Open Access

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

  • Authors:
    • Xiaodong Yang
    • Yongjia Zhou
    • Haibo Ge
    • Zhongxian Tian
    • Peiwei Li
    • Xiaogang Zhao
  • View Affiliations

  • Published online on: December 9, 2022     https://doi.org/10.3892/etm.2022.11762
  • Article Number: 63
  • Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of lung cancer, which is the most prevalent and lethal malignancy worldwide. Cyclins have been reported to regulate the physiology of various types of tumors by controlling cell cycle progression. However, the key roles and regulatory networks associated with the majority of the cyclin family members in LUAD remain unclear. In total, 556 differentially expressed genes were screened from the GSE33532, GSE40791 and GSE19188 mRNA microarray datasets by R software. Subsequently, protein‑protein interaction network containing 499 nodes and 4,311 edges, in addition to a significant module containing 76 nodes and 2,631 edges, were extracted through the MCODE plug‑in of Cytoscape. A total of four cyclin family genes [cyclin (CCNA2, CCNB1, CCNB2 and CCNE2] were then found in this module. Further co‑expression analysis and associated gene prediction revealed forkhead box M1 (FOXM1), the common transcription factor of CCNB2, CCNB1 and CCNA2. In addition, using GEPIA database, it was found that the high expression of these four genes were simultaneously associated with poorer prognosis in patients with LUAD. Experimentally, it was proved that these four hub genes were highly expressed in LUAD cell lines (Beas‑2B and H1299) and LUAD tissues through qPCR, western blot analysis and immunohistochemical studies. The diagnostic value of these 4 hub genes in LUAD was analyzed by logistic regression, CCNA2 was deleted, following which a nomogram diagnostic model was constructed accordingly. The area under the curve values of CCNB1, CCNB2 and FOXM1 diagnostic models were calculated to be 0.92, 0.91 and 0.96 in the training set (Combined dataset of GSE33532, GSE40791 and GSE19188) and two validation sets (GSE10072 and GSE75037), respectively. To conclude, data from the present study suggested that the FOXM1/cyclin (CCNA2, CCNB1 and/or CCNB2) axis may serve a regulatory role in the development and prognosis of LUAD. Specifically, CCNB1, CCNB2 and FOXM1 have potential as diagnostic markers and/or therapeutic targets for LUAD treatment.
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January-2023
Volume 25 Issue 1

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Copy and paste a formatted citation
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Spandidos Publications style
Yang X, Zhou Y, Ge H, Tian Z, Li P and Zhao X: Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR. Exp Ther Med 25: 63, 2023
APA
Yang, X., Zhou, Y., Ge, H., Tian, Z., Li, P., & Zhao, X. (2023). Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR. Experimental and Therapeutic Medicine, 25, 63. https://doi.org/10.3892/etm.2022.11762
MLA
Yang, X., Zhou, Y., Ge, H., Tian, Z., Li, P., Zhao, X."Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR". Experimental and Therapeutic Medicine 25.1 (2023): 63.
Chicago
Yang, X., Zhou, Y., Ge, H., Tian, Z., Li, P., Zhao, X."Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR". Experimental and Therapeutic Medicine 25, no. 1 (2023): 63. https://doi.org/10.3892/etm.2022.11762