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Background <p>The pathogenic mechanisms underlying rheumatoid arthritis (RA) remain elusive. Lactylation, a novel post-translational modification, may regulate immune and metabolic reprogramming, underscoring the imperative to delineate lactylation-related genes (LRGs) driving RA progression.</p> Methods <p>LRG expression profiles from RA patients and healthy controls were analyzed from GEO datasets. Immune infiltration and LRG-immune correlations were assessed. A machine-learning framework identified a hub LRG signature, whose metabolic and therapeutic relevance was evaluated via functional enrichment and druggability analyses. qRT-PCR validated hub gene expression in RA MH7A cells model.</p> Results <p>Transcriptomic profiling identified 36 differentially expressed LRGs regulating cytokine networks and immune signaling in RA. Disease stratification revealed two molecular subtypes, with Subtype B demonstrating p53 signaling and innate immunity pathway activation via gene set variation analysis (GSVA). Weighted correlation network analysis (WGCNA) identified subtype B-associated modules (504 genes). A machine learning-derived 7-LRG signature (<i>Sdc1</i>, <i>Pfkfb1</i>, <i>Fut8</i>, <i>Adh1b</i>, <i>Kif23</i>, <i>Adh1c</i>, <i>Pkc1</i>) differentiated RA from controls (AUC 0.92). Single-cell resolution analysis localized Sdc1 to plasma cell clusters, correlating with memory B cell expansion and macrophage polarization. Hub LRGs were enriched in glucose metabolism pathways in RA, and <i>Sdc1</i>, <i>Adh1b</i>, and <i>Adh1c</i> were druggable, suggesting potential therapeutic targets. qRT-PCR validation confirmed significant LRG upregulation in RA cellular models.</p> Conclusion <p>Our findings establish lactylation as a key modulator of immune dysregulation in RA pathogenesis. Seven LRG biomarkers were identified and validated, exhibiting dual potential as prognostic indicators and therapeutic targets through lactylation-driven pathway modulation for RA.</p>

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Identification and validation of lactylation-related genes signature and immune infiltration landscape of rheumatoid arthritis based on machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Xiaoli Hu,
  • Qian Xiao,
  • Lizhou Wang,
  • Yuan Xu,
  • Qiaoqiao Gou,
  • Jing Wen,
  • Shi Zhou

摘要

Background

The pathogenic mechanisms underlying rheumatoid arthritis (RA) remain elusive. Lactylation, a novel post-translational modification, may regulate immune and metabolic reprogramming, underscoring the imperative to delineate lactylation-related genes (LRGs) driving RA progression.

Methods

LRG expression profiles from RA patients and healthy controls were analyzed from GEO datasets. Immune infiltration and LRG-immune correlations were assessed. A machine-learning framework identified a hub LRG signature, whose metabolic and therapeutic relevance was evaluated via functional enrichment and druggability analyses. qRT-PCR validated hub gene expression in RA MH7A cells model.

Results

Transcriptomic profiling identified 36 differentially expressed LRGs regulating cytokine networks and immune signaling in RA. Disease stratification revealed two molecular subtypes, with Subtype B demonstrating p53 signaling and innate immunity pathway activation via gene set variation analysis (GSVA). Weighted correlation network analysis (WGCNA) identified subtype B-associated modules (504 genes). A machine learning-derived 7-LRG signature (Sdc1, Pfkfb1, Fut8, Adh1b, Kif23, Adh1c, Pkc1) differentiated RA from controls (AUC 0.92). Single-cell resolution analysis localized Sdc1 to plasma cell clusters, correlating with memory B cell expansion and macrophage polarization. Hub LRGs were enriched in glucose metabolism pathways in RA, and Sdc1, Adh1b, and Adh1c were druggable, suggesting potential therapeutic targets. qRT-PCR validation confirmed significant LRG upregulation in RA cellular models.

Conclusion

Our findings establish lactylation as a key modulator of immune dysregulation in RA pathogenesis. Seven LRG biomarkers were identified and validated, exhibiting dual potential as prognostic indicators and therapeutic targets through lactylation-driven pathway modulation for RA.