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Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Authors Song YJ, Tan J, Gao XH, Wang LX

Received 19 March 2018

Accepted for publication 4 August 2018

Published 21 November 2018 Volume 2018:10 Pages 6097—6108

DOI https://doi.org/10.2147/CMAR.S168636

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 3

Editor who approved publication: Dr Antonella D'Anneo


Ying-Jian Song,1 Juan Tan,2 Xin-Huai Gao,1 Li-Xin Wang1

1Department of Respiratory Medicine, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, People’s Republic of China; 2Department of Gerontology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, People’s Republic of China

Background: Lung cancer is one of the most common malignant tumors. Despite advances in lung cancer therapies, prognosis of non-small-cell lung cancer is still unfavorable. The aim of this study was to identify the prognostic value of key genes in lung tumorigenesis.
Methods: Differentially expressed genes (DEGs) were screened out by GEO2R from three Gene Expression Omnibus cohorts. Common DEGs were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein–­protein interaction networks were constructed by the STRING database and visualized by Cytoscape software. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database, and their genomic alterations were identified by performing the cBioportal. Finally, overall survival analysis of hub genes was performed using Kaplan–Meier Plotter.
Results: From three datasets, 169 DEGs (70 upregulated and 99 downregulated) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that upregulated DEGs were significantly enriched in cell cycle, p53 pathway, and extracellular matrix–receptor interactions; the downregulated DEGs were significantly enriched in PPAR pathway and tyrosine metabolism. The protein–protein interaction network consisted of 71 nodes and 305 edges, including 49 upregulated and 22 downregulated genes. The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group by Gene Expression Profiling Interactive Analysis analysis, and associated with reduced overall survival in lung cancer patients. In the genomic alterations analysis, two hotspot mutations (S2021C/F and E314K/V) were identified in Pfam protein domains.
Conclusion: DEGs, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, might be potential biomarkers for the prognosis and treatment of lung adenocarcinoma.

Keywords: lung adenocarcinoma, prognosis, gene expression profiling, differentially expressed, bioinformatics analysis

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