Risk Evaluation Of The Poor Prognosis Of Glioblastoma Multiforme Based On Bioinformatics

Objective:Glioblastoma multiforme is known as a highly deteriorated diffuse glioma with strong infiltration.The main therapy method of GBM is surgical resection combined with radiotherapy and chemotherapy,but it is easy to relapse after surgery and has a poor prognosis with a median survival of about 14 months.Therefore,it is of great significance to find biomarkers which can predict the poor prognosis of GBM.Methods:Download GBM-related data sets from The Cancer Genome Atlas?TCGA?.Use R language to analyze differential genes,FDR<0.05,fold change>1,and draw related volcano maps and heat maps.A P value<0.05 was considered to be statistically significant.Use the DAVID8.4 database to analyze the differential genes that have an impact on overall survival for Gene Ontology?GO?and Kyoto Encyclopedia of Gene and Genomes?KEGG?enrichment analysis to explore the biological processes,cell composition,molecular functions and signaling pathways involved in target genes;Use the String database to obtain the protein-protein interaction network of differential genes that affect overall survival,Then combined with the MCODE plug-in in Cytoscape software to perform module analysis on the aforementioned genes.Then use the ClueGO plug-in in Cytoscape to perform GO and KEGG gene enrichment analysis on gene clusters related to GBM,observe the relationship between pathways and gene enrichment in the pathway.At the same time,Cox univariate regression analysis on the gene cluster and its clinical data.The variables that may affect patient survival were included in the Cox multivariate regression analysis.According to the median risk score,patients were divided into high-risk group and low-risk group for prognostic survival analysis,and the sensitivity and specificity of the model were tested.Results:1.In this study,the data which was downloaded from TCGA included 5 normal tissues and 156 GBM tissue samples.By difference analysis,6690 genes were obtained,including 3460 up-regulated genes and 3230 down-regulated genes?FDR<0.05,fold change>1?.2.Prognostic analysis was performed on 6690 genes that were differentially expressed,and 323 up-regulated genes and 177 down-regulated genes that affected the prognosis were obtained.GO annotation showed that target genes are involved in biological processes such as extracellular matrix tissue and integrin-mediated signaling pathway,cellular component such as collagen trimers and Focal adhesion,molecular functions such as collagen combined and integrin binding.KEGG enrichment analysis revealed that target genes were involved in signal pathways such as ECM-receptor interaction,Focal adhesion.3.Use the String database and Cytoscape software to visually analyze the protein-protein interaction network of 500 genes which were differentially expressed,and screen under specific conditions,a gene cluster covering 25 genes,which is closely related to the biological processes and signaling pathways of GBM,was obtained.4.Cox single factor regression analysis was performed on the genes contained in the gene cluster,and 23 genes with significant prognosis were obtained.Cox multi-factor regression analysis of the above genes,and a linear prediction model covering 8 genes was obtained.Survival risk score=0.373×Exp_RCN1+0.349×Exp_FN1+0.523×Exp_LAMB1-0.727×Exp_LAMC1+0.747×Exp_ITGB8-0.448×Exp_TNC+0.227×Exp_HSPG2+0.325×Exp_ITGB5.5.According to median risk score,156 GBM patients were divided into high-risk group?n=78?and low-risk group?n=78?for prognostic analysis.The results showed that the high-risk group had a poor prognosis,The AUC value of the ROC curve analysis was 0.822,indicating it has good sensitivity and specificity in predicting the risk of survival in GBM.Conclusion:1.CKAP4,COL1A1,COL5A1,COL6A1,COL6A2,COL6A3,DMP1,FAM20C,FN1,HSPG2,IGFBP1,ITGA1,ITGA3,ITGA5,ITGB5,ITGB8,LAMA4,LAMB1,LAMC1,MXRA8,PTK2,PXN,RCN1,SCG3 and TNC are involved in the biological processes and signaling pathways related to the occurrence and development of GBM,such as collagen catabolic process,integrin-mediated signaling pathway,extracellular matrix organization,extracellular structure organization,basal lamina,platelet-derived growth factor binding,Focal adhesion.2.RCN1,FN1,LAMB1,LAMC1,ITGB8,TNC,HSPG2,ITGB5 can be used as combined markers of prognosis in patients with GBM,and have good sensitivity and specificity compared to a single independent prognostic factor.