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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 hypoxiarelated 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 highlow risk groups were shown to be correlated with the MAPK signaling pathway,Rap1signaling 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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基本信息:
DOI：10.16389/j.cnki.cn42-1737/n.2024.06.009
中图分类号:R735.35
引用信息:
[1]杨光,田瑶,谢寒丹.基于数据挖掘的缺氧调控铁死亡参与结肠癌发生机制研究[J].江汉大学学报(自然科学版),2024,52(06):77-86.DOI:10.16389/j.cnki.cn42-1737/n.2024.06.009.
基金信息:
江汉大学校级学生科研项目（2023yb167）