Background: The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. Methods: For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. objective: The current article is intended to construct a gene signature from LUAD ICI for predicting the patients’ outcome. Results: Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. Conclusions: We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

中文翻译：

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基于免疫细胞浸润景观的新基因特征可预测肺腺癌患者的预后

背景：肿瘤微环境（TME）是由肿瘤产生的，并以肿瘤诱导的相互作用为主。肺腺癌 (LUAD) 患者的长期生存很大程度上受到 TME 中免疫细胞浸润的影响。当前的文章打算构建 LUAD ICI 的基因特征来预测患者的结果。方法：在研究的初始阶段，选择TCGA-LUAD数据集作为数据集选择的训练组。我们在基因表达综合（GEO）数据库中找到了两个名为 GSE72094 和 GSE68465 的数据集用于模型验证。使用 ICI 配置文件对训练队列患者进行无监督聚类。我们采用 Kaplan-Meier 估计量和单变量 Cox 比例风险模型来识别免疫细胞浸润 (ICI) 簇中的预后差异表达基因。然后使用这些预后基因开发 LASSO Cox 模型，该模型可生成预后基因特征。使用 Kaplan-Meier 估计、Cox 和 ROC 分析进行验证。我们分析了我们的特征和重要的免疫相关特征。最后，我们对发现的基因特征进行了基因集富集分析（GSEA）和免疫浸润分析，以进一步检查功能机制和免疫细胞相互作用。目标：本文旨在构建 LUAD ICI 的基因特征来预测患者的结果。结果：我们的研究发现基于数据的 16 个基因特征（EREG、HPGDS、TSPAN32、ACSM5、SFTPD、SCN7A、CCR2、S100P、KLK12、MS4A1、INHA、HOXB9、CYP4B1、SPOCK1、STAP1 和 ACAP1）具有预测意义来自训练队列。该预后特征得到了 Kaplan-Meier、Cox 比例风险和 ROC 曲线的认证。 11/15 免疫相关特征与我们的特征相关。 GSEA 结果表明我们的基因特征与免疫相关途径密切相关。根据免疫浸润分析结果，可以推断特征的预后意义的很大一部分可归因于静息肥大细胞。结论：我们使用生物信息学确定了一个新的、强大的十六基因特征。我们还发现该特征的预后能力与 LUAD 患者静息肥大细胞浸润密切相关。