Luo Zhenguo, Yan Shu, Chao Yu, Shen Ming
Department of Internal Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Department of Gastroenterology, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Heliyon. 2024 Mar 31;10(7):e28935. doi: 10.1016/j.heliyon.2024.e28935. eCollection 2024 Apr 15.
Non-alcoholic fatty liver disease (NAFLD) stands as a predominant chronic liver ailment globally, yet its pathogenesis remains elusive. This study aims to identify Hub mitophagy-related genes (MRGs), and explore the underlying pathological mechanisms through which these hub genes regulate NAFLD.
A total of 3 datasets were acquired from the GEO database and integrated to identify differentially expressed genes (DEGs) in NAFLD and perform Gene Set Enrichment Analysis (GSEA). By intersecting DEGs with MRGs, mitophagy-related differentially expressed genes (MRDEGs) were obtained. Then, hub MRGs with diagnostic biomarker capability for NAFLD were screened and a diagnostic prediction model was constructed and assessed using Nomogram, Decision Curve Analysis (DCA), and ROC curves. Functional enrichment analysis was conducted on the identified hub genes to explore their biological significance. Additionally, regulatory networks were constructed using databases. NAFLD was stratified into high and low-risk groups based on the Riskscore from the diagnostic prediction model. Furthermore, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were employed to analyze immune cell infiltration patterns and the relationship between Hub MRGs and immune cells.
The integrated dataset comprised 122 NAFLD samples and 31 control samples. After screening, 18 MRDEGs were identified. Subsequently, six hub MRGs (NR4A1, PPP2R2A, P4HA1, TUBB6, DUSP1, NAMPT) with diagnostic potential were selected through WGCNA, logistic regression, SVM, RF, and LASSO models, all significantly downregulated in NAFLD samples compared to the control group. A diagnostic prediction model based on these six genes demonstrated robust predictive performance. Functional enrichment analysis of the six hub genes revealed involvement in processes such as protein phosphorylation or dephosphorylation. Correlation analysis demonstrated a significant association between hub MRGs and infiltrating immune cells.
We identified six hub MRGs in NAFLD and constructed a diagnostic prediction model based on these six genes, applicable for early NAFLD diagnosis. These genes may participate in regulating NAFLD progression through the modulation of mitophagy and immune activation. Our findings may contribute to subsequent clinical and basic research on NAFLD.
非酒精性脂肪性肝病（NAFLD）是全球主要的慢性肝病，但发病机制仍不清楚。本研究旨在识别核心线粒体自噬相关基因（MRGs），并探索这些核心基因调控NAFLD的潜在病理机制。
从GEO数据库获取3个数据集并整合，以识别NAFLD中的差异表达基因（DEGs）并进行基因集富集分析（GSEA）。通过将DEGs与MRGs交叉，获得线粒体自噬相关差异表达基因（MRDEGs）。然后，筛选出具有NAFLD诊断生物标志物能力的核心MRGs，并使用列线图、决策曲线分析（DCA）和ROC曲线构建并评估诊断预测模型。对鉴定出的核心基因进行功能富集分析，以探索其生物学意义。此外，使用数据库构建调控网络。根据诊断预测模型的风险评分将NAFLD分为高风险组和低风险组。此外，采用单样本基因集富集分析（ssGSEA）和CIBERSORT算法分析免疫细胞浸润模式以及核心MRGs与免疫细胞之间的关系。
整合数据集包含122个NAFLD样本和31个对照样本。筛选后，鉴定出18个MRDEGs。随后，通过WGCNA、逻辑回归、支持向量机、随机森林和LASSO模型选择了6个具有诊断潜力的核心MRGs（NR4A1、PPP2R2A、P4HA1、TUBB6、DUSP1、NAMPT），与对照组相比，在NAFLD样本中均显著下调。基于这6个基因的诊断预测模型显示出强大的预测性能。对这6个核心基因的功能富集分析表明它们参与蛋白质磷酸化或去磷酸化等过程。相关性分析表明核心MRGs与浸润免疫细胞之间存在显著关联。
我们在NAFLD中鉴定出6个核心MRGs，并基于这6个基因构建了诊断预测模型，适用于NAFLD的早期诊断。这些基因可能通过调节线粒体自噬和免疫激活参与调控NAFLD进展。我们的研究结果可能有助于后续NAFLD的临床和基础研究。
