Objectives: To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes.
Methods: The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the 'limma' package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs. Ferroptosis- and cuproptosis-related genes were obtained from the FerrDb V2 database and available literatures, respectively. A combined signature for cuproptosis- and ferroptosis-related genes, called CRF, was constructed using the STRING database. Hub genes were identified by overlapping DEGs, WGCNA-derived key genes, and combined signature CRF genes, and validated using the GSE109836 and GSE227714 datasets and real-time quantitative polymerase chain reaction. A nomogram of NASH diagnostic model was established utilizing the 'rms' package in R software based on the hub genes, and the diagnostic value of hub genes was assessed using receiver operating characteristic curve analysis. In addition, immune cell infiltration in NASH versus healthy controls was examined using the CIBERSORT algorithm. The relationships among various infiltrated immune cells were explored with Spearman's correlation analysis.
Results: Analysis of GSE89632 identified 236 DEGs between the NASH group and the healthy group. WGCNA highlighted 8 significant modules and 11,095 pivotal genes, of which 330 genes constituted CRF. Intersection analysis identified IL6, IL1B, JUN, NR4A1, and PTGS2 as hub genes. The hub genes were all downregulated in the NASH group, and this result was further verified by the NASH validation dataset and real-time quantitative polymerase chain reaction. Receiver operating characteristic curve analysis confirmed the diagnostic efficacy of these hub genes with areas under the curve of 0.985, 0.941, 1.000, 0.967, and 0.985, respectively. Immune infiltration assessment revealed that gamma delta T cells, M1 macrophages, M2 macrophages, and resting mast cells were predominantly implicated.
Conclusions: Our investigation underscores the significant association of cuproptosis- and ferroptosis-related genes, specifically IL6, IL1B, JUN, NR4A1, and PTGS2, with NASH. These findings offer novel insights into the pathogenesis of NASH, potentially guiding future diagnostic and therapeutic strategies.
目的: 筛选参与非酒精性脂肪肝铜死亡和铁死亡过程的核心基因，并确定其对非酒精性脂肪肝的诊断价值。方法: 本研究从基因表达综合（Gene Expression Omnibus，GEO）数据库中下载非酒精性脂肪肝转录组数据集GSE89632，运用R软件包的"limma"以及加权基因共表达网络分析（weighted correlation network analysis，WGCNA）方法，筛选非酒精性脂肪性肝炎（non-alcoholic steatohepatitis，NASH）组和健康对照（healthy control，HC）组的差异表达基因（differentially expressed genes，DEGs）。采用基因本体（Gene Ontology，GO）和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG），对DEGs进行功能富集分析。从FerrDb V2数据库中提取铁死亡相关基因，并通过文献检索获取铜死亡相关基因。利用STRING数据库构建与铜死亡和铁死亡均相关的基因，命名为CRF（cuproptosis - and ferroptosis-related，CRF）基因。DEGs、WGCNA和CRF基因的交集作为核心基因，采用GSE109836和GSE227714数据集和实时定量聚合酶链反应（real-time quantitative polymerase chain reaction，RT-qPCR）验证核心基因核心基因的在NASH患者中的表达情况。利用R软件包的"rms"，基于核心基因构建非酒精性脂肪肝诊断模型的诺曼图，采用接受者操作特性曲线（receiver operating characteristic，ROC）分析评估核心基因对非酒精性脂肪肝的诊断价值。此外，采用CIBERSORT算法分型NASH组与HC组的免疫细胞浸润情况。采用Spearman相关性分析探讨各种浸润免疫细胞间的关系。结果: GSE89632数据集分析显示，NASH组与HC组之间存在236个DEGs。WGCNA分析揭示了8个显著模块和11，095个模块基因。其中330个为CRF基因。交集分析发现，IL6、IL1B、JUN、NR4A1和PTGS2为核心基因，它们在NASH患者的肝组织中均表达下调，此结果得到了验证数据集和RT-qPCR的验证。ROC曲线分析显示，这些基因对非酒精性脂肪肝具有良好的诊断效能，它们的曲线下面积分别为0.985、0.941、1.000、0.967和0.985。NASH组的γδT细胞、M1巨噬细胞、M2巨噬细胞和静息肥大细胞的浸润比例高于HC组。结论: IL6、IL1B、JUN、NR4A1和PTGS2是与非酒精性脂肪肝铜死亡和铁死亡相关的核心基因，此发现为深入理解非酒精性脂肪肝的发病机制提供了新见解，可能为疾病的诊断和治疗靶点提供新方向。.
Keywords: bioinformatics analysis; cuproptosis; ferroptosis; nonalcoholic fatty liver disease.