Data Mining Analysis of Gene Prognostic Markers of Metastatic Skin Cancer Based on the Elastic Network Method

Abstract

Skin cancer is a typical cancer tumor, which occurs all over the world and has a relatively high recurrence rate, including metastatic tumors that occur in other tissues and metastases to the skin, thus jeopardizing the personal life satisfaction and soundness of patients. Due to individual differences, the traditional treatment methods cannot adapt to every patient accurately, so it is difficult to achieve the desired treatment effect for each individual. Nowadays, with the development of gene chip, many new therapies based on gene are more targeted and flexible for the treatment of skin cancer patients. Therefore, it is necessary to mine and analyze appropriate gene biomarkers according to patients' genes. Because of the high cost of gene chip technology and the large number of human genes, there are few samples of gene data and high dimensions. It is a key problem to mine effective genetic biomarkers from the sample data. In this paper, we firstly performed the preliminary analysis using the difference expression analysis and proportional hazards model, then used the elastic network method to reduce the range of genetic data selection, and screened 26 gene prognostic markers closely related to the recurrence of metastatic skin cancer. Finally, the 26 gene biomarkers were analyzed by functional analysis and verified using a test sample. Research findings have shown that the obtained genetic markers have certain value in the clinical prognostic treatment of metastatic skin cancer.