The tumor microenvironment (TME) plays an essential role in the occurrence and progression of malignancy. The potential prognostic TME-related biomarkers of lung adenocarcinoma (LUAD) remained unclear, which were investigated in this research. The RNA-sequencing profiles and corresponding clinical parameters were extracted from TCGA and GEO databases, based on which the stromal and immune scores were calculated through the ESTIMATE algorithm. Overlapping differentially expressed genes between stromal and immune score group were analyzed by the LASSO and Random Forrest algorithms and validated in cases from our center. And a prognostic 8-gene signature was constructed using Cox regression. The infiltration of 22 hematopoietic cell phenotypes was assessed by the CIBERSORT algorithms. We found that female, elder patients, and solid predominant subtype had obviously higher stromal and immune scores. And patients with early stage LUAD received a prominently higher immune score. A high stromal or immune score meant a good prognosis. Subsequently, eight TME-related prognostic genes (ATAD5, CYP4F3, CYP4F12, ESPNL, FXYD2, GPX2, NLGN4Y, and SERPINC1) were identified by both LASSO regression and Radom Forest algorithms. High 8-gene signature group exhibited worse overall survival. Furthermore, B cell naïve, plasma cells, T cell follicular helper, and macrophages M1 were prominently more in high signature group. Nevertheless, fewer T cells CD4 memory resting, monocytes, and dendritic cell resting were identified in the high signature group. The composition of the tumor microenvironment significantly affected the prognosis of LUAD patients. We provided a new strategy for the exploration of prognostic TME-related biomarkers and immunotherapy.

肿瘤微环境（TME）在恶性肿瘤的发生和发展中起着至关重要的作用。肺腺癌（LUAD）潜在的预后与TME相关的生物标志物尚不清楚，对此进行了研究。从TCGA和GEO数据库中提取RNA测序图谱和相应的临床参数，然后通过ESTIMATE算法计算基质和免疫评分。通过LASSO和Random Forrest算法分析了基质和免疫评分组之间的重叠差异表达基因，并在我们中心进行了验证。并使用Cox回归构建了预后的8基因签名。通过CIBERSORT算法评估了22种造血细胞表型的浸润。我们发现女性老年患者 实体优势亚型的基质和免疫评分明显更高。早期LUAD患者的免疫评分显着提高。基质或免疫评分高意味着预后良好。随后，通过LASSO回归和Radom Forest算法确定了八个与TME相关的预后基因（ATAD5，CYP4F3，CYP4F12，ESPNL，FXYD2，GPX2，NLGN4Y和SERPINC1）。高8基因签名组表现出较差的总体生存率。此外，在高特征组中，幼稚的B细胞，浆细胞，T细胞的卵泡辅助物和巨噬细胞M1明显更多。然而，在高特征组中，发现较少的T细胞CD4记忆静止，单核细胞和树突状细胞静止。肿瘤微环境的组成显着影响LUAD患者的预后。