Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.
Department of Oncology Radiology, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.
Cancer Biomark. 2020;28(4):447-457. doi: 10.3233/CBM-190329.
Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD.
We performed this study to construct a model to predict risk of recurrence in LUAD.
RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213). Differentially expressed genes (DEGs) between recurrent and non-recurrent tumors in the training set were identified. Recurrence-associated DEGs were selected using multivariate Cox regression analysis. The recurrence risk model that identifies patients at low and high risk for recurrence was constructed, followed by the validation of its performance in the validation set and a microarray dataset.
In total, 378 DEGs, including 20 recurrence-associated DEGs, were identified between the recurrent and non-recurrent tumors in the training set. The signatures of 8 genes (including AZGP1, INPP5J, MYBPH, SPIB, GUCA2A, HTR1B, SLC15A1 and TNFSF11) were used to construct the prognostic model to assess the risk of recurrence. This model indicated that patients with high risk scores had shorter recurrence-free survival time compared with patients with low risk scores. ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset. This prognostic model showed high predictive power and performance in predicting recurrence in LUAD.
We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.
复发显著影响肺腺癌（LUAD）患者的生存。然而，预测 LUAD 复发风险的基因特征较少。
本研究旨在构建预测 LUAD 复发风险的模型。
从癌症基因组图谱（TCGA）中下载了 426 例 LUAD 患者的 RNA-seq 数据，并将其随机分配到训练集（n=213）和验证集（n=213）中。在训练集中识别复发和非复发肿瘤之间的差异表达基因（DEG）。使用多变量 Cox 回归分析选择与复发相关的 DEG。构建识别患者低风险和高风险复发的复发风险模型，然后在验证集和微阵列数据集上验证其性能。
在训练集中，共鉴定出 378 个 DEG，包括 20 个与复发相关的 DEG，存在于复发和非复发肿瘤之间。使用 8 个基因（包括 AZGP1、INPP5J、MYBPH、SPIB、GUCA2A、HTR1B、SLC15A1 和 TNFSF11）的特征构建了预后模型，以评估复发风险。该模型表明，高风险评分的患者无复发生存时间短于低风险评分的患者。该模型对 TCGA 队列（训练集和验证集）和 GSE50081 数据集 LUAD 复发的 ROC 曲线分析显示，其具有较高的预测准确性（AUC＞0.8）。该预后模型在预测 LUAD 复发方面具有较高的预测能力和性能。
我们得出结论，该模型可能对评估 LUAD 复发风险具有重要价值。
