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| 基于GEO数据库分析脓毒症相关性死亡的潜在差异表达基因和微小RNAs |
| Analysis of potential differently expressed genes and miRNAs for sepsis-associated mortality based on GEO database |
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| DOI：10.12089/jca.2024.11.012 |
| 中文关键词: GEO数据库 脓毒症相关性死亡 差异表达基因 微小RNAs |
| 英文关键词: GEO database Sepsis-associated mortality Differential expression genes MicroRNAs |
| 基金项目:国家自然科学基金重大项目（T2293730，T2293734） |
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| 中文摘要: |
目的： 基于基因表达数据库（GEO）运用生物信息学筛选与脓毒症相关性死亡相关的潜在差异表达基因和微小RNAs（miRNAs）。
方法： 从GEO数据库下载人类血液样本基因表达谱芯片数据集GSE48080和GSE54514，选择两个时点（诊断脓毒症时、脓毒症病程中），使用GEO2R在线工具对脓毒症存活患者和非存活患者进行差异表达基因（DEGs）筛选。通过基因本体（GO）分析和京都基因与基因组百科全书（KEGG）通路富集分析，研究脓毒症相关性死亡DEGs涉及的病理生理过程和潜在信号通路。使用STRING在线工具构建DEGs蛋白-蛋白相互作用（PPI），使用Cytoscape软件构建PPI网络拓扑，使用CytoHubba工具筛选枢纽（Hub）基因。使用NetworkAnalyst构建Hub基因的目标miRNAs，使用RT-qPCR验证本院脓毒症存活患者和非存活患者相关基因表达变化。
结果： 在脓毒症病程中，脓毒症存活患者和非存活患者基因表达呈现异质性，共筛选出15个DEGs。KEGG通路富集分析显示，金黄色葡萄球菌感染、NOD样受体信号通路、硫代谢和集管酸分泌四个途径存在显著富集。PPI和CytoHubba分析筛选出10个Hub基因（SLC4A1、EPB42、LTF、LCN2、DEFA4、HBM、HBG1、GMPR、CAMP、OLFM4）。NetworkAnalyst分析预测了10个关键miRNAs。RT-qPCR验证结果显示,5个Hub基因（SLC4A1、EPB42、LCN2、DEFA4、OLFM4）与上述分析中的趋势一致。
结论： 基于GEO数据库的生物信息学分析，脓毒症存活患者和非存活患者在脓毒症病程中存在差异表达基因，为探索脓毒症相关性死亡的生物标志物提供了数据支持。 |
| 英文摘要: |
Objective: To identify the potential differently expressed genes and microRNAs (miRNAs) in sepsis survivors and non-survivors through bioinformatics-based research based on gene expression omnibus (GEO).
Methods: Two gene expression profile microarray datasets of human blood samples (GSE48080 and GSE54514) were downloaded from the GEO database. The differential expression genes (DEGs) between sepsis survivors and non-survivors at two time points (diagnosis of sepsis, course of sepsis) were screened with the GEO2R online tool. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were used to study the pathophysiological processes and potential signaling pathways involved in sepsis related death DEGs. STRING online tool was used to construct the DEGs protein-protein interaction (PPI). Cytoscape with CytoHubba was used to investigate the potential hub genes. NetworkAnalyst was used to construct targeted miRNAs of the hub genes. Real-time quantitative PCR (RT-qPCR) was established to evaluate the expression of potential hub genes in our sepsis survivors and non-survivors.
Results: During the course of sepsis, there was heterogeneity in gene expression between sepsis survivors and non-survivors. Fifteen DEGs were found to be remarkably differentially expressed between sepsis survivors and non-survivors during the course of sepsis. Four KEGG pathways, including staphylococcus aureus infection, NOD-like receptor signaling pathway, sulfur metabolism and collecting duct acid secretion, were significantly enriched. In combination with the results of the PPI network and CytoHubba, ten hub genes (SLC4A1, EPB42, LTF, LCN2, DEFA4, HBM, HBG1, GMPR, CAMP, OLFM4) were selected as potential biomarkers for sepsis-associated mortality. With NetworkAnalyst analysis, ten miRNAs were predicted as potential key miRNAs. RT-qPCR confirmed that the expressions of five of these genes (SLC4A1, EPB42, LCN2, DEFA4, OLFM4) were in accordance with the microarray results.
Conclusion: Bioinformatics analysis based on GEO database showed DEGs between sepsis suvivors and non-survivors in the course of sepsis, which contributed to identification of potential biomarkers and risk factors for sepsis-associated mortality. |
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