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. 2022 Dec 22:12:1050288.
doi: 10.3389/fonc.2022.1050288. eCollection 2022.

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival

Jing Yang  1   2 , Zixuan Wu  2 , Xiaoxi Wu  3 , Siya Chen  2 , Xinhua Xia  2 , Jianguo Zeng  1
Affiliations

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival

Jing Yang et al. Front Oncol. .

Abstract

Background: Stomach adenocarcinoma (STAD) arises from the mutations of stomach cells and has poor overall survival. Chemotherapy is commonly indicated for patients with stomach cancer following surgical resection. The most prevalent alteration that affects cancer growth is N6-methyladenosine methylation (m6A), although the possible function of m6A in STAD prognosis is not recognized.

Method: The research measured predictive FRGs in BLCA samples from the TCGA and GEO datasets. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from TCGA and GEO. STAD from TCGA and GEO at 24 m6A was investigated. Lasso regression was used to construct the prediction model to assess the m6A prognostic signals in STAD. In addition, the correlation between m6a and immune infiltration in STAD patients was discussed using GSVA and ssGSEA analysis. Based on these genes, GO and KEGG analyses were performed to identify key biological functions and key pathways.

Result: A significant relationship was discovered between numerous m6A clusters and the tumor immune microenvironment, as well as three m6A alteration patterns with different clinical outcomes. Furthermore, GSVA and ssGSEA showed that m6A clusters were significantly associated with immune infiltration in the STAD. The low-m6Ascore group had a lower immunotherapeutic response than the high-m6Ascore group. ICIs therapy was more effective in the group with a higher m6Ascore. Three writers (VIRMA, ZC3H13, and METTL3) showed significantly lower expression, whereas five authors (METTL14, METTL16, WTAP, RBM15, and RBM15B) showed considerably higher expression. Three readers (YTHDC2, YTHDF2, and LRPPRC) had higher levels of expression, whereas eleven readers (YTHDC1, YTHDF1, YTHDF3, HNRNPC, FMR1, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX) had lower levels. As can be observed, the various types of m6 encoders have varied ramifications for STAD control.

Conclusion: STAD occurrence and progression are linked to m6A-genes. Corresponding prognostic models help forecast the prognosis of STAD patients. m6A-genes and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in STAD, which requires further trials. In addition, the m6a-related gene signature offers a viable alternative to predict bladder cancer, and these m6A-genes show a prospective research area for STAD targeted treatment in the future.

Keywords: CNV; M6A; SNP; immunity; predicting model; stomach adenocarcinoma (STAD).

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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
Landscape of genetic and expression variation. (A) The expression of m6A regulators. (B) The CNV variation frequency. (C) CNV Analysis (M6a shows a significant association with CNV in STAD). (D) The interaction between m6A regulators (The m6A regulatory network is a study of the expression correlation and prognostic importance of the 24 m6A regulators in STAD patients). (E) Waterfall plot of TMB (the frequency of m6A regulator mutations was lower than 7%). (*P < 0.05; **P < 0.01; ***P < 0.001).
Figure 2
Figure 2
Mutation and expression correlation. (A) Mutation and expression (There was considerable variation in four genes (HNRNPC, YTHDC2, YTHDF3, and ZC3H13). (B) m6A survival analysis (All of these m6a-genes were associated with STAD prognosis.).
Figure 3
Figure 3
Tumor classification and Immune cells. (A) The consensus (When k was 3, intraorganizational ties were the greatest and intergenerational relationships were the lowest). (B) PCA (The patients were separated into three groups based on their level of risk). (C) Heatmap. (D) Kaplan-Meier OS curves (Cluster C had a greater survival rate). (E) Enriched values of immune cells (Except for CD56dim.natural.killer.cell and Eosinophil, the variability across the three groups was extremely significant). (na P > 0.05; **P < 0.01; ***P < 0.001).
Figure 4
Figure 4
GSEA. (A) GO. (B) KEGG. These genes were associated with RNA and Methylation.
Figure 5
Figure 5
Cluster variance analysis and DEGs. (A) VNN (Correlation of differential expression of m6a gene among the three clusters). (B) Boxplot. (C) The consensus (When k was 3, intraorganizational links were strongest and intergenerational connections were weakest). (D) Kaplan-Meier OS curves (Cluster B had a greater survival rate). (E) Heatmap.
Figure 6
Figure 6
Enrichment analysis. (A): GO. (B): KEGG.
Figure 7
Figure 7
M6A Score Construction. (A) Survival (The high-m6Ascore group fared better than the low-m6Ascore group). (B) Ggalluvial plot. (C) Immune cells and prognosis m6A-genes. (D) m6A regulators cluster (The highest m6Ascore was discovered in the m6A regulators cluster B, whereas the lowest m6Ascore was discovered in the m6A regulators cluster A). (E) Gene cluster.
Figure 8
Figure 8
TMB Score Construction. (A) TMB of m6Ascore.group. (B) Tumor Burden Mutation with m6Ascore. (C) TMB.survival. (D) TMB-score.survival. (E) High mutations. (F) Low mutations. (G, H) Survival investigation.
Figure 9
Figure 9
(A) T1-T2. (B) T3-4. (C) Anti-PD-1/L1 immunotherapy. (D) ips_ctla4_neg_pd1_neg. (E) ips_ctla4_neg_pd1_pos. (F) ips_ctla4_pos_pd1_neg. (G) ips_ctla4_pos_pd1_pos. (H, I) MSI analysis.
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