ArticlePloS one2025
Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Identification and Experimental Validation of Key Lipid Metabolism-Related Genes in Intracerebral Hemorrhage Based on Machine Learning.Journal of inflammation research · 2026Article
- Deciphering the interplay between inflammation and dysregulated autophagy in lupus nephritis through network analysis and experimental validation.Frontiers in cell and developmental biology · 2026Article
- Identification and Validation of Key Purine Metabolism-Related Genes in Ulcerative Colitis Using Bioinformatics and Machine Learning.Journal of inflammation research · 2026Article
- Machine Learning-Based Identification and Experimental Validation of Hub Ferroptosis-Related Cuproptosis Genes in Lupus Nephritis.Journal of inflammation research · 2025Article
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Abstract
introductionLupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relationship between autophagy and LN remains unelucidated.
methodsWe analyzed differentially expressed genes (DEGs) in kidney tissues from 14 LN patients and 7 normal controls using the GSE112943 dataset. Key modules and their contained genes were identified utilizing weighted gene co-expression network analysis (WGCNA). Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. The area under the receiver operating characteristic (ROC) curves was used to assess the accuracy of the signature genes. We also analyzed immune cell infiltration in LN. Additionally, the association between key genes and kidney diseases was predicted. Finally, key genes expression in kidney was verified by clinical samples and animal experiments.
resultsA total of 10304 DEGs were identified in GSE1129943 and 29 modules were identified in WGCNA. Among them, the brown module and coral 2 module exhibited significant correlation with LN (cor = 0.86, -0.84, p<0.001). Machine learning techniques identified 5 signature genes, but only 2 were validated in the external dataset GSE32591, namely MAP1LC3B (AUC = 0.920) and TNFSF10 (AUC = 0.937), which are involved in autophagy and apoptosis. Immune infiltration analysis suggested that these key genes may be associated with immune cell infiltration in LN. In addition, these genes have been linked to a variety of renal diseases, and their expression was verified in kidney tissues in LN patients and lupus mice.
conclusionMAP1LC3B and TNFSF10 may be key autophagy-related genes in LN. These key genes have the potential to provide new insights into the molecular diagnosis and treatment of LN.
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