Ouyang Wenhao, Jiang Yupeng, Bu Shiyi, Tang Tiantian, Huang Linjie, Chen Ming, Tan Yujie, Ou Qiyun, Mao Luhui, Mai Yingjie, Yao Herui, Yu Yunfang, Lin Xiaoling
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Department of Pulmonary and Critical Care Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Front Cell Dev Biol. 2022 Jan 24;9:758777. doi: 10.3389/fcell.2021.758777. eCollection 2021.
Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures. In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to mA, mC, mA and mG modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity. Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous mA, mC, mA and mG modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores. Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.
肺腺癌(LUAD)是非小细胞肺癌(NSCLC)最常见的亚型,预后较差。然而,目前基于分期的临床方法在生存预测和决策方面存在不足。本研究旨在建立一种基于缺氧、免疫和上皮-间质转化(EMT)基因特征评估LUAD风险的新型模型。在本研究中,我们将来自TCGA-LUAD的数据用于训练队列,将GSE68465和GSE72094的数据用于验证队列。免疫治疗数据集GSE135222、GSE126044和IMvigor210取自先前的一项研究。我们使用生物信息学和机器学习算法,建立了一个基于缺氧、免疫和EMT基因特征的风险模型,然后将患者分为高风险组和低风险组。我们分析了两组之间富集通路的差异,随后研究了风险评分是否与干性评分、与mA、mC、mA和mG修饰相关的基因、免疫微环境、免疫治疗反应以及多种抗癌药物敏感性相关。高风险组和低风险组的总生存期存在显著差异(HR = 4.26)。预测1年、3年和5年生存率的AUC分别为0.763、0.766和0.728。在GSE68465数据集中,HR为2.03,而预测1年、3年和5年生存率的AUC分别为0.69、0.651和0.618。GSE72094数据集中的相应值分别为HR 2.36和AUC 0.653、0.662和0.749。风险评分模型可以独立预测LUAD患者的总生存期,并且与干性评分以及众多与mA、mC、mA和mG修饰相关的基因高度相关。此外,风险模型与多种免疫微环境特征显著相关。在GSE135222数据集中,HR为4.26,AUC为0.702。对GSE126044和IMvigor210队列的评估表明,低风险评分的患者可能适合接受PD-1/PD-LI抑制剂治疗,而高风险评分的患者可能适合接受各种药物的抗癌治疗。我们基于缺氧、免疫和EMT基因特征开发的新型风险模型有助于预测LUAD患者的临床预后并指导治疗。