Identification of ferroptosis-related genes in proliferative lupus nephritis by bioinformatics approach
10.3760/cma.j.cn141217-20221213-00501
- VernacularTitle:铁死亡相关基因在增殖型狼疮肾炎中的生物信息学研究
- Author:
Jie KONG
1
;
Yingying GAO
;
Zhanyun DA
Author Information
1. 南通市第一人民医院 南通大学第二附属医院风湿免疫科，南通 226000
- Keywords:
Lupus erythematosus, systemic;
Lupus nephritis;
Ferroptosis;
Computational biology
- From:
Chinese Journal of Rheumatology
2024;28(7):460-464
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the differential expressions of ferroptosis related genes (FRGs) in proliferative lupus nephritis and its correlation with clinical indices (PLN) by analyzing the gene expression omnibus (GEO) database.Methods:The GSE65391 dataset was downloaded from GEO database, differential FRGs of PLN were screened by limma package of R language. Wilcoxon rank-sum test was used to analyze the differentially expressed genes in peripheral blood between 514 PLN patients and 72 healthy controls. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of differential FRGs were determined with the clusterProfiler package. Hub genes were determined using random forest (RF), support vector machine (SVM), xgboost (XGB) and generalized linear model(GLM) machine learning algorithms. Finally, Spearman rank correlation analysis was used to analyze the correlation between hub genes and clinical indices.Results:A total of 38 differential FRGs of PLN patients were screened out, of which 16 were up-regulated and 22 were down-regulated. Biological process was enriched in cellular response to chemical stress, cellular component was enriched in transcription regulator complex, molecular function was enriched in DNA-binding transcription factor binding. KEGG enrichment analysis showed that FRGs were mainly involved in NOD-like receptor signaling pathway. GLM algorithm was selected to predict gene essentiality according to the area under the receiver operating characteristic (ROC) curve, 10 hub genes were determined, of which MYB was the most important. MYB was positively correlated with SLEDAI ( r=0.21, P<0.001), ALT ( r=0.20, P<0.001), AST ( r=0.18, P<0.001), LDH ( r=0.31, P<0.001), Cr ( r=0.24, P<0.001) and ESR ( r=0.22, P<0.001) and negatively correlated with albumin ( r=-0.28, P<0.001). Conclusion:FRGs may provide new insight into the potential mechanisms of the pathogenesis of PLN.
