Abstract
Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p = 0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.
Highlights
Lung cancer is one of the most common malignant tumors in the world [1]
We found that the four hub genes (CCNB1, CCNE2, MCM7, and PCNA) enriched in the cell cycle pathway were the most important genes, which played significant roles in other pathways
The identification of disease-associated modules via weighted gene co-expression network analysis (WGCNA) which focused on the relationship between gene co-expression modules has emerged as a powerful and reliable method of obtaining novel insights into cancer biology [10, 14]
Summary
Lung cancer is one of the most common malignant tumors in the world [1]. According to the data released by the World Health Organization in 2019, the incidence and mortality of lung cancer in all kinds of malignant tumors in the world are the highest [2]. The development of tumor biomarkers is one of the means of early diagnosis of lung cancer, but only from a local focus on a single or a certain gene cannot meet the regulation of this highly complex tumor. Based on the whole regulation network, some genes in tumors are abnormally expressed and closely related to many other genes. Their expression may play an important role in the occurrence and development of tumors. The WGCNA method was applied to analyze the gene expression dataset to identify the candidate biomarkers for lung cancer based on the TNM stage of lung cancer patients
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