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Comprehensive bioinformatics analysis identifies several potential diagnostic markers and potential roles of cyclin family members in lung adenocarcinoma

Authors Gao LW, Wang GL

Received 20 April 2018

Accepted for publication 29 August 2018

Published 24 October 2018 Volume 2018:11 Pages 7407—7415

DOI https://doi.org/10.2147/OTT.S171705

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 2

Editor who approved publication: Dr Takuya Aoki


Li-Wei Gao,1 Guo-Liang Wang2

1Department of Oncology, General Hospital of Pingmei Shenma Medical Group, Pingdingshan, Henan 467000, China; 2Department of Research & Development, Henan Zhongping Genetic Technology Co, Ltd, Zhengzhou, Henan 450000, China

Purpose: The aim of this study was to identify critical genes in lung cancer progression.
Methods: We downloaded and reanalyzed gene expression profiles from different public datasets using comprehensive bioinformatics analysis. Differentially expressed genes (DEGs) were identified in lung adenocarcinoma tissues compared with adjacent nonmalignant lung tissues. The overlapping DEGs identified from different datasets were used for functional and pathway enrichment analyses and protein–protein interaction (PPI) analysis. Moreover, transcription factors (TFs) and miRNAs that regulated the overlapping DEGs were predicted, followed by a TF–miRNA–target network construction. Furthermore, survival analysis of genes was performed. Several genes were further validated by quantitative real-time PCR (qRT-PCR).
Results: A total of 647 overlapping upregulated genes and 979 overlapping downregulated genes were identified. The overlapping upregulated genes and downregulated genes were involved in different functions, such as cell cycle, p53 signaling pathway, immune response, and cell adhesion molecules (CAMs). Several genes belonging to the cyclin family, including CCNB1, CCNB2, and CCNA2, were hubs of the PPI network and TF–miRNA–target network. Additionally, genes, including NPAS2, GNG7, CHIA, and SLC2A1, were predicted to be prognosis-related DEGs. Gene expression profiles determined by bioinformatics analysis and qRT-PCR were highly comparable.
Conclusion:
CCNB1, CCNB2, CCNA2, NPAS2, GNG7, CHIA, and SLC2A1 are promising targets for the clinical diagnosis and therapy of lung adenocarcinoma.

Keywords:
lung cancer, differentially expressed genes, transcription factor, prognosis

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