肺腺癌中M6A RNA甲基化修饰与肿瘤免疫微环境的关系

Shujuan Li, Qianzhong Li, Luqiang Zhang, Yechen Qi, Hui Bai
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引用次数: 0

摘要

肺腺癌是最致命的肿瘤之一。研究表明,n6 -甲基腺苷RNA甲基化调节剂作为一种动态的化学修饰,影响肺腺癌的发生发展。为了探讨m6A调节因子在肺腺癌中的突变与表达水平的关系,我们研究了38个m6A调节因子的突变与表达水平。我们发现m6A调控因子的突变不影响表达水平的变化,并鉴定出19个差异表达基因。根据19个差异表达的m6a调控基因的表达水平,将所有肿瘤样本分为两个亚型。生存分析显示两种亚型的生存有显著差异。为了探讨两种亚型中免疫细胞浸润与存活的关系,我们计算了两种亚型中23个免疫细胞的浸润情况,我们发现免疫细胞浸润高的亚型生存率更好。我们发现肿瘤纯度低、基质和免疫评分高的亚型生存率更高。取两种异构体中差异表达基因与免疫基因的交集,计算交集免疫基因与差异表达的m6a调控基因之间的Pearson相关系数,鉴定m6a相关免疫基因。最后,利用从m6A相关免疫基因中筛选的预后基因,建立了与m6A相关并与免疫相关的预后模型。对该模型的预测能力进行了评价,并取得了较好的预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.

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