应用生物信息学分析鉴定胃癌潜在关键基因

胜楠 庞
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Differentially expressed genes were screened out by GEO2R, and the Venndiagram plotted the intersection of three gene expression profiles to obtain common differentially expressed Genes; using online analysis websites such as DAVID, String, KM-Plot to analyze the functions of DEGs, construct protein interaction network (PPI) and the relationship with GC prognosis, and visualize the analysis results with Cytoscape, 15 candidate core genes were obtained; furthermore, GEPIA online software was used to verify the expression of core genes and the relationship with clinical stage, and the genomic changes of GC target genes were explored by using cBioPortal, and finally six core genes related to GC prognosis and stage were screened. Results: A total of 106 DEGs were obtained, and functional enrichment analysis showed that the effects of these DEGs were mainly protein degradation and cell adhesion. Predominantly affects the extracellular space in cellular components; mainly affects the same protein binding in molecular function; pathway enrichment analysis mainly focused on gastric acid secretion, protein digestion and absorption, cytochrome P450 drug metabolism, etc, 12 genes (COL1A1, COL1A2, COL11A1, COL10A1, BGN, TFF2, MUC6, ATP4A, THBS2, SULF1, CLDN18, ATP4B) were associated with GC overall survival, of which 6 key genes (COL1A1, COL1A2, THBS2, BGN, TFF2, COL11A1) is closely related to the staging of gastric cancer. 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Identification of Potential Critical Genes in Gastric Cancer by Bioinformatic Analysis
Objective: Data were obtained from public databases to analyze differentially expressed genes in gastric cancer (GC) and normal gastric tissue, and preliminarily explore potential biomarkers. Method: Gene expression profiles (GSE54129, GSE13911, GSE19826) were obtained from GEO data-base. Differentially expressed genes were screened out by GEO2R, and the Venndiagram plotted the intersection of three gene expression profiles to obtain common differentially expressed Genes; using online analysis websites such as DAVID, String, KM-Plot to analyze the functions of DEGs, construct protein interaction network (PPI) and the relationship with GC prognosis, and visualize the analysis results with Cytoscape, 15 candidate core genes were obtained; furthermore, GEPIA online software was used to verify the expression of core genes and the relationship with clinical stage, and the genomic changes of GC target genes were explored by using cBioPortal, and finally six core genes related to GC prognosis and stage were screened. Results: A total of 106 DEGs were obtained, and functional enrichment analysis showed that the effects of these DEGs were mainly protein degradation and cell adhesion. Predominantly affects the extracellular space in cellular components; mainly affects the same protein binding in molecular function; pathway enrichment analysis mainly focused on gastric acid secretion, protein digestion and absorption, cytochrome P450 drug metabolism, etc, 12 genes (COL1A1, COL1A2, COL11A1, COL10A1, BGN, TFF2, MUC6, ATP4A, THBS2, SULF1, CLDN18, ATP4B) were associated with GC overall survival, of which 6 key genes (COL1A1, COL1A2, THBS2, BGN, TFF2, COL11A1) is closely related to the staging of gastric cancer. Conclusion: Screening of differentially expressed genes and signalling pathways by bioinformatics may contribute to the study of the molecular mechanism of gastric cancer and obtain key genes related to the survival and prognosis of gastric cancer, providing new ideas for cancer diagnosis and treatment.
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