Bioinformatics combined with machine learning for screening biomarkers in Helicobacter pylori-associated atrophic gastritis
[Objective]To screen potential biomarkers of Helicobacter pylori-associated atrophic gastritis(HPAG)using weighted gene co-expression network analysis(WGCNA),and machine learning algorithms.[Methods]To download the transcriptomic data of gastric tissues containing HPAG and non-Helicobacter pylori(nonHP)infection was from gene expression databases for differential analysis,and perform gene set enrichment analysis(GSEA)on differentially expressed genes(DEGs).WGCNA results and DEGs were integrated to screen HPAG-related genes.Machine learning methods such as least absolute shrinkage and selection operator(LASSO),support vector machine recursive feature elimination(SVM-RFE)and random forest(RF)were utilized to screen potential biomarkers for HPAG,and biomarker expressions were extracted for intergroup comparison.[Results]A total of 213 DEGs were obtained,which were mainly enriched in signaling pathways such as cholesterol metabolism,digestion and absorption of fat.A machine learning algorithm screened the potential biomarker of AF,S100 calcium-binding protein G(S100G).The expression level of S100G was higher in HPAG samples than in nonHP samples.[Conclusion]HPAG pathogenesis involves cholesterol metabolism,digestion and absorption of fat,and other signaling pathways.S100G expression was significantly increased in HPAG gastric tissues,which may become a new target for HPAG treatment.
万方数据
维普
