Yongjian Zhang1,1, Qiang Fan2,1, Yingying Guo1, Koujun Zhu1. 1. Department of Cardiothoracic Surgery, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China. 2. Department of Oncology Radiology, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.
Abstract
BACKGROUND: Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD. OBJECTIVE: We performed this study to construct a model to predict risk of recurrence in LUAD. METHODS: 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. RESULTS: 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. CONCLUSION: We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.
BACKGROUND: Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD. OBJECTIVE: We performed this study to construct a model to predict risk of recurrence in LUAD. METHODS: 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. RESULTS: 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. CONCLUSION: We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.
Entities:
Keywords:
8-gene signature; The Cancer Genome Atlas; lung adenocarcinoma; prognostic model; recurrence