社区获得性肺炎所致脓毒症相关免疫基因的鉴定。
Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia.
机构信息
Department of Emergency, Minhang Hospital, Fudan University, No. 170 Xinsong Road, Minhang District, Shanghai 201199, China.
Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China.
出版信息
Comput Math Methods Med. 2021 Aug 26;2021:8020067. doi: 10.1155/2021/8020067. eCollection 2021.
BACKGROUND
Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis.
METHODS
Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes.
RESULTS
360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10, included in the models.
CONCLUSION
CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.
背景
免疫抑制在脓毒症发病机制中起着关键作用，因此寻找与免疫相关的标志物来治疗脓毒症具有重要意义。
方法
从 ArrayExpress 数据库中提取社区获得性肺炎（CAP）伴脓毒症的数据集。使用 Limma 包对 CAP 组和正常组之间的差异表达基因（DEGs）进行分析。通过 ESTIMATE 算法计算免疫评分后，在高免疫评分组和低免疫评分组之间选择 DEGs。对相交 DEGs 进行富集分析。进一步使用 Metascape 工具绘制相交 DEGs 的蛋白质-蛋白质相互作用（PPI）网络。通过 Biopython 模型在 NCBI PubMed 中搜索关键 DEGs 的相关文献，并通过 RT-qPCR 验证关键基因的表达。
结果
两组之间获得了 360 个相交的 DEGs（157 个上调和 203 个下调）。同时，相交的 DEGs 富集在 157 个免疫相关术语中。对 DEGs 的 PPI 进行了分析，得到了 8 个模型。在与脓毒症相关的研究中，有 8 个基因的度≥10，被纳入模型中。
结论
CXCR3、CCR7、HLA-DMA 和 GPR18 可能参与了 CAP 伴脓毒症的发病机制。
Zotero 插件