DOI: 10.57760/sciencedb.j00217.00352
title: Constructing and Validating a Prognostic Model for Glycosyltransferases in Melanoma and Analyzing the Tumor Immune Microenvironment Based on Single-Cell Sequencing Data
title: 构建和验证一种糖基转移酶相关黑色素瘤预后模型并基于单细胞测序数据分析肿瘤免疫微环境

description: Objective Our objective is to establish a predictive model based on glycosyltransferase-related genes (GTs) to predict the survival time of patients with Skin Cutaneous Melanoma (SKCM) and explore the pathways and mechanisms through which GTs influence the prognosis of SKCM.Methods The study utilized individualized prognostic modeling based on transcriptomic data of SKCM from the Cancer Genome Atlas (TCGA) and the reliability of the model was validated using GEO data. Univariate Cox regression and LASSO regression were employed to select biomarkers associated with SKCM prognosis and a predictive Riskscore was constructed using multivariate Cox regression. GO、KEGG and GSEA analyses were performed to annotate the functional implications of the Riskscore. The performance of the nomogram model was evaluated using ROC curves、calibration plots and C-Index. Additionally, subsequent analyses were conducted on immune infiltration、somatic mutations and im-mune therapy response based on risk stratification and scRNA analysis was employed to validate these findings.Results This study found a significant correlation between the predictive Riskscore constructed using multivariate Cox regression and the overall survival rate of SKCM. Enrichment analysis of the Riskscore revealed its association with immune function. The nomogram model which combines the Riskscore and clinical prognostic factors, demonstrated robust predictive ability in both the training and validation da-tasets. Subsequent analyses on immune infiltration、single-cell analysis、somatic mutation analysis and immune therapy response all showed a correlation between the key gene MGAT4A and the infiltration of CD8+ T cells and monocytes/macrophages in tumor tissue.Conclusion We have developed an individualized predictive model for predicting the 1-year、3-year, 5-year and 10-year survival rates of SKCM patients. This model has the potential to serve as a valuable tool in guiding personalized diagnosis and treatment for SKCM.
description: Objective Our objective is to establish a predictive model based on glycosyltransferase-related genes (GTs) to predict the survival time of patients with Skin Cutaneous Melanoma (SKCM) and explore the pathways and mechanisms through which GTs influence the prognosis of SKCM.Methods The study utilized individualized prognostic modeling based on transcriptomic data of SKCM from the Cancer Genome Atlas (TCGA) and the reliability of the model was validated using GEO data. Univariate Cox regression and LASSO regression were employed to select biomarkers associated with SKCM prognosis and a predictive Riskscore was constructed using multivariate Cox regression. GO、KEGG and GSEA analyses were performed to annotate the functional implications of the Riskscore. The performance of the nomogram model was evaluated using ROC curves、calibration plots and C-Index. Additionally, subsequent analyses were conducted on immune infiltration、somatic mutations and im-mune therapy response based on risk stratification and scRNA analysis was employed to validate these findings.Results This study found a significant correlation between the predictive Riskscore constructed using multivariate Cox regression and the overall survival rate of SKCM. Enrichment analysis of the Riskscore revealed its association with immune function. The nomogram model which combines the Riskscore and clinical prognostic factors, demonstrated robust predictive ability in both the training and validation da-tasets. Subsequent analyses on immune infiltration、single-cell analysis、somatic mutation analysis and immune therapy response all showed a correlation between the key gene MGAT4A and the infiltration of CD8+ T cells and monocytes/macrophages in tumor tissue.Conclusion We have developed an individualized predictive model for predicting the 1-year、3-year, 5-year and 10-year survival rates of SKCM patients. This model has the potential to serve as a valuable tool in guiding personalized diagnosis and treatment for SKCM.