Sun Jing, Shi Run, Zhou Zhaokai, Xu Weilong, Huai Jiaxuan, Cao Yutian, Zhang Wenhui, Nie Lijuan, Wang Gaoxiang, Yan Qianhua, Wang Xuanbin, Li Minglun, Fang Zhuyuan, Zhou Xiqiao
Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China; The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Int J Biol Macromol. 2025 May;308(Pt 2):142315. doi: 10.1016/j.ijbiomac.2025.142315. Epub 2025 Mar 25.
In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive analysis of the metabolic landscape is urgently needed to categorize patients into distinct subgroups for precise treatment. In this study, a total of 806 NAFLD and 267 normal liver samples were comprehensively analyzed. Alterations in 114 metabolic pathways were investigated and two distinct metabolic clusters were identified. Single-cell RNA sequencing (scRNA-seq) analysis was utilized to decipher the metabolic activities within the microenvironment of NAFLD-derived liver cirrhosis. A refined fibrosis prediction model was developed using a Gaussian Mixture Model (GMM), demonstrating superior performance in fibrosis discrimination across multiple independent cohorts. Additionally, using The Cancer Genome Atlas (TCGA), CACNB1 protein was identified as a promising therapeutic target for hepatocellular carcinoma (HCC) patients with elevated metabolic dysfunction scores (MBDS). Machine learning algorithms were applied to MBDS-related genes to select an optimal prognostic model for HCC. All the models were trained in an HCC cohort obtained from the Gene Expression Omnibus (GEO), and the best model was validated in two independent HCC datasets: the TCGA-HCC cohort and LIRI-JP cohort. Overall, we provide insights of metabolic molecular subtyping and its potential clinical applicability in risk stratification for NAFLD and HCC individuals.
除了对非酒精性脂肪性肝病（NAFLD）进行组织学评估外，迫切需要对代谢格局进行全面分析，以便将患者分类为不同的亚组，从而进行精准治疗。在本研究中，对总共806份NAFLD样本和267份正常肝脏样本进行了全面分析。研究了114条代谢途径的改变，并确定了两个不同的代谢簇。利用单细胞RNA测序（scRNA-seq）分析来解读NAFLD衍生的肝硬化微环境中的代谢活动。使用高斯混合模型（GMM）开发了一种精细的纤维化预测模型，该模型在多个独立队列的纤维化鉴别中表现出卓越性能。此外，利用癌症基因组图谱（TCGA），将CACNB1蛋白确定为代谢功能障碍评分（MBDS）升高的肝细胞癌（HCC）患者的一个有前景的治疗靶点。将机器学习算法应用于与MBDS相关的基因，以选择HCC的最佳预后模型。所有模型均在从基因表达综合数据库（GEO）获得的一个HCC队列中进行训练，并且在两个独立的HCC数据集（TCGA-HCC队列和LIRI-JP队列）中对最佳模型进行了验证。总体而言，我们提供了代谢分子亚型分类及其在NAFLD和HCC个体风险分层中的潜在临床适用性的见解。
