptosis-related LncRNAs models: KaplanMeier survival analysis,ROC curve analysis,C-index curve analysis,line graph analysis,independent prognostic analysis,PFS analysis,PCA analysis,and tumor mutation difference analysis were used to evaluate that the constructed model in this research had high predictive efficacy.3.Functional enrichment analysis: Through GO and KEGG enrichment analysis,we found that differential genes were significantly enriched in extracellular matrix,PI3 KAkt signaling pathway,ECM-receptor interaction,cell cycle,and JAK-STAT signaling pathway.4.Tumor gene mutation analysis and mutation load survival analysis: Top 5 mutations in high-risk group: IDH1,TP53,ATRX,CIC,FUBP1;In the low-score risk group,the top five gene mutations were IDH1,TP53,ATRX,CIC and TTN.In addition,the mutation load survival analysis showed that the high mutation high-risk group had the worst prognosis,while the low mutation low-risk group had the best prognosis.The difference was statistically significant.5.Immune function and drug sensitivity analysis: The analysis of immune scores between high and low risk groups showed that immune scores were statistically significant in 13 immune pathways.The IC50 of 251 drugs were calculated in each sample,suggesting that Dasatinib,JW-7-52-1,LY317615,Z-LLNle-CHO and Cyclopamine may provide new directions for LGG treatment.Conclusion1.We constructed a CR-LncRNAs consisting of 10 Cr-lncrnas(AC136297.1,CNIH3-AS2,MIR4500 HG,AC122719.3,AL049794.1,AL390195.2,AC092687.1,HSD52,AC108673.3 and AC02)6585.1)of the risk prognosis model.2.The risk prognosis model can better distinguish the high and low risk groups of LGG,and the model has a high predictive value for the prognosis of LGG patients.3.Dasatinib,JW-7-52-1,LY317615,Z-LLNle-CHO and Cyclopamine may provide new way for the treatment of LGG.