In Silico Discovery of Potential Diagnostic Biomarkers of Lung Cancer

Abstract

Understanding hub genes implicated in lung cancer (LC) metastasis will help in finding effective ways to diagnose and cure cancer. Accurate identification of protein biomarkers helps in improving the prognosis of LC. Here, the focus of this study was to discern the biomarkers that are implicated in LC. Three datasets were extracted from Gene Expression Omnibus (GEO) database. GEO2R tool was used to identify the differentially expressed genes (DEGs) between LC and normal lung samples. Funrich software, Enrichr and KEGG database was used to identify the common DEGs in LC, continued by identifying functions and pathways. Next, protein-protein interactions was obtained from STRING database. The hub genes were identified by using CytoHubba tool. Then, the prognostic value in the identified genes was verified by using LC database in Kaplan-Meier Plotter platform. A total of 215 downregulated and 84 upregulated were overlapped. A total of ten hub genes such as IL-6, MMP9, SPP1, EZH2, COL1A1, PECAM1, CDK1, VWF, EDN1 and CD34 were selected. Three significant DEGs were identified to be associated with favourable overall survival in LC patients which were PECAM1, EDN1 and VWF. Therefore, this study suggests all the hub genes may be potential biomarkers and treatment target for LC.