School of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Beijing Key Laboratory of TCM Collateral Disease Theory Research, Beijing, 100069, China.
Sci Rep. 2022 Aug 18;12(1):14052. doi: 10.1038/s41598-022-18396-1.
Acute-on-chronic liver failure (ACLF) is a critical and refractory disease and a hepatic disorder accompanied by immune dysfunction. Thus, it is essential to explore key immune-related genes of ACLF and investigate its mechanisms. We used two public datasets (GSE142255 and GSE168048) to perform various bioinformatics analyses, including WGCNA, CIBERSORT, and GSEA. We also constructed an ACLF immune-related protein-protein interaction (PPI) network to obtain hub differentially expressed genes (DEGs) and predict corresponding miRNAs. Finally, an ACLF rat model was established to verify the results. A total of 388 DEGs were identified in ACLF, including 162 upregulated and 226 downregulated genes. The enrichment analyses revealed that these DEGs were mainly involved in inflammatory-immune responses and biosynthetic metabolic pathways. Twenty-eight gene modules were obtained using WGCNA and the coral1 and darkseagreen4 modules were highly correlated with M1 macrophage polarization. As a result, 10 hub genes and 2 miRNAs were identified to be significantly altered in ACLF. The bioinformatics analyses of the two datasets presented valuable insights into the pathogenesis and screening of hub genes of ACLF. These results might contribute to a better understanding of the potential molecular mechanisms of ACLF. Finally, further studies are required to validate our current findings.
急性肝衰竭(ACLF)是一种严重且难治的疾病,是一种伴有免疫功能障碍的肝脏疾病。因此,探索 ACLF 的关键免疫相关基因及其机制至关重要。我们使用了两个公共数据集(GSE142255 和 GSE168048)进行了各种生物信息学分析,包括 WGCNA、CIBERSORT 和 GSEA。我们还构建了 ACLF 免疫相关的蛋白质-蛋白质相互作用(PPI)网络,以获得枢纽差异表达基因(DEGs)并预测相应的 miRNAs。最后,建立了 ACLF 大鼠模型来验证结果。在 ACLF 中鉴定出 388 个 DEGs,包括 162 个上调基因和 226 个下调基因。富集分析表明,这些 DEGs 主要参与炎症免疫反应和生物合成代谢途径。通过 WGCNA 获得了 28 个基因模块,coral1 和 darkseagreen4 模块与 M1 巨噬细胞极化高度相关。结果,鉴定出 10 个枢纽基因和 2 个 miRNA 在 ACLF 中显著改变。这两个数据集的生物信息学分析为 ACLF 的发病机制和枢纽基因的筛选提供了有价值的见解。这些结果可能有助于更好地理解 ACLF 的潜在分子机制。最后,需要进一步的研究来验证我们目前的发现。