Development a m6A regulators characterized by the immune cell infiltration in stomach adenocarcinoma for predicting the prognosis and immunotherapy response
Aging(2023)
摘要
N6-Methyladenosine (m(6)A) has attracted growing interest among scholars as an important regulator of mRNA expression. Although the significant role of m(6)A in multiple biological processes (like proliferation and growth of cancers) has been comprehensively described, an analysis of its possible role in stomach adenocarcinoma (STAD) of tumor immune microenvironment (TIME) remains lacking. The data for RNA expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) were downloaded from The Cancer Genome Atlas (TCGA). Subsequently, 23 m(6)A regulators were curated, with patients being clustered into three m(6)A subtypes and m(6)A-related gene subtypes. Furthermore, they were compared based on overall survival (OS). This study also evaluates the association between m(6)A regulators and immune as well as response to the treatment. According to the TCGA-STAD cohort, three m(6)A clusters conformed to three phenotypes, immuneinflamed, immune-dessert, and immune-excluded, respectively. Patients who displayed lower m(6)A scores presented better overall survival outcomes. The GEO cohort demonstrated that those with a low m(6)A score had obvious general survival benefits and clinical advantages. Low m(6)A scores can carry the enhanced neoantigen loads, triggering an immune response. Meanwhile, three anti-PD-1 cohorts have confirmed the value of predicting survival outcomes. The results of this study indicate that m(6)A regulators are associated with TIME, and the m(6)A score is an efficient prognostic biomarker and predictive indicator for immunotherapy and chemotherapeutics. Moreover, comprehensive evaluations of m(6)A regulators in tumors will broaden our comprehension of TIME, efficiently guiding enhancing explorations on immunotherapy and chemotherapy strategies for STAD.
更多查看译文
关键词
STAD,m(6)A regulator,TIME,m(6)A score,therapy
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要