基于 RNA 甲基化基因的脓毒症关键生物标志物的鉴定和验证。
Identification and validation of key biomarkers based on RNA methylation genes in sepsis.
机构信息
Department of Internal Emergency Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
School of Medicine, Tongji University, Shanghai, China.
出版信息
Front Immunol. 2023 Aug 28;14:1231898. doi: 10.3389/fimmu.2023.1231898. eCollection 2023.
BACKGROUND
RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis.
METHODS
Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis.
RESULTS
Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed and , with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed , and was featured by the regulation of mRNA stability and amino acid metabolism. and were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (, , , and ) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of , , and were upregulated, while was downregulated in septic patients. The area under the ROC curve (AUC) of , , , and was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively.
CONCLUSIONS
Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.
背景
RNA 甲基化与免疫调节密切相关，但在脓毒症中的作用尚不清楚。在这里，我们旨在研究 RNA 甲基化相关基因（RMGs）在分类和诊断脓毒症中的作用。
方法
使用五种类型的 RMG（m1A、m5C、m6Am、m7G 和 Ψ）基于从 GEO 数据库（GSE57065、GSE65682 和 GSE95233）获得的基因表达谱数据，来识别脓毒症亚群。使用无监督聚类分析来识别不同的 RNA 修饰亚型。使用 CIBERSORT、WGCNA、GO 和 KEGG 分析来探索每个聚类的免疫浸润模式和生物学功能。使用 RF、SVM、XGB 和 GLM 算法来识别脓毒症中的诊断性 RMG。最后，通过收集脓毒症患者的 PBMCs 并用 qRT-PCR 验证这五个关键 RMG 的表达水平，并结合临床数据使用 ROC 分析验证它们对脓毒症的诊断功效。
结果
脓毒症分为三个亚型（簇 1 到 3）。簇 1 高度表达 和 ，其特征为中性粒细胞激活和 MAPK 信号通路的上调。簇 2 高度表达 ，其特征为调节 mRNA 稳定性和氨基酸代谢。簇 3 中上调 ，它参与核糖核蛋白复合物生物发生和碳水化合物代谢途径。此外，我们鉴定出五个 RMG（ 、 、 、 和 ）可以作为脓毒症临床诊断的生物标志物。为了验证，我们确定了脓毒症患者中 、 、 、 和 的相对表达上调，而 下调。 、 、 、 和 的 ROC 曲线下面积（AUC）分别为 0.828、0.707、0.846、0.834 和 0.976。
结论
我们的研究表明，RNA 甲基化基因（m1A、m5C、m6Am、m7G 和 Ψ）的失调与脓毒症的发病机制密切相关，为脓毒症的分类提供了新的见解。我们还发现五个关键 RMG 可以作为新的诊断生物标志物和潜在的治疗靶点。
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