Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode - Jiawei, Zhou; Min, Mu; Yingru, Xing; Xin, Zhang; Danting, Li; Yafeng, Liu; Jun, Xie; Wangfa, Hu; Lijun, Zhang; Jing, Wu; Dong, Hu (Frontiers in Molecular Biosciences, 2020)
Background: The development of human tumors is associated with the abnormal expression of various functional genes, and tumor based massive database needs to be deeply mined. Based on multigene prediction model is urgent to access prognosis of patients. Materials and Methods: We selected three RNA expression profiles (GSE32863, GSE10072, GSE43458) from the LUAD database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from TCGA database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess prediction model. Multivariate Cox analysis was used to identify independent predictor. TCGA PAAD dataset were used to establish nomogram model. Results: We found 98 significantly prognosis-related genes using Kaplan-Meier (K-M) and COX analysis, among which 6 genes were found to be the DEGs in GEO. Using Multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these 6 genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p=0.013) and had an applicable reliability (AUC=0.665). By combining Risk score and various clinical features, the Nomogram model was constructed, which had been proved to have a high consistency for the prediction of 3-year and 5-year survival rate (Concordance=0.751) and a high accuracy tested by ROC (AUC=0.71；AUC=0.708). Conclusion: We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for prognosis evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.