Screening of Potential Hub Genes in Glioma Progression Based on Bioinformatics Analysis
Abstract
Objectives: Glioma is the most common primary tumor of the central nervous system, and its therapeutic effect is not optimistic. In recent years, related therapeutic technologies have developed rapidly, but unfortunately, the improvement of clinical therapeutic effect is not satisfactory. In addition to conventional therapies, there are some attractive therapies, such as biological therapy (immunotherapy), gene therapy, etc[1]. Therefore, searching for potential target genes of glioma is of great significance for developing new therapeutic directions and designing new biomarkers[2]. Methods: Download gene expression data set, GSE137902 gelatin and GSE13790 matrix through NCBI-G to screen overlapping differential expression genes (DEGs). In order to identify central genes from these genes, we conducted protein protein interaction (PPI) network. To further explore the potential mechanism of central genes in glioma, we performed gene ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analysis. Then get the intersection of key genes according to five algorithms of Closeness Degree EPC MCC Stress. The intersection is obtained through GSE117423, GSE188256 and GSE90598 in geo database, and finally verified through Receiver Operating Characteristic (ROC) curve. Results: A total of 1274 differentially expressed genes are identified, and then 309 genes are obtained by intersection of the two. 16 Hub genes were obtained, and then the intersection of the two genes with GSE117423, GES188256 and GSE90598 genes was verified to obtain the key gene TIMP1 of glioma. Made the ROC curve of key gene.The intersection with hub gene was determined to identify TIMP1 as the key gene. Conclusion: The DEGs and Hub genes and signal pathways found in this study can confirm that the key gene TIMP1 is closely related to the occurrence and evolution of glioma, and provide candidate targets for the diagnosis and treatment of glioma.
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Copyright (c) 2023 Chengzhi Cui, Shi Yin, Lei Meng, Jiaming Huang, Dong Chen
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