基于机器学习构建新型结肠癌免疫评分模型

A novel prediction model of immune signatures for colon cancer based on machine learning

目的:基于免疫相关基因,利用机器学习方法构建结肠癌免疫评分模型,用于评估患者的生存状态和免疫治疗效果。方法:使用Lasso+bootstrap+多因素Cox的组合策略,基于1 301个免疫相关基因在结肠癌中的表达情况,对TCGA中的结肠癌患者进行免疫相关基因(IRG)评分,并对高/低分组在功能差异、预后状态、免疫治疗相关等方面进行比较。结果:基于IRG评分的分组在结肠癌患者预后状态方面具有显著差异,并且通过其他独立数据集的验证,IRG评分模型同时能够评估结肠癌患者的免疫治疗效果。结论:本研究能为基于免疫基因的结肠癌免疫治疗和研究提供思路,IRG评分模型可以用于评估结肠癌患者的预后情况。

Objective:To construct A novel scoring model of immune signatures for colon cancer based on machine learning,which improve the survival prediction and immune therapy.Methods:Screening immune signatures from 1301 immune-related genes(IRG)by the combined strategy of Lasso+bootstrap+multi Cox to calculate IRG scores of colon cancer patients from TCGA databases,and comprehensive the differences on function,prognostic status and immune therapy between high IRG scores group and IRG scores group.Results:Groups based on IRG scores were significantly different on the prognostic status of colon cancer patients,which were validated by other independent datasets.The IRG scores also could assess the effect of immune therapy of colon cancer.Conclusion:This study provides ideas for immune therapy and researches of colon cancer based on immune genes,and IRG scores can be used to assess the prognosis of colon cancer patients.