Liu Zheng, Wang Xiaohong, Xiu Mingzhu, Luo Rui, Shi Xiaomin, Wang Yizhou, Ye Yusong, Wang Ruiyu, Liu Sha, Lv Muhan, Tang Xiaowei
Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China.
Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China.
Sci Rep. 2025 Aug 1;15(1):28177. doi: 10.1038/s41598-025-13477-3.
Non-alcoholic fatty liver disease (NAFLD) is a global health challenge with complex pathogenesis and limited diagnostic biomarkers. Palmitoylation, a post-translational modification, has emerged as a critical regulator in metabolic disorders, yet its role in NAFLD remains underexplored. This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. Transcriptomic datasets from human liver tissues were analyzed to identify differentially expressed genes (DEGs) and co-expression modules via WGCNA. Intersection analysis revealed 60 palmitoylation-related DEGs (PR-DEGs). Seven machine learning models were employed, with Neural Network (NNET) and Decision Tree (DT) outperforming others, identifying three hub genes: TYMS, WNT5A, and ZFP36. A nomogram integrating these genes demonstrated robust diagnostic accuracy (AUC = 0.976). The pivotal role of these genes in diagnosing NAFLD was confirmed using the validation dataset (AUC = 0.903). Functional enrichment linked these genes to TNF signaling, lipid metabolism, and immune pathways. Single-cell RNA-seq analysis highlighted their expression in hepatocytes and immune cells, with altered intercellular communication patterns. Immune infiltration analysis revealed significant shifts in monocytes, dendritic cells, and macrophages in NAFLD. Regulatory network analysis highlighted that hsa-let-7b-5p might be pivotal co-regulator of the three hub gene expressions. Finally, the top 10 potential gene-targeted drugs were screened. This study unveils novel palmitoylation-related biomarkers and provides insights into NAFLD pathogenesis, offering diagnostic and therapeutic avenues.
The online version contains supplementary material available at 10.1038/s41598-025-13477-3.
非酒精性脂肪性肝病（NAFLD）是一项全球性的健康挑战，其发病机制复杂，诊断生物标志物有限。棕榈酰化是一种翻译后修饰，已成为代谢紊乱的关键调节因子，但其在NAFLD中的作用仍未得到充分研究。本研究整合生物信息学分析和机器学习，以识别NAFLD的棕榈酰化相关生物标志物。通过加权基因共表达网络分析（WGCNA）对来自人类肝脏组织的转录组数据集进行分析，以识别差异表达基因（DEG）和共表达模块。交集分析揭示了60个棕榈酰化相关的差异表达基因（PR-DEG）。采用了七种机器学习模型，其中神经网络（NNET）和决策树（DT）表现优于其他模型，确定了三个核心基因：胸苷酸合成酶（TYMS）、无翅型MMTV整合位点家族成员5A（WNT5A）和锌指蛋白36（ZFP36）。整合这些基因的列线图显示出强大的诊断准确性（曲线下面积[AUC]=0.976）。使用验证数据集确认了这些基因在诊断NAFLD中的关键作用（AUC=0.903）。功能富集将这些基因与肿瘤坏死因子（TNF）信号传导、脂质代谢和免疫途径联系起来。单细胞RNA测序分析突出了它们在肝细胞和免疫细胞中的表达，以及细胞间通讯模式的改变。免疫浸润分析显示NAFLD中单核细胞、树突状细胞和巨噬细胞有显著变化。调控网络分析突出表明，hsa-let-7b-5p可能是这三个核心基因表达的关键共调节因子。最后，筛选出了前10种潜在的基因靶向药物。本研究揭示了新的棕榈酰化相关生物标志物，并为NAFLD发病机制提供了见解，为诊断和治疗提供了途径。
在线版本包含可在10.1038/s41598-025-13477-3获取的补充材料。
