研究动态
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m1A调控基因签名与肿瘤微环境中特定的免疫细胞组成相关,并预测了肾透明细胞癌的生存率。

m1A Regulatory gene signatures are associated with certain immune cell compositions of the tumor microenvironment and predict survival in kidney renal clear cell carcinoma.

发表日期:2023 Sep 07
作者: Linjun Zhou, Weidong Zhou, Yuan Li, Ruifang Hua
来源: Immunity & Ageing

摘要:

RNA的腺嘌呤N1甲基化(m1A)作为一种转录后修饰方式,在癌症的发展过程中已被证实具有重要作用。本研究旨在分析m1A调控因子的遗传改变和预后意义在肾透明细胞癌(KIRC)中的作用。我们从癌症基因组图谱(TCGA)和基因全表达数据库(GEO)获取了558例KIRC患者的基因组和临床病理特征。分析了十个m1A调控因子的基因表达变化,并使用Cox回归方法进行了生存分析。我们还通过对十个与m1A相关的调控因子的综合分析,确定了三个患者簇,这些患者簇根据其不同的m1A改变模式,与KIRC中的总体生存率(OS)、无疾病生存率(DFS)和肿瘤微环境(TME)免疫细胞浸润相关。研究结果表明,m1A改变模式对于决定TME的复杂性及其免疫细胞组成具有关键作用。此外,不同的m1A表达模式与KIRC患者的DFS和OS率显著相关。总之,确定的m1A RNA修饰模式为根据TME免疫细胞浸润对KIRC患者进行分类提供了一种潜在有效的方法,从而为这些患者的个体化和成功治疗策略的开发提供了可能。© 2023. BioMed Central Ltd., part of Springer Nature.
Adenosine N1 methylation (m1A) of RNA, a type of post-transcriptional modification, has been shown to play a significant role in the progression of cancer. The objective of the current research was to analyze the genetic alteration and prognostic significance of m1A regulators in kidney renal clear cell carcinoma (KIRC). Genomic and clinicopathological characteristics were obtained from 558 KIRC patients in the Cancer Genome Atlas (TCGA) and Gene Omnibus Expression (GEO) databases. Alterations in the gene expression of ten m1A-regulators were analyzed and survival analysis was performed using the Cox regression method. We also identified three clusters of patients based on their distinct m1A alteration patterns, using integrated analysis of the ten m1A-related regulators, which were significantly related to overall survival (OS), disease-free survival (DFS) and tumor microenvironment (TME) immune cell infiltration cells in KIRC. Our findings showed that m1A alteration patterns have critical roles in determining TME complexity and its immune cell composition. Furthermore, different m1A expression patterns were significantly associated with DFS and OS rates in KIRC patients. In conclusion, the identified m1A RNA modification patterns offer a potentially effective way to classify KIRC patients based on their TME immune cell infiltration, enabling the development of more personalized and successful treatment strategies for these patients.© 2023. BioMed Central Ltd., part of Springer Nature.