ABS0432 LABEL-FREE QUANTITATIVE PROTEOMICS IDENTIFIES NOVEL PLASMA BIOMARKERS FOR DISTINGUISHING RHEUMATOID ARTHRITIS COMBINED WITH ACTIVE TUBERCULOSIS AND RHEUMATOID ARTHRITIS COMBINED WITH LATENT TUBERCULOSIS INFECTION

<h2>Abstract</h2><h3>Background:</h3> Patients with rheumatoid arthritis (RA) are at increased risk of tuberculosis infection, including active tuberculosis (ATB) and latent tuberculosis infection (LTBI). Treatment with immunosuppressive agents in patients with RA also increases the risk of ATB, the main reason for which is reactivation of LTBI. The molecular mechanisms behind progression from LTBI to ATB in RA patients are currently not elucidated. Screening of RA patients for tuberculosis infection is essential. The sensitivity and specificity of traditional screening methods such as tuberculin skin test and interferon-gamma release assays (IGRA) may be compromised in patients with RA, and there is a lack of validated differential diagnostic methods for RA combined with ATB and RA combined with LTBI at present. <h3>Objectives:</h3> In this study, we obtained the plasma proteomic profiles of RA combined with tuberculosis infection, which contribute to a better understanding of the pathogenesis involved in the transition from RA combined with LTBI to RA combined with ATB and provide new potential diagnostic biomarkers for distinguishing RA combined with ATB and RA combined with LTBI. <h3>Methods:</h3> We collected plasma from patients with RA combined with ATB and RA combined with LTBI for label-free quantitative proteomics technique to screen for differentially expressed proteins (DEPs). Enrichment analysis of DEPs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The pathways associated with RA or tuberculosis were screened through Comparative Toxicogenomics Database, and the corresponding proteins were identified. A protein-protein interaction (PPI) network was constructed by STRING, and data were visualized by Cytoscape. We used cytoHubba to identify hub DEPs and selected proteins that were the same as the corresponding proteins of the enrichment pathway as the final DEPs. <h3>Results:</h3> A total of 54 DEPs were screened in this study, among which 36 proteins were up-regulated and 18 proteins were down-regulated (Figure 1A). The intensity changes of the 54 DEPs are shown as a heat map in Figure 1B. We classified the 54 DEPs by GO analysis as Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). The results showed that the proteins were mainly concentrated in intracellular, organelle, membrane-bounded organelle, and intracellular organelle (Figure 2A). KEGG enrichment analysis showed that the DEPs were enriched in the pathways of Biosynthesis of unsaturated fatty acids, other types of o-glycan biosynthesis, fatty acid metabolism, nucleocytoplasmic transport, and carbon metabolism (Figure 2B). Carbon metabolism was associated with RA and we identified the corresponding DEPs (Table 1). The PPI network demonstrated that most of these DEPs closely interacted with each other (Figure 3A). The top 20 hub proteins were identified by cytoHubba (Figure 3B), which intersected with DEPs corresponding to pathways related to RA, and identified PHGDH as the hub DEPs (Figure 3C). <h3>Conclusion:</h3> PHGDH in plasma may be a potential biomarker for distinguishing RA combined with ATB and RA combined with LTBI. <h3>REFERENCES:</h3> <b>NIL</b>. Figure 1Volcano map and hierarchical cluster analysis of the 54 differentially expressed proteins (DEPs). RA_ATB, rheumatoid arthritis combined with active tuberculosis; RA_LTBI, Rheumatoid arthritis combined with latent tuberculosis infection. Figure 2Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the DEPs. Figure 3Bioinformatics analysis of the DEPs. (A) PPI network of DEPs. (B) Identification of the hub DEPs. (C) Intersection of DEPs corresponding to KEGG pathway related to RA with hub DEPs. Table 1DEPs corresponding to pathway related to RAKEGG PathwayProteinP-valueCarbon metabolismACOX30.0468533148386877PHGDH0.0112288970487983 <h3>Acknowledgements:</h3> <b>NIL</b>. <h3>Disclosure of Interests:</h3> <b>None declared</b>. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.