Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study.
Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study.
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非酒精性脂肪肝生物标志物的鉴定：机器学习方法和实验研究
DOI：
10.3389/fgene.2022.1020899
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发表时间：
2022
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
非酒精性脂肪性肝病（NAFLD）已成为最常见的慢性肝病。然而，NAFLD的早期诊断具有挑战性。因此，本研究的目的是使用机器学习算法识别NAFLD的诊断生物标志物。从GEO数据库中分别鉴定NAFLD和正常样本之间的差异表达基因。通过蛋白质-蛋白质相互作用网络筛选出关键的DEG，并对其生物学功能进行了分析。然后，选择三种机器学习算法分别构建NAFLD模型，并确定样本残差最小的模型为最佳模型。采用Logistic回归分析判断5个基因预测NAFLD风险的准确性。采用单样本基因集富集分析算法评价NAFLD的免疫细胞浸润情况，并分析诊断生物标志物与免疫细胞浸润的相关性。最后，收集10对NAFLD患者和正常对照的外周血样本进行RNA提取和定量实时聚合酶链反应进行验证。总之，CEBPD、H4 C11、CEBPB、GATA 3和KLF 4被机器学习算法鉴定为NAFLD的诊断生物标志物，并且与NAFLD中的免疫细胞浸润相关。这些关键基因为NAFLD患者的机制和治疗提供了新的见解。
Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.