Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis

Abstract

Background: Lung cancer is the most common cause of cancer-related death among all human cancers and the five-year survival rates are only 23%. The precise molecular mechanisms of non-small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis.

Methods: Four GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software and Molecular Complex Detection (MCODE) were utilized to PPI network to pick out meaningful DEGs. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database. The expressions and prognostic values of hub genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier plotter. Finally, quantitative PCR and the Oncomine database were used to verify the differences in the expression of hub genes in lung cancer cells and tissues.

Results: A total of 121 DEGs (49 upregulated and 72 downregulated) were identified from four datasets. The PPI network was established with 121 nodes and 588 protein pairs. Finally, AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 were selected by Cytohubba, and they all correlated with worse overall survival (OS) in NSCLC.

Conclusion: The results showed that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC.

Key points: Our results indicated that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. Our methods showed a new way to explore the key genes in cancer development.

Keywords: Bioinformatics analysis; differentially expressed gene; gene expression omnibus; non-small-cell lung cancer; prognosis.