Comprehensive Analysis to Identify MAGEA3 Expression Correlated With Immune Infiltrates and Lymph Node Metastasis in Gastric Cancer

Abstract

Gastric cancer (GC) is an aggressive malignant tumor and causes a significant number of deaths every year. With the coming of the age of cancer immunotherapy, search for a new target in gastric cancer may benefit more advanced patients. Melanoma-associated antigen-A3 (MAGEA3), one of the members of the cancer-testis antigen (CTA) family, was considered an important part of cancer immunotherapy. We evaluate the potential role of MAGEA3 in GC through the TCGA database. The result revealed that MAGEA3 is upregulated in GC and linked to poor OS and lymph node metastasis. MAGEA3 was also correlated with immune checkpoints, TMB, and affected the tumor immune microenvironment and the prognosis of GC through CIBERSORT, TIMER, and Kaplan-Meier plotter database analysis. In addition, GSEA-identified MAGEA3 is involved in the immune regulation of GC. Moreover, the protein-protein interaction (PPI) networks of MAGEA3 were constructed through STRING database and MAGEA3-correlated miRNAs were screened based on the joint analysis of multiple databases. In terms of experimental verification, we constructed pET21a (+)/MAGEA3 restructuring plasmids and transformed to Escherichia coli Rosetta. MAGEA3 protein was used as an antigen after being expressed and purified and can effectively detect the specific IgG in 93 GC patients' serum specimens with 44.08% sensitivity and 92.54% specificity. Through further analysis, the positive rate of MAGEA3 was related to the stage and transfer number of lymph nodes. These results indicated that MAGEA3 is a novel biomarker and correlated with lymph node metastasis and immune infiltrates in GC, which could be a new target for immunotherapy.

Keywords: MAGEA3; gastric cancer; immune infiltrates; immunotherapy; lymph node metastasis (LNM).

Figure 1

Expression of MAGEA3 in gastric cancer. (A) MAGEA3 expression in different types of cancer was investigated with the TIMER database. (B) Increased expression of MAGEA3 in gastric cancer compared to normal tissues in the GEPIA and TCGA database. (C) Correlation between MAGEA3 and prognosis of gastric cancer in TIMER and Kaplan-Meier plotter database. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 2

MAGEA3 expression increased significantly in gastric cancer–caused lymph node metastasis. *p < 0.05.

Figure 3

Differential expression of immune checkpoints inMAGEA3 groups and correlations of MAGEA3 expression with TMB and MSI. (A) The expression distribution of Immune checkpoints related gene in MAGEA3 high group and MAGEA3 low group. (B) Correlation analysis of MAGEA3 gene expression and TMB, MSI. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 4

Results of CIBERSORT analysis and immune infiltration between MAGEA3 high- and low-expression groups. (A) Correlation matrix of infiltration degree of immune cells in GC samples. Red indicates trends consistent with the positive correlation, and blue indicates trends consistent with the negative correlation between two immune cells. The bigger size of the number statistics data represents the more positive or negative correlation. (B) The distribution of 22 immune cells in 267 filtered gene matrix. Red indicates higher immune infiltration expression, and green indicates lower expression. (C) Violin diagram of immune cell proportions in two groups. The blue fusiform fractions on the left represent the MAGEA3 high-expression group, and the red fusiform fractions on the right represent the MAGEA3 low-expression group.

Figure 5

Correlation analysis of MAGEA3 expression and immune infiltration in GC by TIMER. (A) Association between MAGEA3 gene copy number and immune cell infiltration levels in GC cohorts. (B) Correlation of MAGEA3 expression with immune infiltration level in GC. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 6

Comparison of Kaplan-Meier survival curves of the high and low expression of MAGEA3 in GC based on immune cell subgroups. Relationships between MAGEA3 of different immune cell subgroup and prognoses in gastric cancer (A–D).

Figure 7

Enrichment plots from gene set enrichment analysis (GSEA).

Figure 8

PPI network analysis of MAGEA3-related genes. (A) Volcano plot of the differentially expressed genes between MAGEA3 high group and MAGEA3 low group. (B) PPI structureof co-expressed genes. (C) The top module involved MAGEA3 identified by MCODE plugin in Cytoscape. (D) An intersection analysis of the MAGEA3-binding and correlated genes was conducted. (E) Correlation analysis between MAGEA3 expressed and screened common genes, including MAGEA12, PRAME, and CSAG1.

Figure 9

Prediction potential miRNA of MAGEA3 in GC. (A) The predicted miRNAs targeting MAGEA3 in five databases. (B) The expression correlation between predicted miRNAs and MAGEA3 in GC analyzed by starBase database. (C) The expression of hsa-miR-767-3p in GC and control normal samples determined by starBase database.

Figure 10

pET21(+)/MAGEA3 plasmid contruction and purification. (A) The map of the expression vector pET21a(+)/MAGEA3 codon-optimized MAGEA3 942bp with 6xHis tag cloned into the prokaryotic expression vector pET21a(+) by NedI/XhoI restriction sites. (B) Affinity purified MAGE-A3 protein expressed from E. coli Rosetta were analyzed with SDS-PAGE. (C) Affinity purified MAGE-A3 identified by western blot with mouse anti-His mAb. Lane M: pre-stained protein marker; Lane 1, Rosetta; Lane 2, pET21a(+)/Rosetta; Lane 3, pET21a(+)/MAGE-A3 at 48 kDa; Lane4, purified MAGE-A3protein at 48 kDa.

Figure 11

ELISA analysis of MAGE-A3 specific IgG antibodies in the serum of gastric cancer. (A) MAGE-A3 specific serum IgG antibody levels in three groups of patients with healthy controls, chronic gastritis and gastric cancer. (B) The serum antibody positive detection rate of MAGE-A3 in each group. *P<0.05.