Wang Bingyu, Yu Hongyang, Gao Jiawei, Yang Liuxin, Zhang Yali, Yuan Xingxing, Zhang Yang
Heilongjiang University of Chinese Medicine, Harbin, China.
Department of Gastroenterology, Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China.
Heliyon. 2024 Apr 23;10(9):e29860. doi: 10.1016/j.heliyon.2024.e29860. eCollection 2024 May 15.
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent liver disease worldwide and lack of research on the diagnostic utility of mitochondrial regulators in NAFLD. Mitochondrial dysfunction plays a pivotal role in the development and progression of NAFLD, especially oxidative stress and acidity β-oxidative overload. Thus, we aimed to identify and validate a panel of mitochondrial gene expression biomarkers for detection of NAFLD.
We selected the GSE89632 dataset and identified key mitochondrial regulators by intersecting DEGs, WGCNA modules, and MRGs. Classification of NAFLD subtypes based on these key mitochondrial regulatory factors was performed, and the pattern of immune system infiltration in different NAFLD subtypes were also investigated. RF, LASSO, and SVM-RFE were employed to identify possible diagnostic biomarkers from key mitochondrial regulatory factors and the predictive power was demonstrated through ROC curves. Finally, we validated these potential diagnostic biomarkers in human peripheral blood samples and a high-fat diet-induced NAFLD mouse model.
We identified 25 key regulators of mitochondria and two NAFLD subtypes with different immune infiltration patterns. Four potential diagnostic biomarkers (BCL2L11, NAGS, HDHD3, and RMND1) were screened by three machine learning methods thereby establishing the diagnostic model, which showed favorable predictive power and achieved significant clinical benefit at certain threshold probabilities. Then, through internal and external validation, we identified and confirmed that BCL2L11 was significantly downregulated in NAFLD, while the other three were significantly upregulated.
The four MRGs, namely BCL2L11, NAGS, HDHD3, and RMND1, are novel potential biomarkers for diagnosing NAFLD. A diagnostic model constructed using the four MRGs may aid early diagnosis of NAFLD in clinics.
非酒精性脂肪性肝病（NAFLD）是一种在全球范围内高度流行的肝脏疾病，目前缺乏关于线粒体调节因子在NAFLD诊断效用方面的研究。线粒体功能障碍在NAFLD的发生和发展中起关键作用，尤其是氧化应激和β-氧化过载。因此，我们旨在识别和验证一组用于检测NAFLD的线粒体基因表达生物标志物。
我们选择了GSE89632数据集，并通过整合差异表达基因（DEGs）、加权基因共表达网络分析（WGCNA）模块和线粒体调节基因（MRGs）来识别关键的线粒体调节因子。基于这些关键的线粒体调节因子对NAFLD亚型进行分类，并研究不同NAFLD亚型中免疫系统浸润的模式。采用随机森林（RF）、套索回归（LASSO）和支持向量机递归特征消除（SVM-RFE）从关键的线粒体调节因子中识别可能的诊断生物标志物，并通过ROC曲线证明其预测能力。最后，我们在人类外周血样本和高脂饮食诱导的NAFLD小鼠模型中验证了这些潜在的诊断生物标志物。
我们确定了25个关键的线粒体调节因子和两种具有不同免疫浸润模式的NAFLD亚型。通过三种机器学习方法筛选出四个潜在的诊断生物标志物（BCL2L11、NAGS、HDHD3和RMND1），从而建立了诊断模型，该模型显示出良好的预测能力，并在一定的阈值概率下取得了显著的临床效益。然后，通过内部和外部验证，我们确定并证实BCL2L11在NAFLD中显著下调，而其他三个则显著上调。
四个MRGs，即BCL2L11、NAGS、HDHD3和RMND1，是诊断NAFLD的新型潜在生物标志物。使用这四个MRGs构建的诊断模型可能有助于临床早期诊断NAFLD。
