摘要
目 的 基于数据挖掘技术,探讨在缺氧条件下铁死亡参与结肠癌的机制.方 法 以TCGA-COAD为实验队列,GSE39582为验证队列.根据 174个缺氧相关基因和 517个铁死亡相关基因,通过Cox和LASSO回归分析构建风险评分预后模型,将数据中所有病患区分为高风险组和低风险组.随后进一步对两个风险组之间的临床和免疫特征进行综合分析,最后进行高低风险组之间的差异基因与结肠癌的GO(BP、MF、CC)和KEGG富集分析.结 果 构建了包含 9 个 基 因(CDKN2A、FNDC5、ENO3、GSTM1、JDP2、ANGPTL4、ANKZF1、TKTL1、PPARGC1A)的风险评分模型.与高风险组相比,低风险组预后较佳,且高风险组的免疫细胞浸润水平高于低风险组.通过对高低风险评分组之间的差异基因进行GO分析及KEGG通路富集分析显示与MAPK信号通路、Rap1信号通路、趋化因子信号通路等肿瘤及免疫相关通路密切相关.结 论 构建了缺氧条件下结合铁死亡的结肠癌患者风险评分模型,可为肿瘤的个体化治疗提供新的思路.
Abstract
Objective To study the mechanism of ferroptosis involved in colon cancer under hypoxia based on data mining technology.Methods Taking TCGA-COAD as the experimental cohort and GSE39582 as the verification cohort,based on 174 hypoxia-related genes and 517 ferroptosis-related genes,a risk-scoring prognostic model was constructed by Cox and LASSO regression analysis,and the patients were divided into high-risk and low-risk groups.Then,the clinical and immune characteristics of the two risk groups were further analyzed.Finally,GO(BP,MF,CC)and KEGG enrichment analysis of differential genes between the high and low-risk groups were performed.Results A risk-scoring model containing 9 genes(CDKN2A,FNDC5,ENO3,GSTM1,JDP2,ANGPTL4,ANKZF1,TKTL1,PPARGC1A)was constructed.Compared with the high-risk group,the low-risk group had a better prognosis,and the level of immune cell infiltration in the high-risk group was higher than that in the low-risk group.Through GO analysis and KEGG pathway enrichment analysis,the differential genes between the high-low risk groups were shown to be correlated with the MAPK signaling pathway,Rap1 signaling pathway,Chemokine signaling pathway,and other tumor and immune-related pathways.Conclusion The risk-scoring model for colon cancer patients with ferroptosis under hypoxia conditions provides a new idea for individualized tumor treatment.
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