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. 2022 Jun 15:10:913307.
doi: 10.3389/fcell.2022.913307. eCollection 2022.

N6-Methyladenosine Modification Patterns and Tumor Microenvironment Immune Characteristics Associated With Clinical Prognosis Analysis in Stomach Adenocarcinoma

Zhang Meijing  1 , Luo Tianhang  2 , Yang Biao  2
Affiliations

N6-Methyladenosine Modification Patterns and Tumor Microenvironment Immune Characteristics Associated With Clinical Prognosis Analysis in Stomach Adenocarcinoma

Zhang Meijing et al. Front Cell Dev Biol. .

Abstract

Background: N6-methyladenosine (m6A) modification is a part of epigenetic research that has gained increasing attention in recent years. m6A modification is widely involved in many biological behaviors of intracellular RNA by regulating mRNA, thus affecting disease progression and tumor occurrence. However, the effects of m6A modification on immune cell infiltration of the tumor microenvironment (TME) are uncertain in stomach adenocarcinoma (STAD). Methods: The Cancer Genome Map (TCGA) database was used to download transcriptome data, clinicopathological data, and survival data for m6A-regulated genes in 433 STAD tissues that meet the requirements of this study. GSE84437 data were obtained from the Gene Expression Omnibus (GEO) database. The correlation between 23 m6A regulated genes was analyzed using R software. Sample clustering analysis was carried out on the genes of the m6A regulatory factor, and survival analysis and differentiation comparison were made for patients in clustering grouping. Then, the Gene Set Enrichment Analysis (GSEA), the single-sample GSEA (ssGSEA), and other methods were conducted to assess the correlation among m6A modification patterns, TME cell infiltration characteristics, and immune infiltration markers. The m6A modification pattern of individual tumors was quantitatively evaluated using the m6A score scheme of the principal component analysis (PCA). Results: From the TCGA database, 94/433 (21.71%) samples were somatic cell mutations, and ZC3H13 mutations are the most common. Based on the consensus, matrix k-3 is an optimal clustering stability value to identify three different clusters. Three types of m6A methylation modification patterns were significantly different in immune infiltration. Thus, 1028 differentially expressed genes (DEGs) were identified. The survival analysis of the m6A score found that patients in the high m6A score group had a better prognosis than those in the low m6A score group. Further analysis of the survival curve combining tumor mutation burden (TMB) and m6A scores revealed that patients had a significantly lower prognosis in the low tumor mutant group and the low m6A score group (p = 0.003). The results showed that PD-L1 was significantly higher in the high m6A score group than in the low score group (p < 2.22e-16). The high-frequency microsatellite instability (MSI-H) subtype score was significantly different from the other two groups. Conclusions: This study systematically evaluated the modification patterns of 23 m6A regulatory factors in STAD. The m6A modification pattern may be a critical factor leading to inhibitory changes and heterogeneity in TME. This elucidated the TME infiltration characteristics in patients with STAD through the evaluation of the m6A modification pattern.

Keywords: N6-methyladenosine; immunotherapy; microsatellites instability; mutation burden; stomach adenocarcinoma; tumor microenvironment.

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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
Genetic variation profile of m6A regulators in STAD. (A) Mutation frequency of the m6A regulators of stomach adenocarcinoma patients in the TCGA-STAD cohort. (B) Location of CNV changes of 23 m6A regulators on the chromosome. (C) A histogram plotting the CNV mutation frequency of each gene obtained by statistical analysis of the copy number of m6A. The abscissa was the m6A-related gene, and the ordinate was the mutation frequency. (D) The box plot of m6A differential expression analysis in the tumor and normal samples. The asterisks represented the statistical p value (∗∗∗ p < 0.0001, ∗∗ p < 0.01, ∗ p < 0.05).
FIGURE 2
FIGURE 2
Identification of m6A methylation modification patterns in STAD. (A) Interaction of 23 m6A regulators and their prognostic significance in STAD. The circle size represented the effect of each regulator on the prognosis, and the range of values calculated by Cox test was p < 0.001, p < 0.01, p < 0.05, and p < 1, respectively. (B) Kaplan–Meier curves of m6A modification patterns. Kaplan-Meier curves with Log-rank p-value 0.005 showed a significant survival difference among three m6A modification patterns.
FIGURE 3
FIGURE 3
Distinct immune landscapes in m6A modification patterns and the biological characteristics of each pattern. (A–C) GSVA analyzed the differences between functional pathways in m6A modification patterns (adjusted p-value < 0.05). (A), m6Acluster A vs. m6Acluster B; (B), m6Acluster B vs. m6Acluster C; (C), m6Acluster A vs. m6Acluster C. (D) Differential expression analysis of 23 immune cells among three m6A modification patterns. The asterisks represented the statistical p value (∗∗∗ p < 0.0001, ∗∗ p < 0.01, ∗ p < 0.05). (E) Scatter plot of PCA for m6A methylation modification pattern. (F) Unsupervised clustering of 23 m6A regulators of STAD.
FIGURE 4
FIGURE 4
m6A-related genes’ functional annotation. (A) 1028 m6A-related DEGs between three m6A clusters are shown in the Venn diagram. (B) Functional annotation for m6A-related genes using GO enrichment analysis on the bar chart. (C) Functional annotation for m6A-related genes using GO enrichment analysis on the bubble chart. (D) Functional annotation for m6A-related genes using KEGG enrichment analysis on the bar chart. (E) Functional annotation for m6A-related genes using KEGG enrichment analysis on the bubble chart.
FIGURE 5
FIGURE 5
Identification of m6A-related genes’ phenotypes and m6A scores. (A) Heat map of genetic modification patterns. (B) Survival curves of different geneclusters (p < 0.0001, Log-rank test). (C) Box plot of the differential expression analysis of m6A-related genes among different geneclusters. The asterisks represented the statistical p value (∗∗∗ p < 0.0001, ∗∗ p < 0.01, ∗ p < 0.05). The one-way ANOVA test was used to test the statistical differences among three gene clusters. (D) Sankey diagrams of different genotypes. (E) Correlation analysis between the m6A score and immune cells, with red indicating positive correlation and blue indicating a negative correlation. The Kruskal-Wallis test was used to compare the statistical difference between three gene clusters (p < 0.001). (F) Differential expression analysis of the m6A score in the m6A cluster. (G) Difference analysis of m6A score in genecluster (p < 0.001, Kruskal-Wallis test)
FIGURE 6
FIGURE 6
m6A scores’ clinical prognosis analysis and somatic tumor mutations. (A) Survival analysis of high- and low-m6A score groups using Kaplan-Meier curves (p < 0.001, Log-rank test). (B) Stratified analysis of the m6A score for STAD patients by tumor mutation burden (p < 0.001, Wilcoxon test). (C) A scatter plot describing the positive correlation between the m6A score and TMB. (D) Survival analysis of TMB (p < 0.001, Log-rank test). (E) Survival analysis of TMB combined with m6A score (p = 0.003, Log-rank test). (F) Waterfall chart of the high-m6A score group. (G) Waterfall chart of the low-m6A score group.
FIGURE 7
FIGURE 7
Validation and application of the m6A score in clinical evaluation. (A) Proportion of survival and death in high- and low-m6A score groups. (B) Comparison of the m6A score between survival and dead patients (p < 0.001, Wilcoxon test). (C) Stratified analysis of the m6A score for STAD patients by T1-T2 (p = 0.006, Log-rank test). (D) Stratified analysis of m6A score for STAD patients by T3-T4 (p < 0.001, Log-rank test). (E) Stratified analysis of m6A score for STAD patients by PD-L1 (p < 0.0001, Wilcoxon test).
FIGURE 8
FIGURE 8
Analysis of the m6A score in anti-PD-L1 and CTLA-4 immunotherapy. (A) Differential analysis for the low m6A score group and the high m6A score group in immunophenoscore (IPS) with CTLA4 (+)/PD1 (+) (p = 0.084, Wilcoxon test). (B) Differential analysis for the low m6A score group and the high m6A score group in IPS with CTLA4 (+)/PD1 (−) (p < 0.0001, Wilcoxon test). (C) Differential analysis for the low m6A score group and the high m6A score group in IPS with CTLA4 (−)/PD1 (+) (p = 0.013, Wilcoxon test). (D) Differential analysis for the low m6A score group and the high m6A score group in IPS with CTLA4 (−)/PD1 (−) (p < 0.0001, Wilcoxon test). (E) The proportion of the m6A score in groups with high or low MSI and stable status. (F) Differences in the m6A score among high or low MSI and stable status. The differences between the three groups were compared through the Kruskal-Wallis test. MSS, microsatellite stable; MSI-H, high microsatellite instability; MSI-L, low microsatellite instability.
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