Rheumatoid arthritis (RA) significantly accelerates atherosclerosis (AS) progression, with both conditions sharing common features of immune dysregulation and metabolic alterations, particularly abnormal lactate metabolism. This study aims to comprehensively investigate lactylation-related genes as potential diagnostic biomarkers for patients with concurrent RA and AS, and to explore the relationship between lactylation patterns and immune cell infiltration in the shared pathogenesis of these diseases.

We integrated bulk RNA sequencing data from two disease-related datasets (GSE89408 for RA and GSE43292 for AS) from the GEO database. After batch effect removal and normalization, differential gene expression analysis was performed using the limma package. We identified 336 lactylation-related genes through gene ontology and literature review, selecting 44 significantly differentially expressed genes shared between both diseases. Three machine learning algorithms—LASSO regression, Random Forest, and XGBoost—were employed to identify hub genes. Protein-protein interaction networks were constructed using STRING database. Functional enrichment analyses (GO and KEGG) were conducted using clusterProfiler. Immune cell infiltration was assessed by ssGSEA algorithm, and correlations between hub genes and immune cells were analyzed using Spearman correlation. Single-cell RNA sequencing data (PMID: 38954480) from 20 RA patients and 5 healthy controls were analyzed using Seurat and harmony packages, with lactylation scores calculated by GSVA. Finally, a collagen-induced arthritis (CIA) mouse model was established to validate hub gene expression through qRT-PCR.

We identified four lactylation-related hub genes (SMARCC2, CCNA2, NUP50, GATAD2B) through the intersection of three machine learning methods. These genes demonstrated high diagnostic potential with AUC values >0.88 (SMARCC2: 0.985; CCNA2: 0.927; NUP50: 0.95; GATAD2B: 0.88). Functional analysis revealed significant enrichment in immune-related processes, including cell-cell adhesion, cytokine production, and T cell activation. CCNA2 and NUP50 showed positive correlations with immune cell infiltration, while GATAD2B and SMARCC2 exhibited negative correlations. Single-cell analysis identified 14 distinct immune cell clusters, with monocytes, effector memory CD4+ T cells, memory CD8+ T cells, and naive CD8+ T cells showing relatively higher lactylation levels. Lactylation scores were strongly correlated with oxidative phosphorylation, MYC targets, and mTORC1 signaling pathways. Consensus clustering based on hub gene expression revealed two distinct molecular subtypes with different pathway activities. qPCR validation in CIA mice confirmed the bioinformatics findings.

This study identifies four lactylation-related hub genes (SMARCC2, CCNA2, NUP50, GATAD2B) as promising diagnostic biomarkers for concurrent RA and AS. These genes are significantly correlated with immune cell infiltration patterns, highlighting the critical role of lactylation in the shared pathogenic mechanisms of RA and AS. The single-cell analysis reveals distinct lactylation profiles across immune cell populations, particularly in pro-inflammatory cells. These findings provide novel insights into the metabolic-immune crosstalk in RA-related AS and establish a foundation for developing targeted therapeutic strategies for this high-risk patient population. Further clinical validation is warranted to translate these biomarkers into clinical practice.