Chen Yalei, Liu Anqi, Liu Hunan, Cai Guangyan, Lu Nianfang, Chen Jianwen
Department of Critical Care Medicine, Capital Medical University Electric Power Teaching Hospital/State Grid Beijing Electric Power Hospital, Beijing, China.
State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Nephrology Institute of the Chinese People's Liberation Army, Beijing, China.
Front Cell Dev Biol. 2023 Jul 27;11:1210714. doi: 10.3389/fcell.2023.1210714. eCollection 2023.
Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.
急性肾损伤（AKI）是一种常见且严重的疾病，其高发病率和死亡率给全球健康带来了负担。近年来，铁死亡已被认为与急性肾损伤密切相关。我们的目标是通过综合生物信息学分析和机器学习，开发一种基于铁死亡相关基因（FRGs）的急性肾损伤诊断标志物。我们下载了之前上传的小鼠急性肾损伤数据集GSE192883和另一个数据集GSE153625，通过生物信息学分析来识别共同表达的差异表达基因（coDEGs）。然后将FRGs与coDEGs进行重叠，以识别差异表达的FRGs（deFRGs）。利用免疫细胞浸润来研究急性肾损伤中的免疫细胞失调。应用功能富集分析和蛋白质-蛋白质相互作用网络分析来识别急性肾损伤的候选枢纽基因。然后，在两个人类数据集中，使用受试者工作特征曲线分析和机器学习分析（Lasso）来筛选诊断标志物。最后，通过定量实时PCR在急性肾损伤模型和多个数据集中对这些潜在的生物标志物进行验证。在GSE192883和GSE153625数据集中，共识别出885个coDEGs和33个deFRGs作为差异表达基因。在coDEGs的蛋白质-蛋白质相互作用网络的聚类1中，我们发现一组20个基因与deFRGs聚集在一起，共识别出48个上调的枢纽基因。经过ROC分析，我们发现25个枢纽基因的曲线下面积（AUC）大于0.7；在两个人类数据集GSE217427和GSE139061中，Lcn2、Plin2和Atf3的AUC均超过此阈值。通过Lasso分析，筛选出四个枢纽基因（Lcn2、Atf3、Pir和Mcm3）用于构建列线图并评估诊断价值。最后，在急性肾损伤数据集和实验室研究中验证了这四个基因的表达，表明它们可能是急性肾损伤理想的铁死亡标志物。识别出四个枢纽基因（Lcn2、Atf3、Pir和Mcm3）。经过验证，确认了该标志物的通用性，基于这四个基因的列线图模型能够有效区分急性肾损伤样本。我们的研究结果为急性肾损伤的进展提供了关键见解，并可指导个体化诊断和治疗。
