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Machine learning-driven discovery of STAT1 and TRIM22 as immune biomarkers for lupus nephritis: translational insights into diagnosis and pathogenesis
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  • Published: 20 February 2026

Machine learning-driven discovery of STAT1 and TRIM22 as immune biomarkers for lupus nephritis: translational insights into diagnosis and pathogenesis

  • Jiayi Deng3 na1,
  • Zimiao Zhang1 na1,
  • Yueyuan Lai1,
  • Jinpeng Chen1,
  • Xiaomei Ma1,
  • Zhenkai Gao2,
  • Chao Lin1,
  • Xiaohong Li1,
  • Weihao Wu1,
  • Congjie Chen1,
  • Xiaohui Shangguan1,
  • Yanhong Huang1,
  • Haoran Qiu1,
  • Xiaoming Qiu1 &
  • …
  • Longtian Chen1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Computational biology and bioinformatics
  • Diseases
  • Immunology
  • Nephrology

Abstract

Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus and a major cause of renal dysfunction, while reliable non-invasive biomarkers remain limited. Transcriptomic data from three LN cohorts were analyzed to identify differentially expressed genes (DEGs). Immune-associated DEGs were selected using WGCNA and prioritized via multiple machine learning algorithms. Diagnostic performance was evaluated with ROC curves and nomogram modeling, accompanied by functional enrichment and immune infiltration analyses. Independent validation was performed by qRT-PCR in peripheral blood samples from 13 LN patients and 10 healthy controls. A total of 320 DEGs were identified, including 53 linked to immune processes. In the transcriptomic datasets, four candidate hub genes (CD40LG, RETN, TRIM22, STAT1) were initially identified. Furthermore, immune infiltration analysis suggested gene-specific immune interaction patterns, particularly associating TRIM22 with CD4⁺ T-cell–related signatures. qRT-PCR confirmed upregulation of STAT1 and TRIM22, while RETN and CD40LG showed no significant elevation. Accordingly, a refined two-gene signature was constructed, showing consistent discriminatory trends in the training dataset and the clinical validation cohort (AUCs > 0.9). STAT1 and TRIM22 were consistently upregulated in the peripheral blood of patients with lupus nephritis and may represent potential immune-related biomarkers.

Data availability

The datasets analyzed during the current study are publicly available in the Gene Expression Omnibus (GEO) database:- GSE72326: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE72326- GSE99967: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE99967- GSE81622: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE81622A total of 2,483 IRGs were retrieved from the ImmPort database (https://www.immport.org/home) and are listed in Supplementary Table (1) The list of 320 DEGs identified between LN and normal samples is available in Supplementary Table (2) The original qRT-PCR data used to validate the expression of selected hub genes are provided in Supplementary Table 3.Results of the univariable linear regression analysis evaluating the associations between clinical parameters and *STAT1* and *TRIM22* expression levels in LN patients are shown in Supplementary Table 4. Detailed clinical characteristics of the LN patients, including demographic and disease-related information, are summarized in Supplementary Table 5. The baseline clinical and pathological characteristics of the integrated LN cohorts are provided in Supplementary Table 6.All other relevant data generated or analyzed during this study are included in the main manuscript and the supplementary material.

Abbreviations

STAT1:

Signal transducer and activator of transcription 1

TRIM22:

Tripartite motif containing 22

CD40LG:

CD40 ligand

RETN:

Resistin

SLE:

Systemic lupus erythematosus

LN:

Lupus nephritis

DEGs:

Differentially expressed genes

IRGs:

Immune-related genes

WGCNA:

Weighted gene co-expression network analysis

IR-DEGs:

Immune-related differentially expressed genes

LASSO:

Least Absolute Shrinkage and Selection Operator

SVM:

Support Vector Machine

XGBoost:

Extreme Gradient Boosting

ROC:

Receiver operating characteristic

GSEA:

Gene Set Enrichment Analysis

PCA:

Principal Component Analysis

ssGSEA:

Single-sample Gene Set Enrichment Analysis

TFs:

Transcription factors

qRT-PCR:

Quantitative real-time PCR

HCs:

Healthy controls

eGFR:

Estimated glomerular filtration rate

SLEDAI:

Systemic Lupus Erythematosus Disease Activity Index

AUC:

Area Under the Curve

PCA:

Principal component analysis

NK cells:

Natural killer cells

ISGs:

Interferon-stimulated genes

IFN:

Interferon

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Acknowledgements

The authors thank all participants of the study. ChatGPT (OpenAI) was used solely to assist with language refinement and formatting during manuscript preparation. All intellectual content, data analysis, and interpretation were conducted independently by the authors.

Funding

This work was supported by the Fujian Provincial Natural Science Foundation Program [grant number 2023J011894], the Longyan Municipal Science and Technology Planning Project [grant number 2022LYF17110], and the Fujian Provincial Health Technology Project [grant number 2022QNA102].

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  1. Jiayi Deng MD and Zimiao Zhang contributed equally to this work.

Authors and Affiliations

  1. Longyan First Affiliated Hospital of Fujian Medical University, Longyan, 364000, China

    Zimiao Zhang, Yueyuan Lai, Jinpeng Chen, Xiaomei Ma, Chao Lin, Xiaohong Li, Weihao Wu, Congjie Chen, Xiaohui Shangguan, Yanhong Huang, Haoran Qiu, Xiaoming Qiu & Longtian Chen

  2. The Third Hospital of Longyan, Longyan, 364000, China

    Zhenkai Gao

  3. Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, 364000, China

    Jiayi Deng

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Contributions

JYD, ZMZ, and ZKG conceptualized and designed the study, with LTC and XMQ serving as guarantors. YYL and JPC conducted sample collection, while CL and WHW coordinated all data acquisition. YHH and HRQ performed systematic analysis and interpretation of the data. JPC, CL, and YHL designed and plotted the figures and tables. JYD, ZMZ, XHSG, and XHL wrote the original manuscript draft. LTC, XMM, CJC, and WHW critically reviewed and edited the final version. This work was funded by LTC, XMM, and CJC. All authors (JYD, ZMZ, YYL, JPC, ZKG, CL, XHL, WHW, XMM, CJC, XHSG, YHH, HRQ, XMQ, LTC) read and approved the final manuscript.

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Correspondence to Jiayi Deng, Xiaoming Qiu or Longtian Chen.

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Ethics approval and consent to participate

This study was approved by the Ethics Committee of Longyan First Hospital (Approval No. LYREC2024-k172-01). Whole blood samples from 13 lupus nephritis patients (diagnosed according to ISN/RPS criteria) and 10 healthy controls were collected after obtaining written informed consent from all participants. All procedures were conducted in accordance with the Declaration of Helsinki.

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Deng, J., Zhang, Z., Lai, Y. et al. Machine learning-driven discovery of STAT1 and TRIM22 as immune biomarkers for lupus nephritis: translational insights into diagnosis and pathogenesis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41028-x

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  • Received: 09 September 2025

  • Accepted: 17 February 2026

  • Published: 20 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-41028-x

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Keywords

  • Lupus nephritis
  • STAT1
  • TRIM22
  • Immune biomarkers
  • Machine learning
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