The Impact of TRAIL (C1595T and G1525A) and DR4 (rs20576) Gene Polymorphisms on Systemic Lupus Erythematosus

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning

INTRODUCTION

Lupus 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.

METHODS

We 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.

RESULTS

A 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.

CONCLUSION

MAP1LC3B 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.

Pharmacogenetic Predictors of Response to Interferon Beta Therapy in Multiple Sclerosis

First-line therapy with interferon beta (IFN-β), involved in gene expression modulation in immune response, is widely used for multiple sclerosis. However, 30-50% of patients do not respond optimally. Variants in CBLB, CTSS, GRIA3, OAS1 and TNFRSF10A genes have been proposed to contribute to the variation in the individual response. The purpose of this study was to evaluate the influence of gene polymorphisms on the IFN-β response in relapsing-remitting multiple sclerosis (RRMS) patients. CBLB (rs12487066), GRIA3 (rs12557782), CTSS (rs1136774), OAS1 (rs10774671) and TNFRSF10A (rs20576) polymorphisms were analysed by Taqman in 137 RRMS patients. Response to IFN-β and change in the Expanded Disability Status Scale (EDSS) after 24 months were evaluated using multivariable logistic regression analysis. Carriers of at least one copy of the C allele of CTSS-rs1136774 had a better response to IFN-β (p = 0.0423; OR = 2.94; CI95% = 1.03, 8.40). Carriers of TT genotype of TNFRSF10A-rs20576 had a higher probability of maintaining their EDSS stable after 24 months of IFN-β treatment (p = 0.0251; OR = 5.71; CI95% = 1.39, 31.75). No influence of CBLB (rs12487066), OAS1 (rs10774671) and GRIA3 (rs12557782) gene polymorphisms in the variation of the individual response to IFN-β was shown. Our results suggest that the TNFRSF10A-rs20576 and CTSS-rs1136774 gene polymorphisms influence the response to IFN-β after 24 months, while the CBLB (rs12487066), OAS1 (rs10774671) or GRIA3 (rs12557782) gene polymorphisms had no effect on the variation of the individual response to IFN-β.