Screening And Bioinformatics Analysis Of Prognosis Of Esophageal Squamous Cell Carcinoma Markers

Objective: To perform bioinformatics analysis on tissue gene chip data of patients with Esophageal Squamous Cell Carcinoma(ESCC)and Screened target genes that may be related to the mechanism of ESCC and analyzed to further screen genes related to prognosis.Methods: High-throughput gene chip data in the GEO was used to screen out the ESCC information chip.The GO gene function and KEGG enrichment analysis was used for annotation and visual analysis of the protein interaction network,and Kaplan Meier survival analysis.Results:(1)A total of 667 differentially expressed genes were identified,include 346 up-regulated genes and 321 down-regulated genes.Further screened 56 genes that were remarkable differentially expressed,of which 39 were remarkable up-regulated and 17 were remarkable down-regulated.(2)GO enrichment indicates that the biological processes of remarkable differentially expressed genes were enriched in keratinocyte differentiation,peptide cross-linking,and keratinization;cellular components were concentrated in cornified envelope,extracellular region,extracellular exosome and other parts;molecular functions were enriched in functions such as structural molecular activity,metalloendopeptidase activity,chemokine activity,etc.KEGG were enriched in Cytokine-cytokine receptor interaction and Chemokine signaling pathway.Analysis of the protein interaction network found that there were direct or indirect links between 42 remarkable differentially expressed genes,and 6remarkable differentially expressed genes interacted with other more than 5 remarkable differentially expressed genes.Some differential genes were screened to correlate with the prognosis,and the genes with better prognosis were: KRT13,MAGEA3,MAGEA6,EYA2,CLIC3;genes with poor prognosis include: LXH2,HOXA10,COL11A1,XCL1,HOXD10.Conclusion: Fifty-six remarkable differentially expressed genes were selected by analyzing the expression data of ESCC in the GEO database,which can provide a method for the prognosis of this disease.