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Article

Identification of prognostic gene biomarkers for metastatic skin cancer using data mining

  • Authors:
    • Gang Liu
    • Chen Li
    • Haiyan Zhen
    • Zhigang Zhang
    • Yongzhong Sha
  • View Affiliations / Copyright

    Affiliations: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P.R. China, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, Gansu 730000, P.R. China, School of Management, Lanzhou University, Lanzhou, Gansu 730000, P.R. China
  • Pages: 22-30
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    Published online on: May 18, 2020
       https://doi.org/10.3892/br.2020.1307
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Abstract

Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10‑fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow‑up clinical treatment of skin cancer.
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Copy and paste a formatted citation
Spandidos Publications style
Liu G, Li C, Zhen H, Zhang Z and Sha Y: Identification of prognostic gene biomarkers for metastatic skin cancer using data mining. Biomed Rep 13: 22-30, 2020.
APA
Liu, G., Li, C., Zhen, H., Zhang, Z., & Sha, Y. (2020). Identification of prognostic gene biomarkers for metastatic skin cancer using data mining. Biomedical Reports, 13, 22-30. https://doi.org/10.3892/br.2020.1307
MLA
Liu, G., Li, C., Zhen, H., Zhang, Z., Sha, Y."Identification of prognostic gene biomarkers for metastatic skin cancer using data mining". Biomedical Reports 13.1 (2020): 22-30.
Chicago
Liu, G., Li, C., Zhen, H., Zhang, Z., Sha, Y."Identification of prognostic gene biomarkers for metastatic skin cancer using data mining". Biomedical Reports 13, no. 1 (2020): 22-30. https://doi.org/10.3892/br.2020.1307
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Spandidos Publications style
Liu G, Li C, Zhen H, Zhang Z and Sha Y: Identification of prognostic gene biomarkers for metastatic skin cancer using data mining. Biomed Rep 13: 22-30, 2020.
APA
Liu, G., Li, C., Zhen, H., Zhang, Z., & Sha, Y. (2020). Identification of prognostic gene biomarkers for metastatic skin cancer using data mining. Biomedical Reports, 13, 22-30. https://doi.org/10.3892/br.2020.1307
MLA
Liu, G., Li, C., Zhen, H., Zhang, Z., Sha, Y."Identification of prognostic gene biomarkers for metastatic skin cancer using data mining". Biomedical Reports 13.1 (2020): 22-30.
Chicago
Liu, G., Li, C., Zhen, H., Zhang, Z., Sha, Y."Identification of prognostic gene biomarkers for metastatic skin cancer using data mining". Biomedical Reports 13, no. 1 (2020): 22-30. https://doi.org/10.3892/br.2020.1307
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