Li Rui, Liu Xiao, Zhou Xi-Jia, Chen Xiao, Li Jian-Ping, Yin Yun-Hong, Qu Yi-Qing
Department of Pulmonary and Critical Care Medicine, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China.
Department of Respiratory Medicine, Tai'an City Central Hospital, Tai'an, China.
Front Oncol. 2020 Sep 2;10:1588. doi: 10.3389/fonc.2020.01588. eCollection 2020.
Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.
免疫相关基因（IRGs）在肿瘤免疫微环境（IME）中发挥着重要作用。本研究旨在基于Cox预测模型发现差异表达的免疫相关基因（DEIRGs），通过生物信息学分析预测肺鳞状细胞癌（LUSC）的生存情况。首先，使用limma R包基于癌症基因组图谱（TCGA）获取差异表达基因（DEGs），从ImmPort数据库获得DEIRGs，而差异表达转录因子（DETFs）则从Cistrome数据库获取。此后，构建了一个转录因子介导的IRGs网络，以在分子水平上识别LUSC中那些DEIRGs的候选机制。此外，进行了基因本体（GO）以及京都基因与基因组百科全书（KEGG）通路富集分析，以探索DEIRGs的功能富集情况。此外，进行单变量和多变量Cox回归分析以建立DEIRGs生物标志物的预测模型。此外，通过免疫细胞相关性分析进一步探讨了预后模型与免疫细胞之间的关系。总共从LUSC组织和相邻非癌组织中获得了3599个DEGs、223个DEIRGs和46个DETFs。根据多变量Cox回归分析，检索到10个DEIRGs（包括CALCB、GCGR、HTR3A、AMH、VGF、SEMA3B、NRTN、ENG、ACVRL1和NR4A1）以建立LUSC的预后模型。免疫细胞浸润分析表明，树突状细胞和中性粒细胞与IRGs呈正相关，这可能在LUSC的IME中发挥重要作用。我们的研究确定了一种基于IRGs的预后模型，然后用于预测LUSC的预后并分析免疫细胞浸润。这可能为探索LUSC的IME中潜在的IRGs提供新的见解。
