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. 2022 May 4:12:881015.
doi: 10.3389/fonc.2022.881015. eCollection 2022.

Identification by Bioinformatics Analysis of Potential Key Genes Related to the Progression and Prognosis of Gastric Cancer

Wencang Gao  1 , Min Yang  1
Affiliations

Identification by Bioinformatics Analysis of Potential Key Genes Related to the Progression and Prognosis of Gastric Cancer

Wencang Gao et al. Front Oncol. .

Abstract

Objective: Despite increasingly sophisticated medical technology, the prognosis of patients with advanced gastric cancer is still not objectively certain. Therefore, it is urgent to identify new diagnostic and prognostic biomarkers. To identify potential critical genes related to gastric cancer's staging mechanism and to the prognosis of gastric cancer.

Methods: Dynamic trend analysis was conducted to find genes with similar trends in gastric cancer staging in order to explore the differentially expressed genes in gastric cancer and identify the intersection of the results of the dynamic trend analysis. Functional predictive analysis were performed on the obtained genes to observe the expression of prognostic genes in gastric cancer and in gastric cancer stages as well as the correlation with tumor immune cell infiltration. Gastric cancer samples were collected and sequenced for follow-up analysis based on the results of the Cancer Genome Atlas (TCGA) database analysis.

Results: The expression of genes enriched in module 0 had a similar trend in gastric cancer staging. 3213 differential genes were screened. A total of 50 intersection genes were obtained among genes with similar trends, of which only 10 genes have prognostic significance in gastric cancer. These 10 genes were correlated with macrophage infiltration in varying degrees. In addition, we found that AGT was significantly abnormally expressed in the results of sample sequencing. AGT was related to the occurrence of gastric cancer and interacted with brd9, golph3, nom1, klhl25, and psmd11.

Conclusion: AGT has prominent abnormal expression in gastric cancer and may promote gastric cancer progression. This study provides a new direction for further exploring potential biomarkers and molecular targeted gastric cancer therapy.

Keywords: RNA-seq; TNM stage; bioinformatics analysis; gastric cancer; tumor immune cell infiltration.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Dynamic trend analysis: (A) the significance and gene number of each module; (B) histogram showing the situation of each module.
Figure 2
Figure 2
Differential expression analysis: (A) volcano plot of differentially expressed genes, with red indicating up-regulated genes and blue indicating down-regulated genes; (B) heat map of differential gene expression.
Figure 3
Figure 3
Venn diagram of the intersection.
Figure 4
Figure 4
Prognosis analysis: (A) forest map of screening prognostic genes; (B) KM curve of genes with prognostic significance.
Figure 5
Figure 5
Expression of 10 genes in gastric cancer and normal tissues. ***p < 0.001.
Figure 6
Figure 6
Expression of 10 genes in diverse stages of gastric cancer. *p < 0.05; **p < 0.01; ***p < 0.001; ns, non significant.
Figure 7
Figure 7
10 Relationship between gene expression and immune cells.
Figure 8
Figure 8
WGCNA analysis: (A) Expression of AGT in gastric cancer based on the sequencing expression matrix; (B) soft threshold; (C) gene clustering; (D) module eigenvector clustering; (E) module and phenotype correlation heat map; (F) AGT coexpression network. *p < 0.05.
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