Zhang Jie, Wang Wei, Wang Xiao-Qing, Hao Hai-Rong, Hu Wen, Ding Zong-Li, Dong Li, Liang Hui, Zhang Yi-Yuan, Kong Lian-Hua, Xie Ying
Department of Endocrinology, Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China.
Department of Endocrinology and Metabolism, Huai'an Hospital Affiliated to Xuzhou Medical University, Huai'an, 223002, Jiangsu, China.
Hepatol Int. 2025 Apr 9. doi: 10.1007/s12072-025-10792-9.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a critical health concern, with metabolic dysfunction-associated steatohepatitis (MASH) representing a severe subtype that poses significant risks. This study aims to develop a robust diagnostic model for high-risk MASH utilizing a multi-omics approach.
We initiated proteomic analysis to select differential proteins, followed by liver transcriptional profiling to localize these proteins. An intersection of differential proteins and liver-expressed genes facilitated the identification of candidate biomarkers. Subsequently, scRNA-seq data helped ascertain the subcellular localization of these biomarkers in kupffer cells. We then established two MASLD models to investigate the co-localization of F4/80 and the target proteins in Kupffer cells using immunofluorescence dual-labeling. Correlation analyses were performed using blood samples from a discovery cohort of 144 individuals with liver pathology to validate the relationships between candidate biomarkers and MASLD phenotypes. Using LASSO regression, we established the ABD-LTyG predictive model for high-risk MASH (NAS ≥ 4 + F ≥ 2) and validated its efficacy in an independent cohort of 171 individuals. Finally, we compared this model against three classic non-invasive liver fibrosis diagnostic methods.
A proteo-transcriptomic comparison identified 58 consistent biomarkers in plasma and liver, with 25 closely associated with MASLD phenotype. Utilizing single-cell data and the HPA database, we delineated the localization of these biomarkers in liver cells, identifying TREM2, IL18BP, and LGALS3BP predominantly in the Kupffer cell subpopulation. Validation in animal models confirmed elevated expression and cellular localization of TREM2, IL18BP, and LGALS3BP in MASLD. To enhance diagnostic capability, we integrated clinical characteristics using LASSO regression to develop the ABD-LTyG model, comprising AST, BMI, total bilirubin (TB), vitamin D, TyG, and the biomarkers LGALS3BP and TREM2. This model demonstrated an AUC of 0.832 (95% CI 0.753-0.911) in the discovery cohort and 0.807 (95% CI 0.742-0.872) in the validation cohort for diagnosing high-risk MASH, outperforming traditional assessments such as FIB-4, NFS, and APRI.
The integration of circulating biomarkers and clinical parameters into the ABD-LTyG model offers a promising approach for diagnosing high-risk MASH. This study underscores the importance of multi-omics strategies in enhancing diagnostic accuracy and guiding clinical decision-making.
代谢功能障碍相关脂肪性肝病（MASLD）是一个严重的健康问题，其中代谢功能障碍相关脂肪性肝炎（MASH）是一种严重的亚型，具有重大风险。本研究旨在利用多组学方法开发一种用于高危MASH的强大诊断模型。
我们启动蛋白质组学分析以选择差异蛋白，随后进行肝脏转录谱分析以定位这些蛋白。差异蛋白与肝脏表达基因的交集有助于鉴定候选生物标志物。随后，单细胞RNA测序（scRNA-seq）数据有助于确定这些生物标志物在库普弗细胞中的亚细胞定位。然后我们建立了两个MASLD模型，使用免疫荧光双标记研究F4/80与库普弗细胞中靶蛋白的共定位。使用来自144例有肝脏病理的个体的发现队列的血液样本进行相关性分析，以验证候选生物标志物与MASLD表型之间的关系。使用套索回归，我们建立了用于高危MASH（NAS≥4 + F≥2）的ABD-LTyG预测模型，并在171例个体的独立队列中验证了其有效性。最后，我们将该模型与三种经典的非侵入性肝纤维化诊断方法进行了比较。
蛋白质组-转录组比较在血浆和肝脏中鉴定出58个一致的生物标志物，其中25个与MASLD表型密切相关。利用单细胞数据和人类蛋白质图谱（HPA）数据库，我们描绘了这些生物标志物在肝细胞中的定位，确定TREM2、IL18BP和LGALS3BP主要在库普弗细胞亚群中。在动物模型中的验证证实了TREM2、IL18BP和LGALS3BP在MASLD中的表达升高和细胞定位。为了提高诊断能力，我们使用套索回归整合临床特征以开发ABD-LTyG模型，该模型包括AST、BMI、总胆红素（TB）、维生素D、TyG以及生物标志物LGALS3BP和TREM2。该模型在发现队列中诊断高危MASH的AUC为0.832（95%CI 0.753 - 0.911），在验证队列中为0.807（95%CI 0.742 - 0.872），优于FIB-4、NFS和APRI等传统评估方法。
将循环生物标志物和临床参数整合到ABD-LTyG模型中为诊断高危MASH提供了一种有前景的方法。本研究强调了多组学策略在提高诊断准确性和指导临床决策方面的重要性。
