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Predictors of response of rituximab in rheumatoid arthritis by weighted gene co-expression network analysis

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Abstract

Purpose

The purpose of this study was to identify a biomarker that can predict the efficacy of rituximab (RTX) in the treatment of rheumatoid arthritis (RA) patients.

Methods

Utilized weighted gene co-expression network analysis (WGCNA) and LASSO regression analysis of whole blood transcriptome data (GSE15316 and GSE37107) related to RTX treatment for RA from the GEO database, the critical modules, and key genes related to the efficacy of RTX treatment for RA were found. The biological functions were further explored through enrichment analysis. The area under the ROC curve (AUC) was validated using the GSE54629 dataset.

Results

WGCNA screened 71 genes for a dark turquoise module that were correlated with the efficacy of RTX treatment for RA (r = 0.42, P < 0.05). Through the calculation of gene significance (GS) and module membership (MM), 12 important genes were identified; in addition, 21 important genes were screened by the LASSO regression model; two key genes were obtained from the intersection between the important genes. Then, BANK1 (AUC = 0.704, P < 0.05) was identified as a potential biomarker to predict the efficacy of RTX treatment for RA by ROC curve evaluation of the treatment and validation groups. BANK1 gene expression was significantly decreased after RTX treatment, and a statistically significant difference was found (log FC =  − 2.08, P < 0.05). Immune cell infiltration analysis revealed that the infiltration of CD4 + T cell memory subset was increased in the group with high BANK1 expression, and a statistically significant difference was found (P < 0.05).

Conclusions

BANK1 can be used as a potential biomarker to predict the response of RTX treatment in RA patients.

Key Points

• Identifying the hub genes BANK1 as a potential biomarker to predict the response of RTX treatment in RA patients and confirming it in validation data.

• Using the WGCNA approach and LASSO analyses to identify the BANK1 in a data set consisting of two GEO data merged and assessing the correlations between BANK1 and immune infiltration by CIBERSORT algorithm.

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Acknowledgements

We acknowledge GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Funding

This study is funded by the Guizhou Provincial Health Commission Project [gzwkj2021-134], the NSFC Cultivation Project of Affiliated Hospital of Guizhou Medical University [I-2020–24], and the Doctoral Research Start-up Fund Project of Guizhou Medical University [gyfybsky-2021–62].

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Authors and Affiliations

  1. Rheumatology and Immunology Department, Affiliated Hospital of Guizhou Medical University, Yunyan District, 28 Guiyi Street, Guiyang, 550004, Guizhou, China

    Shan Zhang, Peiting Li, Pengjia Wu, Lei Yang, Xiaoxia Liu, Jun Liu, Yong Zhang & Jiashun Zeng

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  1. Shan Zhang
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  2. Peiting Li
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  6. Jun Liu
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  7. Yong Zhang
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  8. Jiashun Zeng
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Corresponding author

Correspondence to Jiashun Zeng.

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This article does not contain any studies with human participants or animals performed by any of the authors. GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues.

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Zhang, S., Li, P., Wu, P. et al. Predictors of response of rituximab in rheumatoid arthritis by weighted gene co-expression network analysis. Clin Rheumatol 42, 529–538 (2023). https://doi.org/10.1007/s10067-022-06438-y

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  • DOI: https://doi.org/10.1007/s10067-022-06438-y

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