Qu Shuye, Huang Hui, Diao Yan, Liu Bowei, Tang Baozhu, Huo Shijiao, Lei Yu, Xuan Xiuchen, Mou Wenling, Liu Ping, Zhang Jiye, Liu Ying, Li Yanze
Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China.
Department of Gastroenterology, Heilongjiang Provincial Hospital, Harbin, China.
Cell Mol Biol (Noisy-le-grand). 2023 Oct 31;69(10):254-263. doi: 10.14715/cmb/2023.69.10.38.
The mechanisms of the effect of propionate metabolism and immunity on nonalcoholic fatty liver disease (NAFLD) have not been adequately studied. Firstly, differentially expressed-propionate metabolism-related genes (DE-PMRGs) were selected by overlapping PMRGs and differentially expressed genes (DEGs) between the simple steatosis (SS) and health control (HC) groups. Then, common genes were selected by overlapping DE-PMRGs and key module genes obtained from weighted gene co-expression network analysis (WGCNA). Subsequently, the biomarkers were screened out by machine learning algorithms. The expression of the biomarkers was validated by quantitative Real-time PCR. In total, 5 biomarkers (JUN, LDLR, CXCR4, NNMT, and ANXA1) were acquired. The nomogram constructed based on 5 biomarkers had good predictive power for the risk of SS. Next, 5 biomarkers, 11 miRNAs, and 149 lncRNAs were encompassed in the ceRNA regulatory network. The expression of biomarkers was significantly higher in the HC group than in the SS group, which was consistent with the results in the GSE89632 and GSE126848 datasets. In this study, 5 immune and propionate metabolism-related biomarkers (JUN, LDLR, CXCR4, NNMT, and ANXA1) were screened out to provide a basis for exploring the prediction of diagnosis of NAFLD.
丙酸代谢与免疫对非酒精性脂肪性肝病（NAFLD）影响的机制尚未得到充分研究。首先，通过重叠单纯性脂肪变性（SS）组与健康对照组（HC）之间的丙酸代谢相关基因（PMRGs）和差异表达基因（DEGs），筛选出差异表达的丙酸代谢相关基因（DE-PMRGs）。然后，通过重叠DE-PMRGs与从加权基因共表达网络分析（WGCNA）获得的关键模块基因，筛选出共同基因。随后，通过机器学习算法筛选出生物标志物。通过定量实时PCR验证生物标志物的表达。总共获得了5个生物标志物（JUN、LDLR、CXCR4、NNMT和ANXA1）。基于这5个生物标志物构建的列线图对SS风险具有良好的预测能力。接下来，ceRNA调控网络纳入了5个生物标志物、11个miRNA和149个lncRNA。生物标志物在HC组中的表达显著高于SS组，这与GSE89632和GSE126848数据集中的结果一致。在本研究中，筛选出5个免疫和丙酸代谢相关生物标志物（JUN、LDLR、CXCR4、NNMT和ANXA1），为探索NAFLD诊断预测提供依据。
