Zhuge Jinke, Wang Xiuqing, Li Jingtai, Wang Tongyuan, Wang Hongkang, Yang Mingxing, Dong Wen, Gao Yong
Department of Respiratory Medicine, Hainan Cancer Hospital, Haikou, China.
Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Front Genet. 2022 Nov 25;13:968376. doi: 10.3389/fgene.2022.968376. eCollection 2022.
Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.
先前的研究表明，肺腺癌（LUAD）患者将从表皮生长因子受体酪氨酸激酶抑制剂（EGFR-TKI）中显著获益。然而，许多LUAD患者会对EGFR-TKI产生耐药性。因此，我们的研究旨在建立预测EGFR-TKI耐药性和LUAD预后的模型。使用两个基因表达综合数据库（GEO）数据集（GSE31625和GSE34228）作为发现数据集，以找出EGFR-TKI耐药LUAD谱中常见的差异表达基因（DEG）。在癌症基因组图谱（TCGA）数据库中研究这些常见DEG与LUAD预后的关联。此外，我们通过LASSO分析构建了用于LUAD预后预测的风险评分。使用独立数据集（GSE37745）计算预测LUAD预后的风险评分的性能。在训练数据集中训练了由风险评分基因组成的随机森林模型，并在内部测试数据集和外部测试数据集（GSE122005、GSE80344和GSE123066）中验证了区分敏感和EGFR-TKI耐药样本的诊断能力。从发现数据集中，鉴定出267个常见的上调基因和374个常见的下调基因。在这些常见的DEG中，有59个基因与预后呈负相关，而21个基因与预后呈正相关。选择八个基因（ABCC2、ARL2BP、DKK1、FUT1、LRFN4、PYGL、SMNDC1和SNAI2）构建风险评分特征。在发现数据集和独立验证数据集中，风险评分较高的LUAD患者预后较差。基于风险评分的列线图在预后预测中表现良好，C指数为0.77。ABCC2、ARL2BP、DKK1、LRFN4、PYGL、SMNDC1和SNAI2的表达水平与EGFR-TKI的耐药性呈正相关。然而，FUT1的表达水平与EGFR-TKI反应性呈正相关。随机森林模型在内部和外部测试数据集中出色地区分了敏感和耐药的EGFR-TKI样本，曲线下预测面积（AUC）分别为0.973和0.817。我们的研究揭示了八个与EGFR-TKI耐药性相关的基因，并为LUAD患者的EGFR-TKI耐药性和预后预测提供了模型。
