Yongmei Wang, Guimin Zhang* and Ruixian Wang Pages 1 - 11 ( 11 )
Background: This study aims to explore the prognostic values of CT83 and CT83- related genes in lung adenocarcinoma (LUAD).
Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients.
Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83- related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, which could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the risk score, which were also differentially expressed between the LUAD samples with high and low risk scores, suggesting that the poor prognosis of LUAD patients with high risk score might be due to the immunosuppressive microenvironments.
Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.
Lung adenocarcinoma, CT83, risk score, prognostic, biomarker, overall survival.
Department of pathology, Tianjin Haihe Hospital, Tianjin Respiratory Research Institute, Tianjin, 300350, Department of pathology, Tianjin Jinnan Hospital, Tianjin, 300350, Department of pathology, Tianjin Jinnan Hospital, Tianjin, 300350