Jin Zhangliu, Cao Jianyun, Liu Zhaoxun, Gao Mei, Liu Hailan
Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China.
Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, Hunan, 410005, China.
Metabol Open. 2025 Apr 15;26:100366. doi: 10.1016/j.metop.2025.100366. eCollection 2025 Jun.
The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing, with an incompletely understood pathophysiology involving multiple factors, particularly innate and adaptive immune responses. Given the limited pharmacological treatments available, identification of novel immune metabolic targets is urgently needed. In this study, we aimed to identify hub immune-related genes and potential biomarkers with diagnostic and predictive value for MASH patients.
The GSE164760 dataset from the Gene Expression Omnibus was utilized for analysis, and the R package was used to identify differentially expressed genes. Immune-related differentially expressed genes (IR-DEGs) were identified by comparing the overlap of differentially expressed genes with well-known immune-related genes. Furthermore, the biological processes and molecular functions of the IR-DEGs were analyzed. To characterize the hub IR-DEGs, we employed a protein-protein interaction network. The diagnostic and predictive values of these hub IR-DEGs in MASH were confirmed using GSE48452 and GSE63067 datasets. Finally, the significance of the hub IR-DEGs was validated using a mouse model of MASH.
A total of 91 IR-DEGs were identified, with 61 upregulated and 30 downregulated genes. Based on the protein-protein interaction network, FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3 were identified as the hub IR-DEGs. Moreover, we found that these hub genes are closely correlated with immune cells. Notably, the validation across two independent cohorts as well as a murine MASH model confirmed their high diagnostic potential.
The hub IR-DEGs, such as FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3, may enhance the diagnosis and prognosis of MASH by modulating immune homeostasis.
代谢功能障碍相关脂肪性肝炎（MASH）的发病率正在上升，其病理生理学涉及多种因素，尤其是先天性和适应性免疫反应，目前尚未完全了解。鉴于可用的药物治疗有限，迫切需要确定新的免疫代谢靶点。在本研究中，我们旨在确定对MASH患者具有诊断和预测价值的核心免疫相关基因和潜在生物标志物。
利用基因表达综合数据库中的GSE164760数据集进行分析，并使用R软件包识别差异表达基因。通过比较差异表达基因与知名免疫相关基因的重叠来识别免疫相关差异表达基因（IR-DEG）。此外，还分析了IR-DEG的生物学过程和分子功能。为了表征核心IR-DEG，我们构建了蛋白质-蛋白质相互作用网络。使用GSE48452和GSE63067数据集确认了这些核心IR-DEG在MASH中的诊断和预测价值。最后，使用MASH小鼠模型验证了核心IR-DEG的重要性。
共鉴定出91个IR-DEG，其中61个基因上调，30个基因下调。基于蛋白质-蛋白质相互作用网络，确定FN1、RHOA、FOS、PDGFRα、CCND1、PIK3R1、CSF1和FGF3为核心IR-DEG。此外，我们发现这些核心基因与免疫细胞密切相关。值得注意的是，在两个独立队列以及小鼠MASH模型中的验证证实了它们具有很高的诊断潜力。
核心IR-DEG，如FN1、RHOA、FOS、PDGFRα、CCND1、PIK3R1、CSF1和FGF3，可能通过调节免疫稳态来提高MASH的诊断和预后。
