Yao Yan, Zhang Tingting, Qi Lingyu, Liu Ruijuan, Liu Gongxi, Li Jie, Sun Changgang
Clinical Medical Colleges, Weifang Medical University, Weifang, Shandong Province, People's Republic of China.
College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People's Republic of China.
Pharmgenomics Pers Med. 2021 Jan 8;14:15-26. doi: 10.2147/PGPM.S283414. eCollection 2021.
Tumor microenvironment (TME) cells constitute a vital element of tumor tissues. Increasing evidence has shown that immune response in the microenvironment plays an active role in tumor invasion, metastasis, and recurrence, and is an important factor affecting tumor prognosis. Our study aimed to identify the gene signatures in lung adenocarcinoma (LUAD) microenvironment for prognosis and immunotherapy.
In this study, we evaluated, for the first time, the stromal and immune scores of 594 patients from The Cancer Genome Atlas (TCGA) database with LUAD using the ESTIMATE algorithm. Three hundred and sixty-seven dysregulated immune-related genes were identified. Then, we performed functional enrichment analysis of these genes, and found the best gene model and construct the signature through univariate, Lasso and multivariate COX regression analysis. To assess the independently prognostic ability of the signature, the Kaplan-Meier survival analysis and Cox's proportional hazards model were performed.
Functional enrichment analysis and protein-protein interaction networks showed that the immune-related genes mainly played a role in immune response, activation/proliferation of immune-related cells, and chemokine activity. A prognostic model involving 6 genes was constructed and the signature was identified as an independent prognostic factor and significantly associated with the overall survival (OS) of LUAD. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the 6 genes signature in predicting the 3-year survival rate was 0.708. Finally, four genes (FOXN4, KLHL4, FAM83F and CCR2) can be used as candidate prognostic biomarkers for LUAD.
Our findings will help evaluate the prognosis of LUAD and provide new ideas for exploring the potential relationship between TME and LUAD treatment and prognosis.
肿瘤微环境（TME）细胞是肿瘤组织的重要组成部分。越来越多的证据表明，微环境中的免疫反应在肿瘤侵袭、转移和复发中发挥着积极作用，是影响肿瘤预后的重要因素。我们的研究旨在确定肺腺癌（LUAD）微环境中用于预后和免疫治疗的基因特征。
在本研究中，我们首次使用ESTIMATE算法评估了来自癌症基因组图谱（TCGA）数据库的594例LUAD患者的基质和免疫评分。鉴定出367个失调的免疫相关基因。然后，我们对这些基因进行了功能富集分析，通过单变量、Lasso和多变量COX回归分析找到最佳基因模型并构建特征。为了评估该特征的独立预后能力，进行了Kaplan-Meier生存分析和Cox比例风险模型。
功能富集分析和蛋白质-蛋白质相互作用网络表明，免疫相关基因主要在免疫反应、免疫相关细胞的激活/增殖和趋化因子活性中发挥作用。构建了一个包含6个基因的预后模型，该特征被确定为独立的预后因素，与LUAD的总生存期（OS）显著相关。6基因特征预测3年生存率的受试者操作特征曲线（ROC曲线）的曲线下面积（AUC）为0.708。最后，四个基因（FOXN4、KLHL4、FAM83F和CCR2）可作为LUAD的候选预后生物标志物。
我们的研究结果将有助于评估LUAD的预后，并为探索TME与LUAD治疗和预后之间的潜在关系提供新思路。
