Ferroptosis-related differential genes.

Background
Neuronal ferroptosis is closely related to the disease of the nervous system, and the objective of the present study was to recognize and verify the potential ferroptosis-related genes to forecast the neurological outcome after cardiac arrest.
Methods
Cardiac Arrest-related microarray datasets GSE29540 and GSE92696 were downloaded from GEO and batch normalization of the expression data was performed using “sva” of the R package. GSE29540 was analyzed to identify DEGs. Venn diagram was applied to recognize ferroptosis-related DEGs from the DEGs. Subsequently, The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed, and PPI network was applied to screen hub genes. Receiver operating characteristic (ROC) curves were adopted to determine the predictive value of the biomarkers, and the GSE92696 dataset was applied to further evaluate the diagnostic efficacy of the biomarkers. We explore transcription factors and miRNAs associated with hub genes. The “CIBERSORT” package of R was utilized to analyse the proportion infiltrating immune cells. Finally, validated by a series of experiments at the cellular level.
Results
112 overlapping ferroptosis-related DEGs were further obtained via intersecting these DEGs and ferroptosis-related genes. The GO and KEGG analysis demonstrate that ferroptosis-related DEGs are mainly involved in response to oxidative stress, ferroptosis, apoptosis, IL-17 signalling pathway, autophagy, toll-like receptor signalling pathway. The top 10 hub genes were selected, including HIF1A, MAPK3, PPARA, IL1B, PTGS2, RELA, TLR4, KEAP1, SREBF1, SIRT6. Only MAPK3 was upregulated in both GSE29540 and GAE92696. The AUC values of the MAPK3 are 0.654 and 0.850 in GSE29540 and GSE92696 respectively. The result of miRNAs associated with hub genes indicates that hsa-miR-214-3p and hsa-miR-483-5p can regulate the expression of MAPK3. MAPK3 was positively correlated with naive B cells, macrophages M0, activated dendritic cells and negatively correlated with activated CD4 memory T cells, CD8 T cells, and memory B cells. Compared to the OGD4/R24 group, the OGD4/R12 group had higher MAPK3 expression at both mRNA and protein levels and more severe ferroptosis.
Conclusion
In summary, the MAPK3 ferroptosis-related gene could be used as a biomarker to predict the neurological outcome after cardiac arrest. Potential biological pathways provide novel insights into the pathogenesis of cardiac arrest.

背景
神经元铁死亡（neuronal ferroptosis）与神经系统疾病密切相关，本研究旨在识别并验证潜在铁死亡相关基因，以预测心脏骤停后的神经系统预后。
方法
从基因表达综合数据库（Gene Expression Omnibus, GEO）下载心脏骤停相关微阵列数据集GSE29540与GSE92696，采用R包"sva"对表达数据进行批次标准化处理。以GSE29540为分析数据集识别差异表达基因（differentially expressed genes, DEGs）。通过韦恩图从差异表达基因中筛选铁死亡相关差异表达基因。随后开展基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析，并借助蛋白质相互作用（Protein-Protein Interaction, PPI）网络筛选核心基因。采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估生物标志物的预测价值，同时使用GSE92696数据集进一步验证该生物标志物的诊断效能。本研究还探究了与核心基因相关的转录因子与微小RNA（microRNA, miRNA）。利用R语言的"CIBERSORT"包分析免疫细胞浸润比例。最后通过一系列细胞水平实验完成验证。
结果
通过将差异表达基因与铁死亡相关基因取交集，共获得112个重叠的铁死亡相关差异表达基因。GO与KEGG富集分析结果显示，铁死亡相关差异表达基因主要参与氧化应激应答、铁死亡、细胞凋亡、IL-17信号通路、自噬以及Toll样受体信号通路。筛选得到排名前十的核心基因，包括HIF1A、MAPK3、PPARA、IL1B、PTGS2、RELA、TLR4、KEAP1、SREBF1及SIRT6。仅MAPK3在GSE29540与GSE92696中均呈上调表达。MAPK3在GSE29540与GSE92696中的曲线下面积（Area Under Curve, AUC）分别为0.654与0.850。核心基因相关miRNA分析结果表明，hsa-miR-214-3p与hsa-miR-483-5p可调控MAPK3的表达。MAPK3与初始B细胞、M0型巨噬细胞、活化树突状细胞呈正相关，与活化CD4记忆性T细胞、CD8 T细胞以及记忆性B细胞呈负相关。相较于OGD4/R24组，OGD4/R12组的MAPK3 mRNA与蛋白表达水平均更高，且铁死亡程度更为严重。
结论
综上，MAPK3作为铁死亡相关基因，可作为预测心脏骤停后神经系统预后的生物标志物。潜在的生物学通路为心脏骤停的发病机制研究提供了全新视角。