Bioinformatics Analysis Of Differentially Expressed Genes And Key Pathways In Bladder Cancer

ObjectAmong urinary tumors,bladder cancer is more common worldwide,and the incidence and mortality of bladder cancer is higher than other urological tumors.The accumulation of proto-oncogenes,tumor suppressor genes,and somatic mutations is thought to be the cause of BC occurrence,development,and metastasis.Although many cancer-related genes and cellular pathways have been shown to be involved in the development and progression of BC,the accuracy of early diagnosis,treatment,and prognosis assessment of BC remains low.Therefore,studying the molecular mechanisms of proliferation,apoptosis and invasion of bladder cancer is crucial for the development of diagnostic and therapeutic strategies.With the development of gene microarray technology,various advanced gene expression evaluation technologies are widely used in the development and progress of tumors at a low cost.Due to the deepening of gene research,a large number of gene expression profiles and differentially expressed genes(DEGs)have been revealed,which has laid a rich information foundation for our study of gene regulatory networks.However,comparative analysis of DEGS in independent studies appears to show little substantial overlap and no reliable biomarker profiles to identify cancerous and non-cancerous samples.At present,due to the bioinformatics method,the data generated by microarray technology is analyzed to determine the gene expression changes of collected urothelial cells,and to find and find DEGs——Differential expression of genes between cancerous and normal tissues of the bladder.However,the discovery of DEGs,and the interactions between DEGs,especially in the DEGs interaction network,still require further validation in clinical work and in the next experiments.This study used bioinformatics methods to explore the underlying molecular mechanisms of bladder cancer development using bioinformatics tools and to develop potential BC therapeutic targets.This provides a new direction for further research of the experiment,and also provides a new theoretical basis for the clinical diagnosis and treatment of BC patients.MethodThis study used the bladder cancer-associated gene chip expression data obtained from the public gene chip expression profiling database(GEO)of the National Center for Biotechnology(NCBI)as an analytical material.Using GEO2 R online statistical analysis software for comparative analysis,a significant differentially expressed gene between BC and normal bladder mucosa was screened from a large number of gene sequences.Then,the DAVID online analysis tool was used to perform GO function analysis and KEGG pathway enrichment analysis on the above DGES.At the same time,we introduce the bioinformatics analysis tools such as String and Cytoscape,and construct a protein interaction network model of differential genes based on the relationship between genes.According to the density of protein interaction,we can analyze the key node genes and information pathways.ResultWhen using bioinformatics tools,the screening conditions were set to |Log Fc|>1.5,adj.P.Val<0.01,and a total of 353 DGEs were screened.And based on the positive and negative values of the difference Log Fc,65 differentially expressed genes(Upregulated DEGs)were up-regulated and 288 Downregulated DEGs were down-regulated.SRING datebase is used to map the interaction network between DGEs——PPI network.The interactions between the proteins encoded by these genes were found to be mainly concentrated on the key node genes of TOP2 A,DCN,KIF2 C,KIF20A,CCNB2,AURKB,CDC20,and CEP55.Finally,in view of the functions and research results of these important node genes,we conducted related literature review and found that most of these genes are related to the occurrence and development of bladder cancer,and some genes have no reports that have no obvious relationship with BC,but they still report Has a high diagnostic and research value.ConclusionBioinformatics analysis of DGEs in BC and normal bladder mucosa,literature mining of these differentially expressed DGEs,further understanding of DGSs interaction information and bioinformatics pathways,providing new options for BC early diagnosis and therapeutic target selection The clues can also lay a theoretical foundation and provide new research directions for our next experimental research on BC.