肿瘤细胞治疗研究
英文原题：Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma.
Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma.
建立m6A相关lncRNAs的预后模型可独立预测LUAD的总生存期，并可能有助于制定个体化免疫治疗策略。
N6-甲基腺苷（m6A）甲基化被认为可诱导肿瘤细胞增殖、迁移和凋亡。了解m6A相关lncRNAs在肺腺癌（LUAD）发展中的机制可能有助于预测预后。
识别了与肺癌相关的m6A相关lncRNAs，并与MeRIP-Seq数据集结合。利用共识聚类方法对LUAD患者进行分型，并使用Lasso Cox算法构建预后模型。使用cluster profiler包进行基因本体论和KEGG富集分析。使用CIBERSORT算法估计免疫浸润比例。通过rpart包构建决策树，通过rms包构建列线图。分析Connectivity Map数据库以探讨小分子药物对LUAD的治疗效果。此外，进行qPCR、集落形成和transwell实验以验证m6A相关lncRNAs的功能。
在LUAD中鉴定出19个m6A修饰的lncRNA。根据19个m6A相关lncRNA的表达，将LUAD患者分为两类。Cluster 2患者具有更好的抗原产生和表达，而cluster 1中naive B细胞、浆细胞和活化NK细胞较低。筛选出9个m6A相关lncRNA建立风险模型，用于评估LUAD患者的预后。高风险组具有更高的肿瘤突变负荷和更低的TIDE评分，并伴有更多的gamma delta T细胞和中性粒细胞。Nomogram显示，基于决策树模型分析的风险评分，该预后模型对LUAD患者具有较好的预测能力。Benzo(a)pyrene和neurodazine可能改善LUAD患者的预后。qRT-PCR结果证实了分析结果的可靠性。
BACKGROUND: N6-methyladenosine (m6A) methylation is considered to induce tumor cell proliferation, migration, and apoptosis. Understanding the mechanism of m6A-related lncRNAs in the development of lung adenocarcinoma (LUAD) may help predict prognosis. METHODS: m6A-related lncRNAs related to lung cancer were identified and combined with the MeRIP-Seq dataset. The consensus clustering method was utilized to divide LUAD patients, and prognostic model was constructed using the Lasso Cox algorithm. The cluster profiler package was used for gene ontology and KEGG enrichment. The proportion of immune infiltration was estimated using the CIBERSORT algorithm. The decision tree was constructed by the rpart package, and nomograms were built by the rms package. The Connectivity Map database was analyzed for the therapeutic effects of small molecule drugs for LUAD. In addition, qPCR, colony formation and transwell assays were performed to validate functions of m6A-associated lncRNAs. RESULTS: Nineteen m6A-modified lncRNAs in LUAD were identified. LUAD patients were divided into two categories based on the expression of 19 m6A-related lncRNAs. Cluster 2 patients had better antigen production and expression, while naive B cells, plasma cells, and activated NK cells were lower in cluster 1. Nine m6A-related lncRNAs were selected to establish a risk model for evaluating the prognosis of LUAD patients. The high-risk group had higher tumor mutational burden and lower TIDE scores with more gamma delta T cells and neutrophils. Nomograms showed that the prognostic model had predominant predictive ability for LUAD patients based on the risk score analyzed by the decision tree model. Benzo(a)pyrene and neurodazine might improve the prognosis of LUAD patients. The qRT-PCR results confirmed the reliability of the analytical results. CONCLUSION: The establishment of a prognostic model of m6A-related lncRNAs can independently predict overall survival in LUAD and may help to develop personalized immunotherapy strategies.
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