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February 23, 2026Journal of Translational Autoimmunity0 citationsOpen Access

GRN and KLRB1 define a shared peripheral-blood transcriptomic signature linking SLE and IPF

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LPLijun PangYLYunfei LiJCJunjie Chen

Key Points

  • The goal is to identify shared gene programs in blood that connect systemic lupus erythematosus and idiopathic pulmonary fibrosis, and to evaluate diagnostic models based on these.
  • Analyzed peripheral-blood transcriptomes from SLE and IPF cohorts using GEO datasets.
  • Conducted differential expression analysis and weighted gene co-expression network analysis to identify shared gene signals.
  • Used LASSO logistic regression, SVM–RFE, and random forest to develop gene panels for SLE and IPF.
  • Validated models in independent cohorts and conducted experimental validation using PBMC RT–qPCR and serum GRN ELISA.
  • Assessed model performance across various inflammatory contexts through fixed models.
  • Identified 43 shared differentially expressed genes (DEGs) linking SLE and IPF.
  • Developed a 6-gene panel for SLE and a 4-gene panel for IPF, achieving high AUC scores for model validation (AUC 0.996 for SLE in discovery).
  • Confirmed dysregulation of the SLE gene panel in an independent hospital cohort, with significantly elevated serum GRN in SLE patients.
  • SLE model demonstrated moderate discrimination for rheumatoid arthritis but limited for ICU sepsis; IPF model showed high discrimination for ICU groups.

Abstract

Systemic lupus erythematosus (SLE) and idiopathic pulmonary fibrosis (IPF) share immune–inflammatory features, yet their convergent peripheral-blood transcriptomic signatures remain incompletely defined. We sought to identify shared blood gene programs linking SLE and IPF, prioritize robust cross-disease markers, and evaluate parsimonious diagnostic models with experimental and external assessments. Peripheral-blood transcriptomes were analyzed in GEO discovery cohorts (SLE: GSE49454; IPF: GSE33566). Differential expression (limma) and weighted gene co-expression network analysis (WGCNA) were performed separately per disease, and concordant shared signals were integrated to form a shared candidate pool. Consensus feature selection combined LASSO logistic regression, nested cross-validated SVM–RFE, and random forest to derive parsimonious gene panels for SLE and IPF. Logistic-regression models were trained in discovery cohorts and externally validated in independent cohorts (SLE: GSE65391, GSE72509; IPF: baseline samples from longitudinal GSE93606). Experimental validation was conducted in an independent hospital cohort (60 SLE, 30 healthy controls) using PBMC RT–qPCR and serum GRN ELISA, with correlation and covariate-adjusted association analyses. Fixed models were additionally applied without refitting to non-target inflammatory cohorts (RA: GSE93272; ICU sepsis/non-infectious critical illness: GSE134347). Discovery analyses identified 389 SLE and 248 IPF DEGs and yielded 43 concordantly regulated shared DEGs; WGCNA identified 43 shared module genes, producing a non-redundant shared candidate pool of 78 genes enriched for B-cell and myeloid programs. Consensus selection generated a 6-gene SLE panel (EIF2AK2, GRN, ASGR2, KLRB1, LGALS9, KLF13) and a 4-gene IPF panel (GRN, ARG1, KLRB1, FCMR). The SLE model achieved AUC 0.996 in discovery and validated at AUC 0.888 (GSE65391) and 0.761 (GSE72509); the IPF model achieved AUC 0.906 in discovery and 0.722 in baseline validation. In the hospital cohort, RT–qPCR confirmed dysregulation of the six-gene panel, and serum GRN was markedly elevated in SLE (median IQR 43.58 38.44–54.42 vs. 14.26 12.79–15.26 ng/mL). Within SLE, serum GRN correlated with SLEDAI and inversely with C3/C4 and WBC; after covariate adjustment, associations with WBC, ESR, C3, and C4 remained significant, whereas associations with hs-CRP and SLEDAI were attenuated. In non-target cohorts, the SLE model showed moderate discrimination for RA (AUC 0.73) but limited discrimination for ICU sepsis (AUC 0.64) and none for non-infectious critical illness (AUC 0.50), while the IPF model showed minimal discrimination for RA (AUC 0.51) but high discrimination for ICU groups (AUC 0.99 and 0.96). GRN and KLRB1 anchor a shared peripheral-blood transcriptomic signature linking SLE and IPF, enabling parsimonious diagnostic models with multi-cohort validation and clinical experimental support. External in silico applications to other inflammatory contexts indicate context-dependent model behavior, underscoring the importance of cohort-appropriate interpretation and validation.

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Cite This Study

Pang et al. (2026) studied this question.

synapsesocial.com/papers/699bee551c6c6bad5397ffe1https://doi.org/10.1016/j.jtauto.2026.100357
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