Suppr超能文献

整合单细胞和批量RNA测序,基于肺腺癌中M2巨噬细胞的分化轨迹建立预测特征。

Integration of single-cell and bulk RNA-seq to establish a predictive signature based on the differentiation trajectory of M2 macrophages in lung adenocarcinoma.

作者信息

Chen Zhike, Yang Jian, Li Yu, Zeng Weibiao, Bai Yiling, Ding Cheng, Xu Chun, Li Chang, Chen Jun, Ju Sheng, Tang Lijuan, Zhao Jun

机构信息

Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.

Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.

出版信息

Front Genet. 2022 Sep 12;13:1010440. doi: 10.3389/fgene.2022.1010440. eCollection 2022.

Abstract

Tumor-associated macrophages as important members of the tumor microenvironment, are highly plastic and heterogeneous. TAMs can be classified into two preliminary subtypes: M1 and M2 macrophages. M2 macrophages are significantly associated with the progression of lung adenocarcinoma. However, no study has investigated the heterogeneity among M2 macrophages and their differentiation-related genes at the single-cell level to guide the clinical treatment of lung adenocarcinoma. Using the available annotation information from the Tumor Immune Single-cell Hub database, we clustered and annotated 12 lung adenocarcinoma samples using the R package 'Seurat'. Subsequently, we extracted M2 macrophages for secondary clustering analysis and performed cell trajectory analysis using the R package 'monocle2'. Based on heterogeneous genes associated with the differentiation trajectory of M2 macrophages, we established a prognostic lung adenocarcinoma model using Lasso-Cox and multivariate stepwise regression. In addition, we also performed immunotherapy and chemotherapy predictions. M2 macrophages exhibit heterogeneity among themselves. M2 macrophages in different differentiation states showed significant differences in pathway activation and immune cell communication. Prognostic signature based on heterogeneous genes can be used to classify the prognostic status and abundance of immune cell infiltration in lung adenocarcinoma patients. In addition, the calculation of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the validation of the GSE126044 database indicated that lung adenocarcinoma patients with high-risk scores had poorer treatment outcomes when receiving immune checkpoint inhibitors treatment. Based on scRNA-seq and Bulk-seq data, we identified M2 macrophage-associated prognostic signature with a potential clinical utility to improve precision therapy.

摘要

肿瘤相关巨噬细胞作为肿瘤微环境的重要成员,具有高度可塑性和异质性。肿瘤相关巨噬细胞可初步分为两种亚型:M1和M2巨噬细胞。M2巨噬细胞与肺腺癌的进展显著相关。然而,尚无研究在单细胞水平上探究M2巨噬细胞之间的异质性及其分化相关基因,以指导肺腺癌的临床治疗。利用肿瘤免疫单细胞中心数据库中的可用注释信息,我们使用R包“Seurat”对12个肺腺癌样本进行聚类和注释。随后,我们提取M2巨噬细胞进行二次聚类分析,并使用R包“monocle2”进行细胞轨迹分析。基于与M2巨噬细胞分化轨迹相关的异质性基因,我们使用Lasso-Cox和多元逐步回归建立了肺腺癌预后模型。此外,我们还进行了免疫治疗和化疗预测。M2巨噬细胞自身表现出异质性。处于不同分化状态的M2巨噬细胞在通路激活和免疫细胞通讯方面存在显著差异。基于异质性基因的预后特征可用于对肺腺癌患者的预后状态和免疫细胞浸润丰度进行分类。此外,肿瘤免疫功能障碍和排除(TIDE)算法的计算以及GSE126044数据库的验证表明,高危评分的肺腺癌患者在接受免疫检查点抑制剂治疗时治疗效果较差。基于单细胞RNA测序(scRNA-seq)和批量RNA测序(Bulk-seq)数据,我们鉴定出具有潜在临床应用价值的M2巨噬细胞相关预后特征,以改善精准治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44a0/9510778/e899813de72a/fgene-13-1010440-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验