Abstract
Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.