Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination - PubMed (original) (raw)

Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination

Weiqiang You et al. Front Immunol. 2022.

Abstract

Background: Although messenger RNA (mRNA) vaccines have unique advantages against multiple tumors, mRNA vaccine targets in stomach adenocarcinoma (STAD) remain unknown. The potential effectiveness of mRNA vaccines is closely associated with the tumor immune infiltration microenvironment. The present study aimed to identify tumor antigens of STAD as mRNA vaccine targets and systematically determine immune subtypes (ISs) of STAD that might be suitable for immunotherapy.

Methods: Gene expression profiles and clinical data of patients with gastric cancer were downloaded from The Cancer Genome Atlas (TCGA; n = 409) and the Gene Expression Omnibus (GEO; n = 433), and genomic data were extracted from cBioPortal. Differential gene expression was analyzed using the limma package, genetic alterations were visualized using maftools, and prognosis was analyzed using ToPP. Correlations between gene expression and immune infiltration were calculated using TIMER software, and potential ISs were identified using ConsensusClusterPlus. Functional enrichment was analyzed in clusterProfiler, and r co-expression networks were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R.

Results: Overexpression of the prognostic and highly mutated antigens ADAMTS18, COL10A1, PPEF1, and STRA6 was associated with infiltration by antigen-presenting cells in STAD. Five ISs (IS1-IS5) in STAD with distinct prognoses were developed and validated in TCGA and GEO databases. The tumor mutational burden and molecular and clinical characteristics significantly differed among IS1-IS5. Both IS1 and IS2 were associated with a high mutational burden, massive infiltration by immune cells, especially antigen-presenting cells, and better survival compared with the other subtypes. Both IS4 and IS5 were associated with cold immune infiltration and correlated with advanced pathological stages. We analyzed the immune microenvironments of five subtypes of immune modulators and biomarkers to select suitable populations for mRNA vaccination and established four co-expressed key modules to validate the characteristics of the ISs. Finally, the correlation of these four mRNA vaccine targets with the transcription factors of DC cells, including BATF3, IRF4, IRF8, ZEB2, ID2, KLF4, E2-2, and IKZF1, were explored to reveal the underlying mechanisms.

Conclusions: ADAMTS18, COL10A1, PPEF1, and STRA6 are potential mRNA vaccine candidates for STAD. Patients with IS1 and IS2 are suitable populations for mRNA vaccination immunotherapy.

Keywords: immune subtype; immunotherapy; mRNA vaccine; stomach adenocarcinoma; tumor immune microenvironment.

Copyright © 2022 You, Ouyang, Cai, Chen and Wu.

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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

Identification of potential prognostic markers for STAD. (A) Statistical data of differentially expressed, high-frequency mutation, and prognosis-related genes in TCGA cohort. (B, D, F, H) ADAMTS18, COL10A1, PPEF1, and STRA6 are differentially expressed in STAD compared with that in adjacent normal tissues. (C, E, G, I) ADAMTS18, COL10A1, PPEF1, and STRA6 are risk factors for poor prognoses.

Figure 2

Figure 2

Correlations between gene expression and immune cells. Expression of ADAMTS18 (A), COL10A1 (B), PPEF1 (C), and STRA6 (D) correlates with six immune cell types (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells).

Figure 3

Figure 3

Identification of immune subtypes for STAD. (A) Consensus cumulative distribution function for K = 2–10. (B) Delta area for K = 2–10. (C) Immune subtypes (IS1–IS5) in TCGA cohort at K = 5. (D) Prognosis associated with five immune subtypes in TCGA cohort. (E, F) Distribution of IS1–IS5 across gastric cancer stages (E) and grades (F) in TCGA cohort. (G) Prognosis associated with five immune subtypes in GEO cohort. (H, I) Distribution of IS1–IS5 across gastric cancer N (H) and T (I) stages in GEO cohort.

Figure 4

Figure 4

Association between immune subtypes and tumor mutational burden. (A) Tumor mutational burden, (B) number of mutated genes, and (C) top 20 high-frequency mutation genes in IS1–IS5.

Figure 5

Figure 5

Immune checkpoints (ICPs), immunogenic cell death (ICD) regulators, and immune infiltration related to immune subtypes. (A, B) Expression of 43 ICP-related genes significantly differs in all subgroups in TCGA (A) and GEO (B) cohorts. (C, D) Significantly different expression of ICD-related genes in TCGA (C), n = 20) and GEO cohort (D), n = 23). (E, F) Differences of immune-infiltrating cells among five subgroups performed by EPIC (E) and McP-Counter (F). *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Figure 6

Figure 6

Identification of immune cells in immune subtypes (ISs) for STAD. (A) Heatmap of ssGSEA showing scores for types of immune cells with IS1–IS5 in TCGA cohort. (B) Immune cell types significantly differ between IS1+IS2 and IS4+IS5 groups in TCGA cohort. (C) Distribution of IS1–IS5 across C1–C6 in TCGA cohort. (D) Heatmap of ssGSEA showing scores for immune cell types with IS1–IS5 in GEO cohort. (E) Immune cell types significantly differ between IS1+IS2 and IS4+IS5 groups in GEO cohort. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Figure 7

Figure 7

Co-expression network analysis of immune-related genes in STAD. (A) Selection of soft threshold for power. (B) Co-expression network modules and (C) number of immune-related genes in each module. (D) Enrichment scores for each module in IS1–IS5 immune subtypes. (E) Prognosis for each module in TCGA cohort. (F) Blue module, KEGG pathway enrichment. (G) Overall survival curves differ between low and high blue modules. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Figure 8

Figure 8

A brief diagrammatic figure to summarize our findings.

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