Identifying Of Biomarkers Associated With Gastric Cancer Based On Topological Analysis And Statistical Methods

Gastric cancer is one of the most common digestive system tumors worldwide.Due to the hidden nature of the disease,the early symptoms are not obvious,most of patients were in the advanced stage at the time of treatment,there is currently no clinically specific early diagnosis and effective treatment for it.Therefore,the development of new early diagnosis and treatment methods is the current research focus.In cancer research,the analysis of gene databases to screen genes related to the occurrence and development of cancer can provide new scientific evidence for clinical diagnosis of diseases.In this study,we used comprehensive statistical and bioinformatics methods to screen hub genes related to gastric cancer from the public gene expression database,and further CDK1 was selected as the research object to explore the correlation between the expression of CDK1 and the clinical characteristics of gastric cancer,and the possibility of CDK1 as an independent prognostic factor of gastric cancer.In view of these two aspects,this paper has referred to a large number of relevant literature and carried out the following work:Methods and Results:(1)The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes(DEGs).A total of 1269 up-regulated and 330 down-regulated genes were identified.The protein-protein interactions(PPI)network of DEGs was constructed by STRING V11 database,and 11 hub genes were selected through intersection of 11 topological analysis methods.All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection(MCODE)clustering algorithm.In order to explore the role of the 11 hub genes,we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence,cell cycle,viral carcinogenesis and p53 signaling pathway were the most associated with GC.Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients.In order to validate the efficiency of our method,another mRNA expression dataset(GSE19826)was downloaded from the GEO database.The same method was carried out on this dataset,the result showed that 10 out of the above 11 hub genes were included,except C3.In addition,seven of the 11 selected hub genes were verified significantly correlated with GC by uni-or multivariable Cox model and Lasso regression analysis including C3,CDK1,FN1,CCNB1,CDC20,BUB1B and MAD2L1.(2)TCGA data and corresponding clinical features of GC were collected.Firstly,the aim gene was selected by combining four topological analysis methods.It is found that CDK1 ranks first among the four methods,so CDK1 was selected as one of the most important genes associated with GC.The gene expression in paracancerous and GC tissues was analyzed by Limma package and Wilcox test,and the expression level of CDK1 in GC tissues was significantly higher than that in paracancerous tissues.Secondly,the correlation between gene expression and clinical features was analyzed by logistic regression,the result showed that the expression level of CDK1 was significantly correlated with pathological stage and grade.Finally,the survival analysis was carried out by using the Kaplan-Meier,and the survival rate of the CDK1 high expression group was significantly lower than that of the low expression group.The gene prognostic value was evaluated by univariate and multivariate Cox analysis,the results showed that CDK1 may serve as an independent prognostic factor for GC.The gene potential biological function was explored by GSEA enrichment analysis,and found that CDK1 expression was mainly involved in prostate cancer,small cell lung cancer and gastric cancer and was enriched in WNT signaling pathway and T cell receptor signaling pathway.Conclusion:(1)C3,CDK1,FN1,CCNB1,CDC20,BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.(2)CDK1 may serve as an independent prognostic factor for GC.It is also expected to be a new target for molecular targeted therapy of GC.