基于转录组测序的脓毒症诱导的急性肺损伤雄性小鼠中与铁死亡相关基因的鉴定。

BACKGROUND: Sepsis can result in acute lung injury (ALI). Studies have shown that pharmacological inhibition of ferroptosis can treat ALI. However, the regulatory mechanisms of ferroptosis in sepsis-induced ALI remain unclear. METHODS: Transcriptome sequencing was performed on lung tissue samples from 10 sepsis-induced mouse models of ALI and 10 control mice. After quality control measures, clean data were used to screen for differentially expressed genes (DEGs) between the groups. The DEGs were then overlapped with ferroptosis-related genes (FRGs) to obtain ferroptosis-related DEGs (FR-DEGs). Subsequently, least absolute shrinkage and selection operator (Lasso) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were used to obtain key genes. In addition, Ingenuity Pathway Analysis (IPA) was employed to explore the disease, function, and canonical pathways related to the key genes. Gene set enrichment analysis (GSEA) was used to investigate the functions of the key genes, and regulatory miRNAs of key genes were predicted using the NetworkAnalyst and StarBase databases. Finally, the expression of key genes was validated with the GSE165226 and GSE168796 datasets sourced from the Gene Expression Omnibus (GEO) database and using quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Thirty-three FR-DEGs were identified between 1843 DEGs and 259 FRGs. Three key genes, Ncf2, Steap3, and Gclc, were identified based on diagnostic models established by the two machine learning methods. They are mainly involved in infection, immunity, and apoptosis, including lymphatic system cell migration and lymphocyte and T cell responses. Additionally, the GSEA suggested that Ncf2 and Steap3 were similarly enriched in mRNA processing, response to peptides, and leukocyte differentiation. Furthermore, a key gene-miRNA network including 2 key genes (Steap3 and Gclc) and 122 miRNAs, and a gene-miRNA network with 1 key gene (Steap3) and 3 miRNAs were constructed using NetworkAnalyst and StarBase, respectively. Both databases predicted that mmu-miR-15a-5p was the target miRNA of Steap3. Finally, Ncf2 expression was validated using both datasets and qRT-PCR, and Steap3 was validated using GSE165226 and qRT-PCR. CONCLUSIONS: This study identified two FR-DEGs (Ncf2 and Steap3) associated with sepsis-induced ALI via transcriptome analyses, as well as their functional and metabolic pathways.

背景：脓毒症可导致急性肺损伤（ALI）。研究表明，铁死亡的药理学抑制可以治疗 ALI。然而，脓毒症诱导的 ALI 中铁死亡的调节机制仍不清楚。

方法：对 10 例脓毒症诱导的 ALI 小鼠模型和 10 例对照小鼠的肺组织样本进行转录组测序。经过质量控制措施后，使用清洁数据筛选两组间差异表达基因（DEGs）。然后将 DEGs 与铁死亡相关基因（FRGs）重叠，得到铁死亡相关 DEGs（FR-DEGs）。随后，采用最小绝对收缩和选择算子（Lasso）和支持向量机-递归特征消除（SVM-RFE）获得关键基因。此外，采用Ingenuity Pathway Analysis（IPA）分析与关键基因相关的疾病、功能和经典途径。使用基因集富集分析（GSEA）研究关键基因的功能，并使用 NetworkAnalyst 和 StarBase 数据库预测关键基因的调控 miRNA。最后，使用基因表达综合数据库（GEO）数据库中的 GSE165226 和 GSE168796 数据集以及实时定量聚合酶链反应（qRT-PCR）验证关键基因的表达。

结果：在 1843 个 DEGs 和 259 个 FRGs 之间鉴定出 33 个 FR-DEGs。基于两种机器学习方法建立的诊断模型，鉴定出 3 个关键基因，Ncf2、Steap3 和 Gclc。它们主要参与感染、免疫和细胞凋亡，包括淋巴系统细胞迁移和淋巴细胞及 T 细胞反应。此外，GSEA 表明 Ncf2 和 Steap3 在 mRNA 加工、肽反应和白细胞分化中具有相似的富集。此外，使用 NetworkAnalyst 和 StarBase 分别构建了包括 2 个关键基因（Steap3 和 Gclc）和 122 个 miRNA 的关键基因-miRNA 网络，以及包括 1 个关键基因（Steap3）和 3 个 miRNA 的基因-miRNA 网络。两个数据库均预测 mmu-miR-15a-5p 是 Steap3 的靶 miRNA。最后，使用 GSE165226 和 qRT-PCR 验证了 Ncf2 的表达，使用 GSE165226 和 qRT-PCR 验证了 Steap3 的表达。

结论：本研究通过转录组分析鉴定了两个与脓毒症诱导的 ALI 相关的 FR-DEGs（Ncf2 和 Steap3），以及它们的功能和代谢途径。