Open Access

Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments

  • Authors:
    • Bo Ling
    • Xianjiu Liao
    • Yuanhe Huang
    • Lingling Liang
    • Yan Jiang
    • Yaqin Pang
    • Guangzi Qi
  • View Affiliations

  • Published online on: November 28, 2019     https://doi.org/10.3892/ijo.2019.4926
  • Pages: 193-205
  • Copyright: © Ling 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 one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co‑expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10‑knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10‑knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin‑β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin‑β1/FAK pathway, as the protein expression levels of integrin‑β1, phosphorylated (p)‑FAK and p‑AKT were significantly upregulated. Activation of the integrin‑β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.
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January 2020
Volume 56 Issue 1

Print ISSN: 1019-6439
Online ISSN:1791-2423

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APA
Ling, B., Liao, X., Huang, Y., Liang, L., Jiang, Y., Pang, Y., & Qi, G. (2020). Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments. International Journal of Oncology, 56, 193-205. https://doi.org/10.3892/ijo.2019.4926
MLA
Ling, B., Liao, X., Huang, Y., Liang, L., Jiang, Y., Pang, Y., Qi, G."Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments". International Journal of Oncology 56.1 (2020): 193-205.
Chicago
Ling, B., Liao, X., Huang, Y., Liang, L., Jiang, Y., Pang, Y., Qi, G."Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments". International Journal of Oncology 56, no. 1 (2020): 193-205. https://doi.org/10.3892/ijo.2019.4926