Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.
Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.
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肿瘤微环境的免疫特征和肺腺癌四基因特征的鉴定。
DOI：
10.18632/aging.202269
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发表时间：
2020-12-09
期刊：
影响因子：
--
通讯作者：
He J
中科院分区：
文献类型：
--
作者：
Fan T;Zhu M;Wang L;Liu Y;Tian H;Zheng Y;Tan F;Sun N;Li C;He J
肿瘤微环境中免疫细胞的组成和相对丰度是影响肺腺癌（LUADs）进展和免疫治疗效果的关键因素。利用来自the cancer Genome Atlas （TCGA）项目的癌症基因表达数据集，我们对基质细胞和免疫细胞进行评分，用CIBERSORT和ESTMATE预测肿瘤纯度。通过差异表达分析，鉴定出374个高分组和低分组基因，并利用这些基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。对差异表达基因（DEGs）进行蛋白-蛋白相互作用（PPI）和Cox回归分析，鉴定4个关键肿瘤微环境（TME）相关基因（CCR2、CCR4、P2RY12和P2RY13）。在不同年龄、性别、不同TNM分期的LUAD患者中，这4种deg的表达水平存在显著差异。我们发现静息记忆CD4+ T细胞、记忆B细胞和M0巨噬细胞向TME的浸润受这四种deg的共同调节。这4个基因与LUAD的预后密切相关，并影响免疫细胞向TME的浸润，对LUAD具有预测预后价值。
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cancer Genome Atlas (TCGA) program, we scored stromal and immune cells for tumor purity prediction by CIBERSORT and ESTMATE. Differential expression analysis was employed to identify 374 genes between the high-score group and the low-score group, which were utilized to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein-protein interaction (PPI) and Cox regression analysis were performed on the differentially expressed genes (DEGs) to identify four key tumor microenvironment (TME) -related genes (CCR2, CCR4, P2RY12, and P2RY13). The expression levels of the four DEGs differed significantly among LUAD patients of different ages, genders, and TNM stages. We found that the infiltration of resting memory CD4+ T cells, memory B cells, and M0 macrophages into the TME was co-regulated by these four DEGs. These four genes were closely related to the prognosis of LUAD and affected the infiltration of immune cells into the TME, which had predictive prognostic value in LUAD.
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影响因子：
7.4
作者：
Li, Lifeng;Peng, Mengle;Zhao, Jie
通讯作者：
Zhao, Jie
DOI：
10.1073/pnas.1913707117
发表时间：
2020-05-05
影响因子：
11.1
作者：
Puschel, Franziska;Favaro, Francesca;Munoz-Pinedo, Cristina
通讯作者：
Munoz-Pinedo, Cristina
单核细胞源性巨噬细胞促进乳腺癌骨转移生长
DOI：
10.1084/jem.20191820
发表时间：
2020-11-02
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Ma RY;Zhang H;Li XF;Zhang CB;Selli C;Tagliavini G;Lam AD;Prost S;Sims AH;Hu HY;Ying T;Wang Z;Ye Z;Pollard JW;Qian BZ
通讯作者：
Qian BZ
影响因子：
6.5
作者：
Forsthuber, Agnes L.;Lipp, Katharina;Loewe, Robert
通讯作者：
Loewe, Robert
AC1MMYR2对癌相关成纤维细胞进行重编程可阻止肿瘤转移并提高化疗效果
影响因子：
9.7
作者：
Ren, Yu;Zhou, Xuan;Kang, Chun-sheng
通讯作者：
Kang, Chun-sheng