Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient’s outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients’ outcomes for LUAD.

Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan–Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan–Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.

An eight-gene signature was built, and it was examined by Kaplan–Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.

Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients’ prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

肺癌已成为最常见的癌症类型，并导致最多的癌症死亡。肺腺癌（LUAD）是肺癌的主要类型之一。越来越多的证据表明，肿瘤微环境与肿瘤进展和患者预后相关。本研究旨在建立基于肿瘤微环境的基因特征，可以预测患者的 LUAD 结果。

从TCGA门户下载的数据集TCGA-LUAD作为训练队列，从GEO数据库获得的数据集GSE72094作为验证队列。在训练队列中，应用ESTIMATE算法寻找肿瘤微环境中的交叉差异表达基因（DEGs）。对交叉DEGs进行Kaplan-Meier分析和单变量Cox回归模型，初步筛选预后基因。此外，实施 LASSO Cox 回归模型以构建多基因特征，然后通过 Kaplan-Meier、Cox 和接受者操作特征曲线 (ROC) 分析在验证队列中进行验证。此外，通过Spearman检验评估肿瘤突变负荷（TMB）与风险评分之间的相关性。

建立了一个八基因特征，并通过 Kaplan-Meier 分析对其进行了检查，揭示了显着的总体生存差异。通过 Cox 回归分析进一步证明了八基因特征与其他临床病理参数无关。此外，ROC 分析表明，该特征对 LUAD 预后具有更好的预测能力。八基因特征与 TMB 相关。此外，GSEA 和免疫浸润分析表明，确切的通路与八基因特征的特征相关，并确定了肥大细胞静息和 B 细胞在八基因特征的预后中的重要作用。

识别八个基因特征（INSL4、SCN7A、STAP1、P2RX1、IKZF3、MS4A1、KLRB1 和 ACSM5）可以准确识别患者的预后，并与静息的肥大细胞和幼稚的 B 细胞密切相互作用，这可能有助于洞察个性化预后LUAD 患者的预测和新疗法。