Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning

Abstract

Abstract Background Gastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this pathology. Methods Microarray data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic biomarkers. Results Herein, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma cells. Conclusions BANF1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.

Keywords

• Gastric cancer
• Biomarker
• WGCNA
• Machine learning algorithm
• Immune cell infiltration