Qi Xiaoguang, Qi Chunyan, Qin Boyu, Kang Xindan, Hu Yi, Han Weidong
Department of Oncology, Chinese PLA General Hospital, Beijing, China.
Department of Health Management, Chinese PLA General Hospital, Beijing, China.
Front Oncol. 2020 Sep 23;10:541330. doi: 10.3389/fonc.2020.541330. eCollection 2020.
Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (, and ) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs , and were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.
肿瘤微环境（TME）中的免疫细胞和基质细胞对肺腺癌的预后有显著影响；然而，与TME相关的免疫预后特征尚不清楚。本研究的目的是建立一种新的肺腺癌TME免疫预后模型。首先，根据癌症基因组图谱（TCGA）数据库，使用ESTIMATE算法确定肺腺癌患者的免疫和基质评分。分析高、低免疫/基质评分组之间差异表达的免疫相关基因（IRG），并进行单变量Cox回归分析，以确定与肺腺癌患者总生存期（OS）显著相关的IRG。此外, 进行最小绝对收缩和选择算子（LASSO）回归分析以生成与TME相关的免疫预后特征。进行基因集富集分析以分析这些免疫预后特征的潜在机制。最后，在综合的TCGA数据集中进一步分析核心IRG的功能，以描述潜在的预后机制。总共筛选出702个相交的差异表达IRG（589个上调和113个下调）。单变量Cox回归分析显示，在训练队列中有58个显著差异表达的IRG与患者预后相关，其中通过LASSO回归分析确定了3个IRG（、和）。基于这三个IRG特征建立了一个强大的预后模型。此外，高风险评分组的功能富集分析主要基于代谢途径，而低风险评分组的分析主要基于免疫调节和免疫细胞激活。最后，核心IRG、和被认为是肺腺癌新的预后生物标志物。这些核心基因在肺腺癌中有不同的突变频率和形式，并参与不同的信号通路。更重要的是，这些核心基因与CD4+T细胞、CD8+T细胞、巨噬细胞、B细胞和中性粒细胞的浸润显著相关。强大的新型TME相关免疫预后特征有效地预测了肺腺癌患者的预后。需要进一步研究以进一步阐明这些核心IRG在TME中的调控机制，并制定新的治疗策略。
