Identification of key candidate tumor biomarkers in non‑small‑cell lung cancer by in silico analysis

Chen,Weiping; Zhu,Song; Zhang,Yifei; Xiao,Jinghua; Tian,Dongbo

doi:10.3892/ol.2019.11169

Identification of key candidate tumor biomarkers in non‑small‑cell lung cancer by in silico analysis

Authors:
- Weiping Chen
- Song Zhu
- Yifei Zhang
- Jinghua Xiao
- Dongbo Tian
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Affiliations: Department of Respiratory Medicine, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, Guangdong 511518, P.R. China, Department of Radiotherapy, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, Guangdong 511518, P.R. China
Published online on: December 2, 2019 https://doi.org/10.3892/ol.2019.11169
Pages: 1008-1016
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Lung cancer is a common malignancy worldwide. The aim of the present study was to investigate differentially expressed genes (DEGs) between non‑small‑cell lung cancer (NSCLC) and normal lung tissue, and to reveal the potential molecular mechanism underlying NSCLC. The Gene Expression Omnibus database was used to obtain three gene expression profiles (GSE18842, GSE30219 and GSE33532). DEGs were obtained by GEO2R. Gene Ontology and pathway enrichment analyses were performed for DEGs in the Database for Annotation, Visualization and Integrated Discovery. A protein‑protein interaction (PPI) network of DEGs was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape software. A survival analysis was performed and protein expression levels of DEGs in human NSCLC were analyzed in order to determine clinical significance. A total of 764 DEGs were identified, consisting of 428 upregulated and 336 downregulated genes in NSCLC tissues compared with normal lung tissues, which were enriched in the ‘cell cycle’, ‘cell adhesion molecules’, ‘p53 signaling pathway’, ‘DNA replication’ and ‘tight junction’. A PPI network of DEGs consisting of 51 nodes and 192 edges was constructed. The top 10 genes were identified as hub genes from the PPI network. High expression of 4 of the 10 hub genes was associated with worse overall survival rate in patients with NSCLC, including CDK1, PLK1, RAD51 and RFC4. In conclusion, the present study aids in improving the current understanding of aberrant gene expression between NSCLC tissues and normal lung tissues underlying tumorgenesis in NSCLC. Identified hub genes can be used as a tumor marker for diagnosis and prognosis or as a drug therapy target in NSCLC.

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