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一种新型的m6A/m5C/m1A/m7G相关分类及风险特征预测胃癌预后并揭示免疫治疗倾向

A novel m6A/m5C/m1A/m7G-related classification and risk signature predicts prognosis and reveals immunotherapy inclination in gastric cancer.

作者信息

Chen Ruyue, Jiang Lixin

机构信息

Medical College, Qingdao University, Qingdao, China.

Department of Gastrointestinal Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.

出版信息

Transl Cancer Res. 2024 Jul 31;13(7):3285-3298. doi: 10.21037/tcr-23-2325. Epub 2024 Jul 26.

DOI:10.21037/tcr-23-2325
PMID:39145046
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11319980/
Abstract

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity and mortality rates, and the prognosis is not optimistic. Therefore, the search for new biomarkers is crucial. Methylation modifications in RNA modifications play a crucial role in tumors. However, the role of methylation modification of integrated m6A/m5C/m1A/m7G, in GC and its related analysis have not been reported. It still needs to be studied in depth. Our study aims to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provide new strategies for GC treatment.

METHODS

We used TCGA-STAD (The Cancer Genome Atlas-Stomach Adenocarcinoma) as a training set and GSE84433 as a validation set to analyze and determine potential associations between m6A/m5C/m1A/m7G-related genes and clinical risk of GC. In addition, we explored the prognostic value and potential biological mechanisms of m6A/m5C/m1A/m7G-related genes in GC through consistent clustering, differential expression gene identification, enrichment analysis, and immune infiltration analysis. Finally, we constructed m6A/m5C/m1A/m7G-related risk signature (MRRS) to evaluate the correlation between risk grade and survival prognosis, drug sensitivity, and immune infiltration, and validated the validity by immunohistochemical staining.

RESULTS

We identified subgroups of C1, C2, and C3 patients by consensus clustering using data from 45 m6A/m5C/m1A/m7G-related genes. The three groups showed significant differences in survival, immune scores, and immune cell infiltration. We then constructed MRRS using least absolute shrinkage and selection operator (LASSO) regression analysis, including and , which could accurately differentiate between high-/low-risk populations. Its accuracy was further validated in the validation set and immunohistochemical staining. These results suggest that m6A/m5C/m1A/m7G are closely related to the GC tumor immune microenvironment, and MRRS has good performance in predicting the survival of GC patients.

CONCLUSIONS

In this study, we highlighted the association of m6A/m5C/m1A/m7G subtypes with changes in the GC immunotumor microenvironment. We constructed and validated MRRS, which is valuable in predicting survival, immune infiltration and drug sensitivity in GC patients. This helps to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provides new strategies for GC treatment.

摘要

背景

胃癌（GC）具有高发病率和死亡率的特点，预后不容乐观。因此，寻找新的生物标志物至关重要。RNA修饰中的甲基化修饰在肿瘤中起着关键作用。然而，整合的m6A/m5C/m1A/m7G甲基化修饰在胃癌中的作用及其相关分析尚未见报道，仍需深入研究。本研究旨在加深我们对m6A/m5C/m1A/m7G甲基化的理解，并可能为胃癌治疗提供新策略。

方法

我们使用TCGA-STAD（癌症基因组图谱-胃腺癌）作为训练集，GSE84433作为验证集，分析并确定m6A/m5C/m1A/m7G相关基因与胃癌临床风险之间的潜在关联。此外，我们通过一致性聚类、差异表达基因鉴定、富集分析和免疫浸润分析，探讨了m6A/m5C/m1A/m7G相关基因在胃癌中的预后价值和潜在生物学机制。最后，我们构建了m6A/m5C/m1A/m7G相关风险特征（MRRS），以评估风险等级与生存预后、药物敏感性和免疫浸润之间的相关性，并通过免疫组织化学染色验证其有效性。

结果

我们使用来自45个m6A/m5C/m1A/m7G相关基因的数据，通过一致性聚类确定了C1、C2和C3患者亚组。这三组在生存、免疫评分和免疫细胞浸润方面存在显著差异。然后，我们使用最小绝对收缩和选择算子（LASSO）回归分析构建了MRRS，包括[具体基因1]和[具体基因2]，它可以准确区分高/低风险人群。其准确性在验证集和免疫组织化学染色中得到进一步验证。这些结果表明，m6A/m5C/m1A/m7G与胃癌肿瘤免疫微环境密切相关，MRRS在预测胃癌患者生存方面具有良好性能。

结论

在本研究中，我们强调了m6A/m5C/m1A/m7G亚型与胃癌免疫肿瘤微环境变化的关联。我们构建并验证了MRRS，它在预测胃癌患者的生存、免疫浸润和药物敏感性方面具有重要价值。这有助于加深我们对m6A/m5C/m1A/m7G甲基化的理解，并可能为胃癌治疗提供新策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/26d9903eac44/tcr-13-07-3285-f8.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/db139d13a7da/tcr-13-07-3285-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/50b43d07bec5/tcr-13-07-3285-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/6b8ce078fcd7/tcr-13-07-3285-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/c2f6f24a85bd/tcr-13-07-3285-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/4114d342b91b/tcr-13-07-3285-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/26d9903eac44/tcr-13-07-3285-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/21ca6e96a902/tcr-13-07-3285-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/f8ea5f749a75/tcr-13-07-3285-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/db139d13a7da/tcr-13-07-3285-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/50b43d07bec5/tcr-13-07-3285-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/6b8ce078fcd7/tcr-13-07-3285-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/c2f6f24a85bd/tcr-13-07-3285-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/4114d342b91b/tcr-13-07-3285-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30f0/11319980/26d9903eac44/tcr-13-07-3285-f8.jpg

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