Zhu Yalan, Zhang He, Jiang Pengjun, Xie Chengxia, Luo Yao, Chen Jie
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Biomedicines. 2023 Mar 21;11(3):970. doi: 10.3390/biomedicines11030970.
Non-alcoholic fatty liver disease (NAFLD) encompasses a broad spectrum of conditions from simple steatosis (non-alcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH), and its global prevalence continues to rise. NASH, the progressive form of NAFLD, has higher risks of liver and non-liver related adverse outcomes compared with those patients with NAFL alone. Therefore, the present study aimed to explore the mechanisms in the progression of NAFLD and to develop a model to diagnose NASH based on the transcriptome and epigenome. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) among the three groups (normal, NAFL, and NASH) were identified, and the functional analysis revealed that the development of NAFLD was primarily related to the oxidoreductase-related activity, PPAR signaling pathway, tight junction, and pathogenic infection. The logistic regression (LR) model, consisting of , , and , outperformed the other five models. With the highest AUC (0.8819, 95%CI: 0.8128-0.9511) and a sensitivity of 97.87%, as well as a specificity of 64.71%, the LR model was determined as the diagnostic model, which can differentiate NASH from NAFL. In conclusion, several potential mechanisms were screened out based on the transcriptome and epigenome, and a diagnostic model was built to help patient stratification for NAFLD populations.
非酒精性脂肪性肝病（NAFLD）涵盖了从单纯性脂肪变性（非酒精性脂肪肝（NAFL））到非酒精性脂肪性肝炎（NASH）的广泛病症，其全球患病率持续上升。与仅患有NAFL的患者相比，NASH作为NAFLD的进展形式，具有更高的肝脏及非肝脏相关不良结局风险。因此，本研究旨在探索NAFLD进展的机制，并基于转录组和表观基因组开发一种诊断NASH的模型。鉴定了三组（正常、NAFL和NASH）之间的差异表达基因（DEGs）和差异甲基化基因（DMGs），功能分析表明NAFLD的发展主要与氧化还原酶相关活性、PPAR信号通路、紧密连接和病原体感染有关。由 、 和 组成的逻辑回归（LR）模型优于其他五个模型。LR模型的AUC最高（0.8819，95%CI：0.8128 - 0.9511），敏感性为97.87%，特异性为64.71%，被确定为诊断模型，可区分NASH和NAFL。总之，基于转录组和表观基因组筛选出了几种潜在机制，并建立了诊断模型以帮助对NAFLD人群进行患者分层。
