Hereditas · 2026
Identification and validation of lactylation-related genes signature and immune infiltration landscape of rheumatoid arthritis based on machine learning.
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Hu Xiaoli, Xiao Qian, Wang Lizhou, Xu Yuan, Gou Qiaoqiao, Wen Jing, Zhou Shi
Аннотация
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.
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.