Liu Qian, Lin Ze, Yue Minghui, Wu Jianbo, Li Lei, Huang Daqi, Fang Yipeng, Zhang Xin, Hao Tao
Department of Cardiology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Shantou University Medical College, Shantou, Guangdong, China.
Front Genet. 2024 Jul 30;15:1365232. doi: 10.3389/fgene.2024.1365232. eCollection 2024.
Ferroptosis has been observed to play an important role during erythrocyte differentiation (ED). However, the biological gene markers and ferroptosis mechanisms in ED remain unknown. We downloaded the datasets of ED in human umbilical cord blood-derived CD34 cells from the Gene Expression Omnibus database. Using median differentiation time, the sample was categorized into long and short groups. The differentially expressed ferroptosis-related genes (DE-FRGs) were screened using differential expression analysis. The enrichment analyses and a protein-protein interaction (PPI) network were conducted. To predict the ED stage, a logistic regression model was constructed using the least absolute shrinkage and selection operator (LASSO). Overall, 22 DE-FRGs were identified. Ferroptosis-related pathways were enriched using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Gene Set Enrichment Analysis and Gene Set Variation Analysis revealed the primary involvement of DE-FRGs in JAK-STAT, MAPK, PI3K-AKT-mTORC1, WNT, and NOTCH signaling pathways. Ten-hub DE-FRGs were obtained using PPI analysis. Furthermore, we constructed mRNA-microRNA (miRNA) and mRNA-transcription factor networks. Immune cell infiltration levels differed significantly during ED. LASSO regression analysis established a signature using six DE-FRGs ( and ) related to the ED stage. Bioinformatic analyses identified ferroptosis-associated genes during ED, which were further validated. Overall, we identified ferroptosis-related genes to predict their correlations in ED. Exploring the underlying mechanisms of ferroptosis may help us better understand pathophysiological changes in ED and provide new evidence for clinical transformation.
已观察到铁死亡在红细胞分化(ED)过程中起重要作用。然而,ED中的生物学基因标志物和铁死亡机制仍不清楚。我们从基因表达综合数据库下载了人脐带血来源的CD34细胞中ED的数据集。利用中位分化时间,将样本分为长组和短组。使用差异表达分析筛选差异表达的铁死亡相关基因(DE-FRGs)。进行了富集分析和蛋白质-蛋白质相互作用(PPI)网络分析。为了预测ED阶段,使用最小绝对收缩和选择算子(LASSO)构建了逻辑回归模型。总体而言,共鉴定出22个DE-FRGs。利用基因本体论和京都基因与基因组百科全书对铁死亡相关通路进行了富集。基因集富集分析和基因集变异分析显示DE-FRGs主要参与JAK-STAT、MAPK、PI3K-AKT-mTORC1、WNT和NOTCH信号通路。通过PPI分析获得了10个枢纽DE-FRGs。此外,我们构建了mRNA-微小RNA(miRNA)和mRNA-转录因子网络。在ED过程中,免疫细胞浸润水平存在显著差异。LASSO回归分析使用与ED阶段相关的6个DE-FRGs建立了一个特征模型。生物信息学分析确定了ED期间与铁死亡相关的基因,并进一步进行了验证。总体而言,我们鉴定了与铁死亡相关的基因,以预测它们在ED中的相关性。探索铁死亡的潜在机制可能有助于我们更好地理解ED中的病理生理变化,并为临床转化提供新的证据。