Bioinformatics-Based Analysis of the Roles of <i>MX1，IFI44，</i> and <i>STAT1</i> in Lupus Nephritis

Chunli CHEN; Daolin CUI

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Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.

Citation:

Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.

Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.

Citation:

Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.

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Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis

Chunli CHEN,
Daolin CUI^,

Dept. of Basic Medicine，Qujing Medical College，Qujing Yunnan 655000，China

Received Date: 2024-06-15

Abstract

Abstract

Objective Lupus nephritis （LN） is a severe organ manifestation of systemic lupus erythematosus （SLE） and one of the leading causes of death in SLE patients. This study aims to identify potential biomarkers associated with LN, with the goal of improving early diagnosis, disease monitoring, and the development of more precise treatment strategies. Methods Gene expression data were downloaded from the Gene Expression Omnibus （GEO） database for datasets GSE22221, GSE112943, GSE99967, and GSE32591. Intersecting genes were obtained through the application of weighted gene co-expression network analysis （WGCNA） and linear models for microarray data （LIMMA）. Subsequently, biological function and pathway analyses were conducted on these intersecting genes using Gene Ontology （GO） and the Kyoto Encyclopedia of Genes and Genomes （KEGG）. Next, protein-protein interaction （PPI） network analysis was performed, and hub genes highly associated with LN were identified using the CytoHubba algorithm, support vector machine （SVM）, and random forest （RF） methods. Receiver operating characteristic （ROC） analysis was performed, and three potential biomarkers were validated using the GSE72798 dataset. Results The green-yellow module （P = 7.4e−40） and the cyan module （P = 1.5e−14） were identified through WGCNA analysis. A total of 193 differentially expressed genes were identified using LIMMA, with 113 intersecting genes related to LN being identified. GO and KEGG analyses indicated that these genes were mainly enriched in viral or bacterial defense, type I interferon signaling pathway, neutrophil-mediated immunity, and Toll-like receptor signaling. MX1, IFI44, and STAT1 were identified as hub genes using CytoHubba, SVM, and RF methods, with AUC values of 0.874, 0.879, and 0.833, respectively. Validation using the GSE72798 dataset demonstrated that the expression of MX1, IFI44, and STAT1 was significantly higher in LN patients compared to healthy individuals （P < 0.001 for all）. Conclusion MX1, IFI44, and STAT1 play crucial roles in the pathogenesis of LN and may serve as important biomarkers and potential therapeutic targets for LN.
- Lupus nephritis,
- Systemic lupus erythematosus,
- Biomarker,
- Hub genes,
- Type I interferon

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References(27)

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