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.

Keywords: Skin cancer;Metastatic;Least absolute shrinkage and selection operator;Gene biomarker;Prognosis;Data mining

资金资助: This work was supported by the Key Project of China Ministry of Education for Philosophy and Social Science: Big Data Driven Risk Research on City's Public Safety (grant no. 16JZD023) and the Fundamental Research Funds for the Central Universities: Big Data Driven Risk Pre-Warning Research on City's Public Safety (grant no. 17LZUJBWZD012)

原文链接：https://www.spandidos-publications.com/10.3892/br.2020.1307#