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Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis

  • Authors:
    • Nan Zhang
    • Shao‑Wei Zhang
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    Affiliations: Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
    Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 3754-3768
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    Published online on: August 6, 2019
       https://doi.org/10.3892/ol.2019.10723
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Abstract

Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein‑protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan‑Meier plotter was used to analyze overall survival (OS). Additionally, one co‑expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC.
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Copy and paste a formatted citation
Spandidos Publications style
Zhang N and Zhang SW: Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncol Lett 18: 3754-3768, 2019.
APA
Zhang, N., & Zhang, S. (2019). Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncology Letters, 18, 3754-3768. https://doi.org/10.3892/ol.2019.10723
MLA
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18.4 (2019): 3754-3768.
Chicago
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18, no. 4 (2019): 3754-3768. https://doi.org/10.3892/ol.2019.10723
Copy and paste a formatted citation
x
Spandidos Publications style
Zhang N and Zhang SW: Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncol Lett 18: 3754-3768, 2019.
APA
Zhang, N., & Zhang, S. (2019). Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncology Letters, 18, 3754-3768. https://doi.org/10.3892/ol.2019.10723
MLA
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18.4 (2019): 3754-3768.
Chicago
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18, no. 4 (2019): 3754-3768. https://doi.org/10.3892/ol.2019.10723
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