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

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Abstract

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.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Funding

This work was supported by the Chungbuk National University Korea National University Development Project (2021).

Author information

Author notes

  1. Woorim Kim and Ha Rim Yeon contributed equally to this study.

Authors and Affiliations

  1. College of Pharmacy, Chungbuk National University, 660-1, Yeonje-ri, Osong-eup, Heungdeok-gu, Cheongju-si, 28160, Republic of Korea

    Woorim Kim, Ha Rim Yeon, Jun Hyeob Kim & Kyung Eun Lee

  2. College of Pharmacy, Kangwon National University, Chuncheon, Republic of Korea

    Woorim Kim

  3. College of Pharmacy, Ajou University, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea

    Joo Hee Kim

  4. Division of Rheumatology, Department of Internal Medicine, Chungbuk National University Hospital, 776, 1sunhwan-ro, Seowon-gu, Cheongju, 28644, Republic of Korea

    Ji Hyoun Kim & In Ah Choi

  5. Department of Rheumatology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, 14, Suwon, 16499, Republic of Korea

    Hyoun-Ah Kim & Ju-Yang Jung

  6. Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, 35015, Republic of Korea

    Jinhyun Kim

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  1. Woorim Kim
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Contributions

Woorim Kim: analysis and writing—original draft; Ha Rim Yeon: analysis and writing—original draft; Jun Hyeob Kim: analysis; Joo Hee Kim: methodology; Ji Hyoun Kim: data curation; Hyoun-Ah Kim: conceptualization; Ju-Yang Jung: conceptualization; Jinhyun Kim: data curation; In Ah Choi: supervision, conceptualization, and data curation; Kyung Eun Lee: supervision, conceptualization, and writing—reviewing and editing.

Corresponding authors

Correspondence to In Ah Choi or Kyung Eun Lee.

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Ethics approval

This study was approved by the Institutional Review Boards of the Ajou University Hospital (approval number: AJIRB-BMR-OBS-17-153), Chungbuk National University Hospital (approval number: 2017-06-011-004), and Chungnam National University Hospital (approval number:2019-06-029). This study was conducted according to the principles of the Declaration of Helsinki (2013).

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All patients submitted written informed consents for participation.

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The authors declare no competing interests.

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Kim, W., Yeon, H.R., Kim, J.H. et al. Association between SYVN1 and SEL1 genetic polymorphisms and remission in rheumatoid arthritis patients treated with TNF-α inhibitors: a machine learning approach. Immunol Res 71, 709–716 (2023). https://doi.org/10.1007/s12026-023-09382-4

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