Queen Mary School, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
The National Engineering Research Center for Bioengineering Drugs and the Technologies, Institute of Translational Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Medicine (Baltimore). 2024 Feb 23;103(8):e37185. doi: 10.1097/MD.0000000000037185.
The development of nonalcoholic fatty liver disease (NAFLD) has been reported to be caused by sphingolipid family inducing insulin resistance, mitochondrial dysfunction, and inflammation, which can be regulated by multiple sphingolipid metabolic pathways. This study aimed to explore the molecular mechanism of crucial sphingolipid metabolism related genes (SMRGs) in NAFLD. Firstly, the datasets (GSE48452, GSE126848, and GSE63067) from the Gene Expression Omnibus database and sphingolipid metabolism genes (SMGs) from previous research were collected for this study. The differentially expressed genes (DEGs) between different NAFLD and controls were acquired through "limma," and the SMRGs were authenticated via weighted gene co-expression network analysis (WGCNA). After overlapping the DEGs and SMRGs, the causality between the intersection genes (DE-SMRGs) and NAFLD was explored to sort out the candidate biomarkers by Mendelian randomization (MR) study. The receiver operating characteristic (ROC) curves of candidate biomarkers in GSE48452 and GSE126848 were yielded to determine the biomarkers, followed by the nomogram construction and enrichment analysis. Finally, the immune infiltration analysis, the prediction of transcription factors (TFs) and drugs targeting biomarkers were put into effect. A total of 23 DE-SMRGs were acquired based on the differential analysis and weighted gene co-expression network analysis (WGCNA), of which 3 DE-SMRGs (CD37, CXCL9 and IL7R) were picked out for follow-up analysis through univariate and multivariate MR analysis. The values of area under ROC curve of CD37 and CXCL9 were >0.7 in GSE48452 and GSE126848, thereby being regarded as biomarkers, which were mainly enriched in amino acid metabolism. With respect to the Spearman analysis between immune cells and biomarkers, CD37 and CXCL9 were significantly positively associated with M1 macrophages (P < .001), whose proportion was observably higher in NAFLD patients compared with controls. At last, TFs (ZNF460 and ZNF384) of CD37 and CXCL9 and a total of 79 chemical drugs targeting CD37 and CXCL9 were predicted. This study mined the pivotal SMRGs, CD37 and CXCL9, and systematically explored the mechanism of action of both biomarkers based on the public databases, which could tender a fresh reference for the clinical diagnosis and therapy of NAFLD.
非酒精性脂肪性肝病 (NAFLD) 的发展据报道是由鞘脂家族诱导的胰岛素抵抗、线粒体功能障碍和炎症引起的，这些可以通过多种鞘脂代谢途径进行调节。本研究旨在探讨与 NAFLD 相关的关键鞘脂代谢相关基因 (SMRGs) 的分子机制。首先，从基因表达综合数据库中收集了数据集 (GSE48452、GSE126848 和 GSE63067) 和之前研究中的鞘脂代谢基因 (SMGs)。通过“limma”获取不同 NAFLD 和对照之间的差异表达基因 (DEGs)，并通过加权基因共表达网络分析 (WGCNA) 验证 SMRGs。重叠 DEGs 和 SMRGs 后，通过孟德尔随机化 (MR) 研究探索交集基因 (DE-SMRGs) 与 NAFLD 之间的因果关系，以整理候选生物标志物。在 GSE48452 和 GSE126848 中对候选生物标志物进行 ROC 曲线分析，以确定生物标志物，然后构建列线图并进行富集分析。最后，进行免疫浸润分析、生物标志物的转录因子 (TFs) 预测和药物靶点预测。通过差异分析和加权基因共表达网络分析 (WGCNA)，共获得 23 个 DE-SMRGs，其中 3 个 DE-SMRGs (CD37、CXCL9 和 IL7R) 通过单变量和多变量 MR 分析进行了后续分析。CD37 和 CXCL9 在 GSE48452 和 GSE126848 中的 ROC 曲线下面积值均大于 0.7，因此被视为生物标志物，主要富集在氨基酸代谢中。关于免疫细胞与生物标志物的 Spearman 分析，CD37 和 CXCL9 与 M1 巨噬细胞呈显著正相关(P<.001)，NAFLD 患者中 M1 巨噬细胞的比例明显高于对照组。最后，预测了 CD37 和 CXCL9 的 TFs (ZNF460 和 ZNF384) 和针对 CD37 和 CXCL9 的共 79 种化学药物。本研究基于公共数据库挖掘了关键的 SMRGs，CD37 和 CXCL9，并系统地探讨了这两个生物标志物的作用机制，可为 NAFLD 的临床诊断和治疗提供新的参考。
