Skin cancer is the most common form of cancer that accounting for at least 40% of cancer cases around the world. This study aimed to identify skin cancer-related biological features and predict skin cancer candidate genes by employing machine learning based on biological features of known skin cancer genes. The known skin cancer-related genes were fetched from database and encoded by the enrichment scores of gene ontology and pathways. The optimal features of the skin cancer related genes were selected with a series of feature selection methods, such as mRMR, IFS, and Random Forest algorithm. Quantitative PCR (Q-PCR) was performed for the predicted genes. Effects on proliferation and metastasis of skin cancer cell line A431 were detected through MTT and transwell assay. The effects on myosin light chain (MLC) phosphorylation of Actin Gamma 1 (ACTG1) were detected by Western blot. A total of 1233 GO terms and 55 KEGG pathway terms were identified as the optimal features for the depiction of skin cancer. According to those terms, 1134 possible skin cancer-related genes were predicted. We further identified 16 new biomarkers in expression and the classification model can predict skin cancer cases with 100% accuracy. Among the 16 genes, ACTG1 had significantly high expression in skin cancer tissue. Our investigation suggested that ACTG1 can regulate the cell proliferation and migration through ROCK signaling pathway.

The study evaluated an approach to treat skin cancer using surgery combined with local 5-aminolevulinic acid-photodynamic therapy (ALA-PDT). Seventy-six patients with skin cancer who were admitted to the Liaocheng People's Hospital from May 2014 to April 2015 were randomly divided into a control and an observation group (38 cases in each). The patients in the control group were treated with ALA-PDT alone. Those in the observation group were first subjected to surgical treatment, and then treated with ALA-PDT. The treatment efficacies of the two groups were compared. The expression of cancer markers CyPA, CyPB and CD147 were detected by immunohistochemical methods before and after the treatment. Our results showed the average healing time of the wounds of patients in the observation group was shorter, the number of treatments needed was less, the efficacy rate and the lesion appearance satisfaction were significantly higher, and the recurrence rate at 12 months after treatment and the incidence of adverse reactions were both significantly lower. Additionally, the levels of CyPA, CyPB and CD147 were reduced to a significantly higher degree after treatment in the observation group. No difference was found in the recurrence rate between the two groups at 6 months after treatment. We conclude that surgery combined with ALA-PDT is a safe and reliable treatment method, which can increase the survival rate while improving the recovery rate and appearance satisfaction in patients with skin cancer.