Open Access

Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis

  • Authors:
    • Yangfeng Shi
    • Yeping Li
    • Chao Yan
    • Hua Su
    • Kejing Ying
  • View Affiliations

  • Published online on: September 30, 2019     https://doi.org/10.3892/ol.2019.10933
  • Pages: 5859-5870
  • Copyright: © Shi 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 the leading cause of cancer‑related mortality worldwide. Despite progress in the treatment of non‑small‑cell lung cancer, there are limited treatment options for lung squamous cell carcinoma (LUSC), compared with lung adenocarcinoma. The present study investigated the disease mechanism of LUSC in order to identify key candidate genes for diagnosis and therapy. A total of three gene expression profiles (GSE19188, GSE21933 and GSE74706) were analyzed using GEO2R to identify common differentially expressed genes (DEGs). The DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein‑protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins, and visualized using Cytoscape software. The expression levels of the hub genes identified using CytoHubba were validated using the University of California, Santa Cruz (UCSC) database and the Human Protein Atlas. A Kaplan‑Meier curve and Gene Expression Profiling Interactive Analysis were then employed to evaluate the associated prognosis and clinical pathological stage of the hub genes. Furthermore, non‑coding RNA regulatory networks were constructed using the Gene‑Cloud Biotechnology information website. A total of 359 common DEGs (155 upregulated and 204 downregulated) were identified, which were predominantly enriched in ‘mitotic nuclear division’, ‘cell division’, ‘cell cycle’ and ‘p53 signaling pathway’. The PPI network consisted of 257 nodes and 2,772 edges, and the most significant module consisted of 66 upregulated genes. A total of 19 hub genes exhibited elevated RNA levels, and 10 hub genes had elevated protein levels compared with normal lung tissues. The upregulation of five hub genes (CCNB1, CEP55, FOXM1, MKI67 and TYMS; defined in Table I) were significantly associated with poor overall survival and unfavorable clinical pathological stages. Various ncRNAs, such as C1orf220, LINC01561 and MGC39584, may also play important roles in hub‑gene regulation. In conclusion, the present study provides further understanding of the pathogenesis of LUSC, and reveals CCNB1, CEP55, FOXM1, MKI67 and TYMS as potential biomarkers or therapeutic targets.

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December 2019
Volume 18 Issue 6

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
APA
Shi, Y., Li, Y., Yan, C., Su, H., & Ying, K. (2019). Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis. Oncology Letters, 18, 5859-5870. https://doi.org/10.3892/ol.2019.10933
MLA
Shi, Y., Li, Y., Yan, C., Su, H., Ying, K."Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis". Oncology Letters 18.6 (2019): 5859-5870.
Chicago
Shi, Y., Li, Y., Yan, C., Su, H., Ying, K."Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis". Oncology Letters 18, no. 6 (2019): 5859-5870. https://doi.org/10.3892/ol.2019.10933