| Popis: |
Background: Tumor infiltration, is known to associate with various cancer initiations and progressions, is potential therapeutic target for this aggressive skin cancer.Methods: single sample gene set enrichment analysis (ssGSEA) algorithm was applied to assess the relative expression of 24 types of immune cell from public database. Firstly, the differentially expressed immune cells between melanomas and normal samples were identified. Next, multiple machine learning algorithms were performed to evaluate the efficiency of immune cells in diagnosis of melanoma. In addition, the feature selection in machine learning methods was used to figure out the most important prognostic immune cells for developing biomarker to predict the prognosis of melanoma.Results: In comparison with the expression of immune cells in tumors and normal controls, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC= 0.965, RF AUC= 0.99, SVM AUC=0.963, LASSO AUC= 0.964 and NNET AUC=0.989). These diagnostic models also validated in three outside datasets and suggested over 90% sensitivity and specificity to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker which could estimate the prognosis of melanoma. This biomarker also further validated in internal and external datasets. Next, we constructed nomogram combined risk score of biomarker and clinical characteristics, which showed good accuracies in predicting 3 and 5 years’ survival. The decision curve of nomogram model manifested a higher net benefit than tumor stage. In addition, melanoma patients divided into high and low risk subgroups by applied risk score system. The high risk group have a significantly shorter survival time than the low risk subgroup. Gene Set Enrichment Analysis (GSEA) analysis revealed that complement, epithelial mesenchymal transition and inflammatory response and so on significantly activated in high risk group. Conclusions: We constructed immune cell related diagnostic and prognostic models, which could provide new clinical applications for diagnosing and predicting the survival of melanoma patients. |
