Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Immunogenetics. 2020 Dec;72(9-10):455-465. doi: 10.1007/s00251-020-01189-z. Epub 2020 Nov 13.
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。本研究旨在探讨肺腺癌（LUAD）潜在的预后 TME 相关生物标志物。从 TCGA 和 GEO 数据库中提取 RNA 测序谱和相应的临床参数，通过 ESTIMATE 算法计算基质和免疫评分。通过 LASSO 和随机森林算法分析基质和免疫评分组之间重叠的差异表达基因，并在本中心的病例中进行验证。使用 Cox 回归构建预后 8 基因特征。通过 CIBERSORT 算法评估 22 种造血细胞表型的浸润情况。我们发现，女性、老年患者和实性为主亚型的基质和免疫评分明显较高。早期 LUAD 患者的免疫评分明显较高。高基质或免疫评分意味着预后较好。随后，通过 LASSO 回归和随机森林算法鉴定出 8 个与 TME 相关的预后基因（ATAD5、CYP4F3、CYP4F12、ESPNL、FXYD2、GPX2、NLGN4Y 和 SERPINC1）。高 8 基因特征组的总生存率较差。此外，高特征组中 B 细胞幼稚、浆细胞、T 细胞滤泡辅助和巨噬细胞 M1 明显较多。然而，高特征组中 T 细胞 CD4 记忆静息、单核细胞和树突状细胞静息较少。肿瘤微环境的组成显著影响 LUAD 患者的预后。我们为探索预后 TME 相关生物标志物和免疫治疗提供了新的策略。
