Key Common Genes in Obstructive Sleep Apnea and Lung Cancer are Associated with Prognosis of Lung Cancer Patients

Abstract

Wenjun Wang, Lirong He, Chao Ouyang, Chong Chen, Xiaofeng Xu, Xiaoqun Ye Department of Respiratory Diseases, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of ChinaCorrespondence: Xiaoqun YeDepartment of Respiratory Diseases, The Second Affiliated Hospital of Nanchang University, 1 Minde Road, Nanchang, 330006, Jiangxi, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: Obstructive sleep apnea (OSA) is associated with an increased risk of lung cancer. This study aimed to identify key common genes in OSA and lung cancer and explore their prognostic value in lung cancer.Materials and Methods: Transcriptome data of OSA and lung cancer were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database, respectively. Genes associated with OSA and lung cancer were screened by weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression algorithms were applied to identify key genes and construct the risk score model. Receiver operating characteristic (ROC) curves and a nomogram were performed to evaluate the prognostic value of the risk score. The screened key genes and their roles in prognosis were validated by GEO (GSE30219) analysis.Results: A total of 104 common genes were screened in OSA and lung cancer by WGCNA. Modulator of apoptosis 1 (MOAP1), chromobox 7 (CBX7), platelet-derived growth factor subunit B (PDGFB), and mitogen-activated protein kinase 3 (MAP2K3) were identified as key genes by univariate and then multivariate Cox regression analyses. The risk score model was constructed on the basis of four gene signatures. ROC curves and the nomogram showed that the risk score had a high accuracy in predicting the survival of patients with lung cancer. In addition, the result of multivariate Cox regression analysis indicated that the risk score was an independent prognostic factor in lung cancer.Conclusion: This study constructed a unique model for predicting the prognosis of lung cancer patients on the basis of four genes common to OSA and lung cancer. These genes may also serve as candidate genes to improve our knowledge about the underlying mechanism of OSA that leads to an increased risk of lung cancer at the genetic level.Keywords: lung cancer, obstructive sleep apnea, OSA, weighted gene co-expression network analysis, WGCNA, lung cancer prognosis

Keywords

• lung cancer
• obstructive sleep apnea (osa)
• weighted gene co-expression network analysis (wgcna)
• lung cancer prognosis