基于机器学习算法构建黑色素瘤免疫治疗预后预测模型

Objective: This study aims to develop a Melanoma Microenvironment Prognostic Score (MMPS) using bioinformatics techniques, integrating gene expression profiles and clinical characteristics to enable dynamic prognostic stratification and provide a theoretical foundation for combined biomarker applications.Methods: We employed a multi-omics analytical framework combined with machine learning algorithms to identify core regulatory genes within the tumor microenvironment (TME). Prognostic genes were optimized and selected via LASSO-Cox regression modeling to construct the MMPS. The model\'s robustness was rigorously validated through multi-level verification, including nomogram calibration.Results: A cohort of 435 melanoma patients from the TCGA-SKCM database was analyzed. By characterizing immune microenvironment infiltration landscapes and performing differentially expressed gene analysis, 199 TME-critical genes were identified. A refined MMPS model comprising five core genes was established using LASSO-Cox regression. The MMPS demonstrated superior predictive performance for 5-year overall survival, outperforming traditional TNM staging by 28% in predictive efficacy.Conclusion: The MMPS model, rooted in melanoma microenvironment dynamics, serves as a robust tool for prognostic prediction in melanoma patients, offering enhanced clinical utility over conventional staging systems.