| Background:Gastric cancer is one of the malignant tumors with high mortality and morbidity.Globally,the five-year survival rate of gastric cancer is only about 10%.In China,gastric cancer is still one of the major diseases that threaten people’s physical and mental health.At present,pathological biopsy is still the gold standard for disease diagnosis in patients with gastric cancer.However,this method of diagnosis is not suitable for patients with early gastric cancer,and may even cause unnecessary trauma to the patient.With the rapid development of high-throughput sequencing technology in recent years,more and more potential tumor targets have been gradually discovered.With the widespread use of this technology,it has been used as a very important tool in the life sciences,playing a pivotal role in the early diagnosis of cancer,cancer grading and prognosis prediction.Purpose:We selected the GSE54129 dataset from the GEO database and tried to predict the upstream and downstream gene regulation relationships by means of bioinformatics methods,aiming to find the key genes for the development of gastric cancer.To find a new target for early screening,early prevention and early diagnosis,and so it could predict the prognosis of gastric cancer patients.Methods:In the GSE54129 dataset,111 cancer samples and 21 normal gastric mucosal epithelial samples were included.A bioinformatics program was used to select differentially expressed genes(DEGs)between gastric cancer patients and healthy individuals,and then we used DAVID for gene ontology(GO)analysis and Kyoto Gene and Genomic Encyclopedia(KEGG)pathway analysis.In addition,3Cytoscape was used to visualize the protein-protein interaction network(PPI)of these DEGs.The core module was detected from the PPI network,and 15 highly related hub genes were selected.The results of the Overall Survival survival analysis were combined to determine the relevant Hub Genes.Finally,based on the specific hub Gene,bioinformatics prediction software was used to predict its upstream and downstream regulation mechanisms,aiming to provide guidance for understanding the role of tumor markers in the development of gastric cancer.Results:Differential genetic analysis revealed a total of 468 up-regulated genes that were enriched for focal adhesions,ECM receptor interactions and PI3K-Akt signaling pathways,and 503 down-regulated genes,enriched for cytochrome P450,and chemically carcinogenic,retinol metabolism and gastric acid secretion.By using the bioinformatics analysis software to construct a protein-protein interaction network(PPI network),three core modules were detected,and 15 highly related hub genes(Hub Gene),including BGN,MMP2,COL1A1 and FN1,were selected.Combined with gene association analysis,BGN gene was closely related to PD-1,which could be used as a new biomarker for the diagnosis and guidance of gastric cancer combined chemotherapy.Therefore,deep mining was carried out.Based on the authoritative bioinformatics websites such as starbase and JASPAR,it was predicted that the transcription factor YY1-induced lncRNA HCG18 competitive absorption of miR-296-3p regulated BGN expression,which in turn promoted gastric cancer migration and invasion.Conclusion: Oncogene BGN can be used as a biomarker for the grade of malignant gastric cancer and guiding combined chemotherapy.It provides a theoretical basis for early prevention,diagnosis and individualized treatment of gastric cancer at the molecular biological level. |
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