Table_2_Immune-Stromal Score Signature: Novel Prognostic Tool of the Tumor Microenvironment in Lung Adenocarcinoma.XLS

Background: 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.
Methods: 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.
Results: 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 (CLEC17A, INHA, and XIRP1) 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 CLEC17A, INHA, and XIRP1 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.
Conclusions: 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.

背景：肿瘤微环境（tumor microenvironment, TME）中的免疫与基质细胞对肺腺癌的预后具有显著影响，但目前与TME相关的免疫预后特征仍不明晰。本研究旨在构建肺腺癌TME的新型免疫预后模型。
方法：首先，基于癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库，采用ESTIMATE算法计算肺腺癌患者的免疫评分与基质评分。分析免疫/基质评分高低两组患者间的差异表达免疫相关基因（immune-related genes, IRGs），并通过单因素Cox回归分析，筛选出与肺腺癌患者总生存期（overall survival, OS）显著相关的IRGs。进一步采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归分析，构建TME相关的免疫预后特征。通过基因集富集分析解析上述免疫预后特征背后的分子机制。最后，在完整的TCGA数据集内进一步分析核心IRGs的功能，以阐明其潜在的预后调控机制。
结果：最终共筛选得到702个交集差异表达IRGs，其中上调基因589个，下调基因113个。单因素Cox回归分析显示，训练队列中共有58个差异表达IRGs与患者预后显著相关；通过LASSO回归分析进一步筛选出其中3个IRGs（CLEC17A、INHA及XIRP1）。基于这3个IRGs构建了稳健的预后模型。进一步的功能富集分析显示，高风险评分组主要富集于代谢通路，而低风险评分组则主要富集于免疫调控与免疫细胞激活相关通路。最后，核心IRGs CLEC17A、INHA及XIRP1被确定为肺腺癌的新型预后生物标志物。这些核心基因在肺腺癌中具有不同的突变频率与突变类型，并参与不同的信号通路。更为重要的是，这些核心基因与CD4阳性T细胞、CD8阳性T细胞、巨噬细胞、B细胞及中性粒细胞的浸润程度显著相关。
结论：本研究构建的新型稳健TME相关免疫预后特征可有效预测肺腺癌患者的预后。未来仍需开展进一步研究，以阐明这些核心IRGs在TME中的调控机制，并开发全新的治疗策略。