DataSheet_1_Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with non-alcoholic fatty liver disease (NAFLD) by bioinformatics analysis and machine learning.pdf

BackgroundChronic kidney disease (CKD) and non-alcoholic fatty liver disease (NAFLD) are closely related to immune and inflammatory pathways. This study aimed to explore the diagnostic markers for CKD patients with NAFLD.
MethodsCKD and NAFLD microarray data sets were screened from the GEO database and analyzed the differentially expressed genes (DEGs) in GSE10495 of CKD date set. Weighted Gene Co-Expression Network Analysis (WGCNA) method was used to construct gene coexpression networks and identify functional modules of NAFLD in GSE89632 date set. Then obtaining NAFLD-related share genes by intersecting DEGs of CKD and modular genes of NAFLD. Then functional enrichment analysis of NAFLD-related share genes was performed. The NAFLD-related hub genes come from intersection of cytoscape software and machine learning. ROC curves were used to examine the diagnostic value of NAFLD related hub genes in the CKD data sets and GSE89632 date set of NAFLD. CIBERSORTx was also used to explore the immune landscape in GSE104954, and the correlation between immune infiltration and hub genes expression was investigated.
ResultsA total of 45 NAFLD-related share genes were obtained, and 4 were NAFLD-related hub genes. Enrichment analysis showed that the NAFLD-related share genes were significantly enriched in immune-related pathways, programmed cell death, and inflammatory response. ROC curve confirmed 4 NAFLD-related hub genes in CKD training set GSE104954 and other validation sets. Then they were used as diagnostic markers for CKD. Interestingly, these 4 diagnostic markers of CKD also showed good diagnostic value in the NAFLD date set GSE89632, so these genes may be important targets of NAFLD in the development of CKD. The expression levels of the 4 diagnostic markers for CKD were significantly correlated with the infiltration of immune cells.
Conclusion4 NAFLD-related genes (DUSP1, NR4A1, FOSB, ZFP36) were identified as diagnostic markers in CKD patients with NAFLD. Our study may provide diagnostic markers and therapeutic targets for CKD patients with NAFLD.

背景：慢性肾脏病（Chronic kidney disease, CKD）与非酒精性脂肪性肝病（non-alcoholic fatty liver disease, NAFLD）均与免疫及炎症通路密切相关。本研究旨在探索合并非酒精性脂肪性肝病的慢性肾脏病患者的诊断标志物。方法：从GEO数据库（GEO database）中筛选慢性肾脏病与非酒精性脂肪性肝病的微阵列数据集，并针对慢性肾脏病数据集GSE104954中的差异表达基因（differentially expressed genes, DEGs）进行分析。采用加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis, WGCNA）方法，在非酒精性脂肪性肝病数据集GSE89632中构建基因共表达网络并识别功能模块。随后通过取慢性肾脏病的差异表达基因与非酒精性脂肪性肝病的模块基因的交集，获得非酒精性脂肪性肝病相关共有基因。继而对非酒精性脂肪性肝病相关共有基因进行功能富集分析。非酒精性脂肪性肝病相关核心基因通过Cytoscape软件与机器学习方法的交集获得。采用ROC曲线（Receiver Operating Characteristic curve）评估非酒精性脂肪性肝病相关核心基因在慢性肾脏病数据集GSE104954以及非酒精性脂肪性肝病数据集GSE89632中的诊断价值。此外使用CIBERSORTx工具探究GSE104954中的免疫景观，并分析免疫浸润与核心基因表达的相关性。结果：本研究共获得45个非酒精性脂肪性肝病相关共有基因，并筛选出4个非酒精性脂肪性肝病相关核心基因。富集分析结果显示，非酒精性脂肪性肝病相关共有基因显著富集于免疫相关通路、程序性细胞死亡及炎症反应。ROC曲线验证了4个非酒精性脂肪性肝病相关核心基因在慢性肾脏病训练数据集GSE104954及其他验证数据集中的诊断效能，可作为慢性肾脏病的诊断标志物。有趣的是，这4个慢性肾脏病诊断标志物在非酒精性脂肪性肝病数据集GSE89632中同样表现出良好的诊断价值，提示这些基因可能是非酒精性脂肪性肝病参与慢性肾脏病发生发展的重要靶点。4个慢性肾脏病诊断标志物的表达水平与免疫细胞浸润程度显著相关。结论：本研究鉴定出4个非酒精性脂肪性肝病相关基因（DUSP1、NR4A1、FOSB、ZFP36）可作为合并非酒精性脂肪性肝病的慢性肾脏病患者的诊断标志物。本研究可为合并非酒精性脂肪性肝病的慢性肾脏病患者提供诊断标志物及治疗靶点。