Association between SYVN1 and SEL1 genetic polymorphisms and remission in rheumatoid arthritis patients treated with TNF-α inhibitors: a machine learning approach.

Rheumatoid arthritis (RA) is a severe chronic inflammatory condition that affects joint synovium. Suppressor/enhancer of lin-12-like (SEL1L)-Synoviolin 1 (SYVN1)-mediated endoplasmic reticulum-associated degradation (ERAD) is highly associated with RA development. Although targeting SEL1L-SYVN1-mediated ERAD can be beneficial, studies that evaluate the association between polymorphisms in their genes and remission from the disease in RA patients taking tumor necrosis factor (TNF)-α inhibitors have yet to be carried out. Hence, the purpose of this study was to investigate the association between SYVN1 and SEL1L polymorphisms and TNF-α inhibitor response using various machine learning models. A total of 12 single-nucleotide polymorphisms (SNPs), including 5 SNPs in SYVN1 and 7 SNPs of SEL1L were investigated. Logistic regression analysis was used to examine the relationship between genetic polymorphisms and response to treatment. Various machine learning methods were employed to evaluate factors associated with remission in patients receiving TNF-α inhibitors. After adjusting for covariates, we found that sulfasalazine and rs2025214 in SEL1L increase the remission rates by approximately 3.3 and 2.8 times, respectively (95% confidence intervals 1.126-9.695 and 1.074-7.358, respectively). Machine learning approaches showed acceptable prediction estimates for remission in RA patients receiving TNF-α inhibitors, with the area under the receiver-operating curve (AUROC) values ranging from 0.60 to 0.65. A polymorphism of the SEL1L gene (rs2025214) and sulfasalazine were found to be associated with treatment response in RA patients receiving TNF-α inhibitors. These preliminary data could be used to tailor treatment for RA patients using TNF-α inhibitors.

Association between nuclear factor of activated T cells C2 polymorphisms and treatment response in rheumatoid arthritis patients receiving tumor necrosis factor-alpha inhibitors.

OBJECTIVES: Nuclear factor of activated T cells C2 (NFATC2) is known as a member of the transcription family and enhances tumor necrosis factor-alpha (TNF-α) synthesis in human T cells at the gene transcription level. Although NFATC2 has a potential role in rheumatoid arthritis (RA) progression and treatment, no study has investigated the association between NFATC2 gene polymorphisms and response status in RA patients receiving TNF-α inhibitors. This study aimed to examine the effects of polymorphisms in NFATC2, a TNF-α transcription factor, on response to TNF-α inhibitors. METHODS: This prospective observational study was performed in two centers. Seven single nucleotide polymorphisms (SNPs) were investigated. Good responders were defined as patients with disease activity score (DAS)28 ≤3.2 after 6 months of treatment. Logistic regression analyses were used to investigate the association between genetic polymorphisms and response to the treatment. To test the model's goodness of fit, a Hosmer-Lemeshow test was performed. RESULTS: This study included 98 patients, among whom 46 showed favorable responses to the treatment. Patients with hypertension revealed an approximately three-fold lower response to TNF-α inhibitors compared to those without hypertension (23.5 vs. 76.5%; P = 0.049). After adjusting for covariates, C allele carriers of NFATC2 rs3787186 exhibited approximately three-fold lower rates of treatment response compared to those with TT genotype (P = 0.037). The Hosmer-Lemeshow test showed that the fitness of the multivariable analysis model was satisfactory (χ2 = 9.745; 8 degrees of freedom; P = 0.283). CONCLUSION: This study suggested an association between the C allele of rs3787186 and treatment response in RA patients receiving TNF-α inhibitors.