首页|机器学习法联合免疫细胞浸润分析铁死亡在慢性髓系白血病中的作用机制

机器学习法联合免疫细胞浸润分析铁死亡在慢性髓系白血病中的作用机制

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[目的]基于机器学习法筛选慢性髓系白血病(CML)中铁死亡关键基因,分析关键基因在CML中的潜在作用机制。[方法]将GEO数据库中的两个CML数据集(GSE5550和GSE24739)进行整合并消除批次效应;采用"LIMMA"R包在25例CML和16例对照样本中对铁死亡基因集进行差异分析;使用"ClusterProfiler"R包对差异基因进行GO和KEGG富集分析;使用SVM-RFE和LASSO回归两种筛选关键基因;通过Cytoscape软件构建关键基因MiRNA-TF-mRNA调控网络;使用CIBERSORT算法分析CML中22种免疫细胞组分的组成模式以及关键基因与免疫细胞相关性。[结果]差异分析共获得出34个铁死亡差异基因,34个差异基因共富集出29条通路和388个GO条目。机器学习鉴定AKR1C1、DUSP1、FADS2和SLC1A4是CML的关键基因,并通过受试者工作特征曲线(ROC)验证。MiRNA-TF-mRNA调控网络显示多个部分MiRNAs可以同时调控铁死亡特征基因,SLC1A4主要调控CLI3、CLI2、PCBP2、BRCA2、SP2和ZBTB17六个转录因子,而其他转录因子则通过MiRNA调控。免疫细胞浸润分析显示,SLC1A4与T滤泡辅助细胞静息相关。FADS2与B祖细胞、B记忆细胞、CD8 T细胞、T滤泡辅助细胞、NK静息细胞、巨噬细胞M0、树突状细胞和中性粒细胞相关。DUSP1与B记忆细胞静息相关。[结论]CML中4个铁死亡关键基因AKR1C1、DUSP1、FADS2和SLC1A4可作为CML的潜在生物标志物,其与免疫细胞浸润的关联可能为理解CML的发展提供新的见解。
Machine learning combined with immune cell infiltration to analyze the mechanism of ferroptosis in chronic myeloid leukemia
[Objective]The key genes of ferroptosis in chronic myeloid leukemia(CML)were screened based on machine learning method,and the potential mechanism of the key genes in CML was analyzed.[Method]Two CML datasets(GSE5550 and GSE24739)in the GEO database were integrated to eliminate the batch effect;The"LIMMA"R package was used to dif-ferentially analyze the ferroptosis gene set in 25 CML and 16 control samples;GO and KEGG enrichment analyses were per-formed for differentially differentiated genes using the"ClusterProfiler"R package;Two key genes were screened using SVM-RFE and LASSO regression;The MiRNA-TF-mRNA regulatory network of key genes was constructed by Cytoscape soft-ware;The CIBERSORT algorithm was used to analyze the composition patterns of 22 immune cell components in cml and the correlation between key genes and immune cells.[Result]A total of 34 ferroptosis differential genes were obtained.A total of 29 pathways and 388 GO entries were enriched from 34 differential genes.Machine learning identified AKR1C1,DUSP1,FADS2 and SLC1A4 as key genes for CML and validated them by Receiver Operating Characteristic Curve(ROC).The MiRNA-TF-mRNA regulatory network showed that multiple MiRNAs could simultaneously regulate ferroptosis,and SLC1A4 mainly regulated six transcription factors,CLI3,CLI2,PCBP2,BRCA2,SP2 and ZBTB17,while other transcription factors were regulated by MiR-NA.Immune cell infiltration analysis showed that SLC1A4 was closely related to T follicular helper cells.FADS2 is associated with B progenitor cells,B memory cells,CD8 T cells,T follicular helper cells,NK resting cells,macrophages M0,dendritic cells,and neutrophils.DUSP1 is closely associated with B memory cells.[Conclusion]The four key ferroptosis genes in CML,AKR1C1,DUSP1,FADS2 and SLC1A4,can be used as potential biomarkers for CML,and their association with immune cell in-filtration may provide new insights for understanding the development of CML.

chronic myeloid leukemiaferroptosismachine learning methodsimmune cell infiltrationmiRNA-TF-mRNA regulatory network

冯小云、秦玉凤、袁月、李丹、张鹏

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贵州医科大学组织工程与干细胞中心,贵州贵阳 550004

贵州医科大学生物学教研室,贵州贵阳 550004

贵州医科大学附属口腔医院,贵州贵阳 550004

慢性髓系白血病 铁死亡 机器学习法 免疫细胞浸润 MiRNA-TF-mRNA调控网络

2024

生物技术
黑龙江省微生物学会 黑龙江省生物工程学会 黑龙江省科学院微生物研究所

生物技术

CSTPCD
影响因子:0.611
ISSN:1004-311X
年,卷(期):2024.34(6)