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胃腺癌 mRNA 疫苗免疫亚型的综合分析。

Comprehensive Analyses of Immune Subtypes of Stomach Adenocarcinoma for mRNA Vaccination.

机构信息

Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

出版信息

Front Immunol. 2022 Jul 7;13:827506. doi: 10.3389/fimmu.2022.827506. eCollection 2022.

DOI:10.3389/fimmu.2022.827506
PMID:35874675
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9300892/
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.

摘要

背景

信使 RNA(mRNA)疫苗在针对多种肿瘤方面具有独特优势,但胃腺癌(STAD)的 mRNA 疫苗靶点仍不清楚。mRNA 疫苗的潜在有效性与肿瘤免疫浸润微环境密切相关。本研究旨在鉴定 STAD 的肿瘤抗原作为 mRNA 疫苗靶点,并系统确定可能适合免疫治疗的 STAD 免疫亚型(IS)。

方法

从癌症基因组图谱(TCGA;n=409)和基因表达综合数据库(GEO;n=433)下载胃癌患者的基因表达谱和临床数据,并从 cBioPortal 提取基因组数据。使用 limma 软件包分析差异基因表达,使用 maftools 可视化遗传改变,使用 ToPP 分析预后。使用 TIMER 软件计算基因表达与免疫浸润的相关性,使用 ConsensusClusterPlus 识别潜在的 IS。在 clusterProfiler 中分析功能富集,在 R 中使用加权基因共表达网络分析(WGCNA)包分析 r 共表达网络。

结果

在 STAD 中,预后和高度突变抗原 ADAMTS18、COL10A1、PPEF1 和 STRA6 的过表达与抗原呈递细胞的浸润有关。在 TCGA 和 GEO 数据库中开发并验证了 STAD 中具有不同预后的 5 种 IS(IS1-IS5)。IS1-IS5 的肿瘤突变负担和分子及临床特征显著不同。IS1 和 IS2 均与高突变负担、大量免疫细胞浸润有关,尤其是抗原呈递细胞,与其他亚型相比生存更好。IS4 和 IS5 均与冷免疫浸润有关,与晚期病理分期相关。我们分析了 5 种免疫调节亚型和生物标志物的免疫微环境,以选择适合 mRNA 疫苗接种的人群,并建立了 4 个共表达的关键模块来验证 IS 的特征。最后,探索了这四个 mRNA 疫苗靶点与 DC 细胞转录因子(包括 BATF3、IRF4、IRF8、ZEB2、ID2、KLF4、E2-2 和 IKZF1)的相关性,以揭示潜在的机制。

结论

ADAMTS18、COL10A1、PPEF1 和 STRA6 是 STAD 的潜在 mRNA 疫苗候选物。IS1 和 IS2 患者是适合 mRNA 疫苗接种免疫治疗的人群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/11a8763d7483/fimmu-13-827506-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/33c612c12219/fimmu-13-827506-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/01c2c310aaa4/fimmu-13-827506-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/98ccad88b36a/fimmu-13-827506-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/1f65f835ca1b/fimmu-13-827506-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/11a8763d7483/fimmu-13-827506-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/33c612c12219/fimmu-13-827506-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/7ce5bae144da/fimmu-13-827506-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/fe6838fb0bd3/fimmu-13-827506-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/404079006be4/fimmu-13-827506-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/01c2c310aaa4/fimmu-13-827506-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/98ccad88b36a/fimmu-13-827506-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/1f65f835ca1b/fimmu-13-827506-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef9a/9300892/11a8763d7483/fimmu-13-827506-g008.jpg

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