{'fraction_non_alphanumeric': 0.017025089605734768, 'fraction_numerical': 0.0062724014336917565, 'mean_word_length': 5.382857142857143, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A new genetic method to predict the prognostic outcome of melanoma

Cutaneous melanoma is quite often encountered in dermato-oncology. This paper describes a new genetic method to predict the prognostic outcome of melanoma. Data were collected from the TCGA databases. According to tumor progression status, the data were divided into two groups to evaluate the significant biological processes and key genes influencing the outcome of melanoma using a bioinformatics method. By adopting a statistical regression analysis method, a novel score based on the contributing genes was developed. Cox regression analysis was used to validate the effectiveness of the genetic risk score in predicting the outcome. Seven biological processes associated with melanocytes were identified. A protein-protein interactions network showed that 27 functional genes were associated with the outcome of melanoma. Among these, three genes (COL17A1, ITGA6, and SPRR2F) were used to calculate the genetic risk score, which was regarded as an independent and effective risk factor for disease progression or overall survival in melanoma.