Identification Of Molecular Markers In Different Stages Of Gastric Cancer Using Bioinformatics Tools

Gastric cancer is a common cancer,accounting for about 6% of all cancers worldwide and the third leading cause of death from cancer.The occurrence of gastric cancer is often a multifactorial and multistep developmental process.The development from normal cells to gastric cancer cells is a complex process,during which multiple transformations of cells may occur,resulting in pathological changes such as atypical hyperplasia of gastric mucosal tissue and intestinal metaplasia.These changes are collectively referred to as gastric cancer precancerous lesions.Identifying the pathological phenomenon of precancerous lesions of gastric cancer is the basis for early treatment,and it is also a feasible screening plan for preventing the occurrence of early gastric cancer.However,precancerous lesions of gastric cancer and early gastric cancer often have no obvious clinical features.From the perspective of gene expression profiling,the study of potential biomarkers and related biological processes in precancerous lesions and early gastric cancer tissues will help to analyze the mechanism of occurrence and development of gastric cancer and provide assistance for the screening of clinical early gastric cancer.To this end,this paper mainly carries out the following research work:(1)Screening of key m RNAs in the development of early gastric cancer.The gene expression profile chips GSE55695 and GSE130823 related to different stages of gastric lesions(gastritis,low-grade,high-grade intratumoral and early gastric cancer)were downloaded from the Gene Expression Omnibus.After preprocessing of batch effect validation,data normalization,and probe re-annotation,two chip datas were integrated into a new expression profile matrix.Differential analysis of expression profile data containing gastric different lesions,construction of protein interaction and co-expression networks to screen key genes.Enrichment analysis was used to study the biological functions of key genes.The results of the study showed that new key m RNAs were screened from samples of various disease stages.Among them,the biological functions of key m RNAs in low-grade intraneoplastic samples are mainly related to protein secretion regulation,vitamin and fat digestion and absorption,cholesterol metabolism,platelet activation,and p53 signaling pathway.The key m RNA enrichment results of high-grade intratumoral neoplasia and early gastric cancer were similar,and they were all enriched in IL-17 signaling pathway,tumor necrosis factor signaling pathway,chemokine signaling pathway,cytokine-cytokine receptor interaction,cytokines Pathways such as viral protein interactions under cytokine receptor binding,rheumatoid arthritis,and epithelial cell signaling in H.pylori infection.This study provides a theoretical basis for understanding the mechanism of occurrence and development of gastric cancer.(2)Screening of key lnc RNAs in the development of early gastric cancer.First,by re-annotating the probe information of the above two chip platforms,the corresponding lnc RNA expression profile data was obtained;then differential analysis was performed to screen the differential m RNAs and differential lnc RNAs in each lesion sample;then these differentially expressed m RNAs and lnc RNAs were used to construct a lnc RNA-m RNA co-expression network,and finally lnc RNAs in important nodes were screened as key lnc RNAs according to the characteristics of the co-expression network.The results suggest that key lnc RNAs during the development of early gastric cancer may be involved in biological processes such as potassium ion transmembrane transport,arginine and proline metabolism,and gastric acid secretion.The screened key lnc RNAs PRDM16-DT,HOXA11-AS and SEMA3B-AS1 may be closely related to the occurrence and development of early gastric cancer,and are worthy of further experimental research.(3)Survival analysis of gastric cancer.Gastric cancer GSE62254 microarray expression profile were downloaded from GEO,and samples for survival analysis were counted based on clinical information.The differentially expressed m RNAs shared with early gastric cancer were then screened.Next,using univariate COX,LASSO algorithm,and multivariate COX regression to analyze shared differentially expressed m RNAs,the differentially expressed m RNAs associated with prognostic risk were identified and used to construct a prognostic risk model.Finally,using the median risk score of all gastric cancer patients as the grouping criterion,the samples were divided into high-risk group and low-risk group,and the Kaplan-Meier survival curve was used to analyze whether there was a significant difference in the survival rate between the two groups.The results show that the gastric cancer prognostic risk model constructed by ADGRG7,CT83 and MMP12 has good prognostic risk prediction ability,and will provide an objective basis for clinical decision-making in gastric cancer treatment.