Introduction

Rheumatoid arthritis (RA) and cardiovascular disease (CVD) have overlapping pathophysiologic mechanisms involving inflammation, immunity, and oxidative stress.^1,2 Rheumatic diseases have been considered vital in the interplay between heart disease and inflammation.³ In the preclinical stage of RA, the self-tolerance of the immune system is decreased, and various autoantibodies are produced.⁴ This subsequently activates the immune system and ultimately leads to immune infiltration into the joint synovium. It is a complex process involving a large number of cytokines and pro-inflammatory cytokines, such as tumor necrosis factor-alpha and interleukin-1 (IL-1), which can stimulate the generation of reactive oxygen species and consequently lead to oxidative stress and cellular injury.^5,6

Although there is definite evidence for the shared mechanisms of RA and CVD, there is still a lack of studies at the molecular level. To date, the understanding of the genes associated with RA and CVD is still limited due to lacking of appropriate techniques and approaches. The increasing availability of large-scale genomic data, such as UK Biobank data, facilitates the investigation of CVD risk-related pathways among RA patients at the molecular level.⁷ Notably, recent Mendelian randomization (MR) studies have provided new insights into the causal relationships between RA and CVD.^8,9 For example, Qiu et al⁸ performed an MR analysis and reported that RA was potentially causally associated with 6 types of cardiovascular conditions, including age-related angina pectoris, hypertension, age-related heart attack, abnormal heart rate, stroke, and general heart disease. Similarly, Wang et al⁹ identified a causal relationship between RA and ischemic heart disease, as well as myocardial infarction (MI). Their study further suggested that reducing RA disease activity could potentially lower CVD risk. Based on the genome-wide data, Guo et al¹⁰ performed a conventional meta-analysis to assess the shared genetic architecture between RA and CVD using the UK Biobank. Their results supported the idea that there is shared genetic pathogenesis in explaining the observed association between RA and CVD.

To further investigate the molecular association between CVD and RA, disease-associated genes were systematically collected from the MalaCards and DisGeNET databases. Subsequently, functional enrichment analysis was conducted to identify the key biological processes and signaling pathways enriched in the shared genes, as well as their potential interactions. Finally, the potential hub genes were identified based on their central roles in the protein–protein interaction (PPI) network, which may be involved in the comorbidity of CVD and RA.

Methods

Selection of Rheumatoid Arthritis– and Cardiovascular Disease–Associated Genes from Databases

A flowchart of the study design is shown in Supplementary Figure 1. Rheumatoid arthritis– and CVD-associated genes were extracted from DisGeNET (https://www.disgenet.org/) and Malacards (https://www.malacards.org/).^11,12 Genes with a gene-disease association score > 0.05 were selected from the DisGeNET database, as this threshold indicates a strong disease association. Additionally, the selection of associated genes from Malacards was performed based on default parameters as described in the previous study.¹² After retrieving RA- and CVD-associated genes from each database, the shared genes between the 2 diseases were identified. These shared genes were considered as potential susceptibility genes contributing to the comorbidity of RA and CVD and were used for enrichment and network analyses.

Functional and Pathway Enrichment Analyses

To explore the biological significance of the shared genes between RA and CVD, a series of functional annotation and pathway enrichment analyses were performed. Firstly, GO analysis was conducted using WebGestalt (http://www.webgestalt.org), with a focus on biological processes significantly enriched among the shared genes [False discovery rate (FDR) < 0.05].¹³ To assess interactions at the protein level, a PPI network was constructed using Metascape (http://metascape.org/), and subnetworks were identified using the molecular complex detection algorithm.^14,15

Hub gene selection was performed using ClueGO, CluePledia and CytoHubba.^16,17 Pathway enrichment analysis was conducted with ClueGO and CluePledia, followed by the identification of key hub genes in the PPI network using the Maximal Clique Centrality algorithm in CytoHubba.

To further investigate functional relationships among biological pathways, a pathway cross-talk analysis was conducted. Enriched KEGG pathways (P < 0.05) were identified using ToppGene (https://toppgene.cchmc.org/enrichment.jsp, FDR < 0.05) based on RA- and CVD-associated genes. Cross-talk between pathways was quantified using the Jaccard Coefficient (A ∩ B / A ∪ B) and Overlap Coefficient (|A ∩ B| / min(|A|, |B|)) to assess gene overlap between pathway pairs, where A and B represent the sets of genes in 2 pathways.^18-20 The pathway interaction network was visualized using Cytoscape (version 3.9.0), providing insight into functionally connected pathways potentially contributing to RA–CVD comorbidity.

Identification of Candidate Genes Through Protein–Protein Interaction Network Analysis

We first mapped the RA-associated genes and CVD-associated genes into the PPI network, which yielded an RA-specific network and a CVD-specific network, respectively. To exclude the irrelevant interactions, the RA-specific network and CVD-specific network were merged into a combined network. Subsequently, the RA-specific network was compared with the CVD-specific network, followed by the extraction of the overlapping network. The Cytoscape software was utilized to calculate the node degree of the genes using the Network Analyzer.^21,22 Then nodes with a degree of 5 or more were selected as candidate genes after removing the RA-associated and CVD-associated genes. For validation, the specific PPI network was also obtained from the STRING database and merged a combined network.

Expression Analysis of Candidate Genes from databases

To explore the tissue and cell-type-specific expression patterns of the candidate genes, an in silico expression analysis was performed using the Tabula Muris database (https://tabula-muris.ds.czbiohub.org/). The Tabula Muris Senis (TMS) dataset is a large-scale, publicly available single-cell RNA-seq dataset of mice. All cells in the dataset have been annotated with cell types by the TMS project. Log-transformed, pre-processed data was obtained from the TMS dataset, which comprises 2 subsets generated using distinct experimental methodologies: fluorescence-activated cell sorting (FACS) and droplet-based sequencing. Using FACS methods, the expression of predicted genes was analyzed in various tissues, including heart tissue, and in different cells.

Results

Identification and Selection of Shared Genes

Rheumatoid arthritis– and CVD-associated genes were retrieved from the DisGeNET and Malacards databases using defined thresholds. Specifically, 290 RA-related genes and 210 CVD-related genes were retrieved from the MalaCards database, and 787 RA-related genes and 433 CVD-related genes from the DisGeNET databases (Supplementary Table 1). Among these genes, 108 shared genes were identified between RA and CVD (Table 1). These shared genes comprise immune-related genes (e.g., CDKN2A, ICAM1, IFNG, TNF), oxidative stress-related genes (e.g., LPA, HIF1A, NOS2, NOS3), and interleukin-related genes (e.g., IL6, IL10, IL1B, IL17A, IL18).

Functional Annotation of the Shared Genes

Gene ontology (GO) enrichment analysis was then performed on the 108 genes, which showed that 10 GO biological processes were significantly enriched (Supplementary Table 2). Among these processes, immune responses were the most significant, followed by secretion by cells, leukocyte activation, and immune effector process. Figure 1 showed the enrichment results for the biological process, cellular component (CC), and molecular function (MF) terms are shown. Notably, the significantly enriched categories included biological regulation, response to stimulus, and multicellular organismal processes. In the CC terms and MF terms, the enrichment items included extracellular space, membrane and nucleus, protein binding, ion binding, and nucleic acid binding.

Protein–Protein Interaction Network Construction for Shared Genes

A total of 6 gene modules (i.e., module 1-6) were generated after mapping all the shared genes onto the PPI network (Figure 2). These modules were mainly associated with key biological functions, including inflammatory response, interleukin signaling transmission, cytokine signaling transmission in the immune system, and lipid metabolism (Table 2).

Hub Genes Selection from the Interaction Network

As shown in Figure 3, 2554 pathway interactions involving 175 nodes were identified. Enriched pathways included lipids and atherosclerosis (AS) signaling, fluid shear stress and AS, as well as the RA and AS. Moreover, the results showed that the AGE-RAGE signaling pathway was enriched in diabetic complications, together with the HIF-1, TNF, and Toll-like receptor signaling pathways. Furthermore, 10 hub genes were identified from the network, including IL-10, IL-1B, TNF, IL-6, AKT1, MMP9, CXCL8, ICAM1, VCAM1, and IL-1A.

Pathway Enrichment of Rheumatoid Arthritis– and Cardiovascular Disease–Associated Genes

Pathway enrichment analysis revealed 69 significant pathways for RA and 48 for CVD (Supplementary Table 3). After overlapping these enriched pathways, 40 shared pathways were obtained (Supplementary Table 4). Some of the shared pathways were associated with the T cell receptor signaling pathway, B cell receptor signaling pathway, chemokine signaling pathway, and leukocyte trans-endothelial migration. In addition, others were associated with signaling transmission, such as the Janus kinase/signal transducer and activator of transcriptio (JAK-STAT) signaling pathway, mitogen-activated protein kinases (MAPK) signaling pathway, the cytokine-cytokine receptor interaction, as well as the endocrine system and cancer-related pathways.

Pathway Cross-Talk Between Rheumatoid Arthritis and Cardiovascular Disease

Among the 40 shared pathways, 38 shared at least 3 genes with at least 1 other pathway and were included in the cross-talk analysis. Subsequently, a pathway interaction network was constructed based on shared genes to explore the underlying biological processes. In total, 52 out of the 108 shared genes were mapped to this network, resulting in 90 nodes and 734 edges (Figure 4). The network was classified into 4 functional modules, including the immune system, endocrine or metabolic system, cancer-related, and signaling transmission. Interestingly, these modules were interconnected through 1 or more key signaling pathways, suggesting coordinated biological relevance across disease mechanisms.

Selection of Candidate Genes Associated with Rheumatoid Arthritis and Cardiovascular Disease

All RA- and CVD-associated genes were mapped onto a PPI network, generating 957 nodes (540 RA-associated and 417 CVD-associated) and 9272 edges (2425 RA-associated and 6747 CVD-associated). Subsequently, a combined network including 867 nodes and 8973 edges was established to identify genes potentially linked to both diseases. According to the node degree, 42 candidate genes that were directly linked to the shared genes were selected with a score of 20 or more (Table 3). Among these genes, 21 genes showed direct association with 5 or more shared genes. In addition, 7 genes (i.e., IFNG, CCL5, CXCL10, FN1, EGFR, CXCL1, and CD44) showed direct association with 9 or more shared genes. The PPI network of the 7 selected candidate genes (Figure 5), which led to the generation of 102 nodes and 673 edges.

Expression Analysis of Candidate Genes from Databases

In this section, tissue- and cell-specific expression analyses of the 21 novel candidate genes were performed. All 21 novel candidate genes were RA-associated, suggesting that these genes may be involved in the molecular mechanisms of CVD. As shown in Figures 6 and 7, the FN1, EGFR, JUN,CXCL1, and RELA were extensively expressed in cardiac tissue. At the same time, FN1, EGFR, JUN,CXCL1, and RELA were extensively expressed in fibroblasts of cardiac tissue. Moreover, CD44, ITGAM, CCL2, CCL4, and CCL3 were specifically expressed in leukocytes in cardiac tissue.

Discussion

In this study, the 108 shared genes between CVD and RA were systematically analyzed. Functional enrichment analyses revealed that these shared genes are involved in immune responses, inflammatory signaling, cytokine activity, and lipid metabolism. Among them, inflammation-related and immune signaling pathways were particularly prominent. Based on degree centrality in the PPI network, 42 candidate genes were identified, of which 7 (i.e., IFNG, CCL5, CXCL10, FN1, EGFR, CXCL1, and CD44) showed direct connections to 9 or more shared genes and were highlighted for further analysis.

Rheumatoid arthritis has been consistently associated with an elevated risk of CVD, which is a leading cause of mortality in this population.²³ This may be attributed to the chronic inflammatory state characteristic of RA, which is marked by elevated levels of circulating inflammatory mediators and endothelial dysfunction.^24,25 This in turn, may promote the AS and cardiomyocyte dysfunction, thereby increasing the risk of CVD, MI, and congestive heart failure.²⁶ The understanding of how susceptibility genes contribute to the interplay between CVD and RA is still limited. To address this, a systematic analysis of the shared genes was conducted between CVD and RA. Enriched analysis identified key pathways, including lipids and AS signaling, fluid shear stress and AS, as well as the RA and AS. These findings highlight potential molecular mechanisms underlying the increased CVD risk in RA patients and may guide future therapeutic strategies targeting shared pathogenic pathways.

Chronic inflammation is a central feature in the pathogenesis of both RA and CVD.²⁷ Lipid abnormalities, particularly the impaired atheroprotective function of high-density lipoprotein, are recognized as key contributors to the increased risk of atherosclerotic cardiovascular disease in RA patients.²⁸ Consistently, the shared gene modules in the PPI network encompassed interleukin signaling, cytokine-cytokine receptor interaction, and pathways regulating inflammation resolution. Additionally, pathways related to lipid metabolism and arachidonic acid were significantly enriched, supporting evidence that altered lipid profiles and inflammatory lipoproteins contribute to the pathogenesis of CVD in RA patients.^29,30 These findings reinforce that inflammation and lipid dysregulation may constitute shared pathological mechanisms driving CVD in the context of RA.

To elucidate key molecular players bridging RA and CVD, we constructed a combined PPI network was constructed and candidate genes were identified based on their connectivity to shared disease-associated genes. Notably, 7 candidate genes that were found showed a direct link to at least 9 genes, including IFNG, CCL5, CXCL10, FN1, EGFR, CXCL1, and CD44. These genes have well-established roles in immune regulation and inflammation. IFNG encodes interferon-gamma (IFN-γ), a key cytokine secreted by both innate and adaptive immune systems. Variants in IFNG have been associated with increased susceptibility to infections and autoimmune diseases,³¹ both of which are implicated in the pathogenesis of RA and CVD.^32,33 These findings suggest that IFNG may be involved in CVD and RA by regulating immune responses and inflammatory pathways. CCL5 encodes a member of the chemokine superfamily involved in immunoregulatory and inflammatory processes.³¹ CCL5-related ankylosing spondylitis was associated with hypertension and the development of obesity, both of which were common risk factors for CVD.³⁴ CXCL1 is also associated with inflammation and the accumulation of neutrophils. In CVD, CXCL1 was crucial in cardiac fibrosis, especially induced by atrial fibrillation, post-irradiation, as well as hypertension.³⁵ Likewise, the role of CXCL10 in CVD has been extensively described,³⁶ particularly in promoting immune cell infiltration via CXCR3. Additionally, Lee et al³⁷ demonstrated that CXCL10 signaling through CXCR3 and TLR4 enhances inflammatory cell migration, potentially contributing to the progression of RA.

Notably, FN-1 has been identified as a key gene associated with RA onset.³⁸ Using bioinformatics methods, Xiong et al³⁹ identified FN-1 as a novel biomarker for aortic valve calcification, an important event in the development of CVD. In a mouse model of collagen-induced arthritis, FN-1 expression was linked to over a 3-fold increased risk of RA, further supporting its role in disease pathogenesis.⁴⁰ The EGFR family and its ligands function as central regulators of multiple cellular processes. Epidermal growth factor receptor (EGFR) signaling is essential for cardiac development and remodeling and has been proposed as a therapeutic target in CVD.⁴¹ Additionally, EGFR contributes to synovial hyperplasia in RA through its roles in angiogenesis and tissue regulation.^42,43 CD44 expression is significantly elevated in diseased arterial tissues and inflammatory cytokine-stimulated endothelial cells.⁴⁴ The CD44-hyaluronic acid axis plays a critical role in inflammatory responses and AS pathogenesis, suggesting its potential as a therapeutic target for CVD.⁴⁵ In RA, CD44 is highly expressed in inflamed synovial tissues compared to normal synovium, indicating its relevance in disease progression and its potential for targeted drug delivery.⁴⁶

These 7 candidate genes represent potential molecular links between CVD and RA and may serve as future therapeutic targets. However, it is important to note that these findings are based on bioinformatics and in silico predictions. Functional validation is needed through experimental models and clinical cohorts to confirm causality and therapeutic relevance. In particular, interventions targeting IFNG or EGFR signaling could be explored for dual impact on inflammation and cardiovascular outcomes in RA patients. Similarly, modulation of chemokines such as CCL5 and CXCL10 may help reduce both synovial and vascular inflammation.

There are some limitations in this study. First, the analysis relied on publicly available databases, which may introduce biases or incomplete gene annotations. Second, the current human interactome is still not complete, and there might be some errors despite significant improvement in the quality of PPI databases. Third, the functional roles of candidate genes require further experimental validation, such as gene knockout or overexpression studies.

Conclusion

This study identified 108 shared genes between CVD and RA, with enrichment analyses highlighting their roles in immune and inflammatory processes. Among these, 7 candidate genes were considered as potential key mediators in the shared pathogenic mechanisms. These findings provide new insights into common molecular mechanisms and may offer promising targets for future diagnostic or therapeutic strategies.

Supplementary Materials

Footnotes

Ethics Committee Approval: All data used in this study are publicly available, and studies are approved by relevant review boards and conducted according to the Declaration of Helsinki, with written informed consent from all participants. No additional ethical approval was required.

Peer-review: Externally peer-reviewed.

Author Contributions: Conception and design: Yaobang Bai, Yunpeng Bai, Nan Jiang; database search and data extraction: Yaobang Bai, Yunpeng Bai; study evaluation: Zhenhua Wu, Qingliang Chen; planned and conducted the statistical analysis: Yaobang Bai, Zhenhua Wu; drew all the figures and tables: Yunpeng Bai, Qingliang Chen; drafted the manuscript: Yaobang Bai, Yunpeng Bai; corrected and validated the manuscript: Nan Jiang. All authors read and approved the final manuscript.

Declaration of Interests: The authors have no conflicts of interest to declare.

References

1. Agca R, Heslinga SC, Rollefstad S. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update. Ann Rheum Dis. 2017;76(1):17-28.
2. Yuan S, Carter P, Mason AM, Yang F, Burgess S, Larsson SC. Genetic liability to rheumatoid arthritis in relation to coronary artery disease and stroke risk. Arthritis Rheumatol. 2022;74(10):1638-1647.
3. Crowson CS, Liao KP, Davis JM. Rheumatoid arthritis and cardiovascular disease. Am Heart J. 2013;166(4):622-628.e1.
4. Weyand CM, Goronzy JJ. The immunology of rheumatoid arthritis. Nat Immunol. 2021;22(1):10-18.
5. Bhol NK, Bhanjadeo MM, Singh AK. The interplay between cytokines, inflammation, and antioxidants: mechanistic insights and therapeutic potentials of various antioxidants and anti-cytokine compounds. Biomed Pharmacother. 2024;178():117177-.
6. Kany S, Vollrath JT, Relja B. Cytokines in inflammatory disease. Int J Mol Sci. 2019;20(23):6008-.
7. Leonard D, Svenungsson E, Dahlqvist J. Novel gene variants associated with cardiovascular disease in systemic lupus erythematosus and rheumatoid arthritis. Ann Rheum Dis. 2018;77(7):1063-1069.
8. Qiu S, Li M, Jin S, Lu H, Hu Y. Rheumatoid arthritis and cardio-cerebrovascular disease: a Mendelian randomization study. Front Genet. 2021;12():745224-.
9. Wang M, Chao C, Mei K. Relationship between rheumatoid arthritis and cardiovascular comorbidity, causation or co-occurrence: a Mendelian randomization study. Front Cardiovasc Med. 2023;10():1099861-.
10. Guo Y, Chung W, Shan Z, Zhu Z, Costenbader KH, Liang L. Genome-wide assessment of shared genetic architecture between rheumatoid arthritis and cardiovascular diseases. J Am Heart Assoc. 2023;12(22):e030211-.
11. Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48(D1):D845-D855.
12. Rappaport N, Twik M, Plaschkes I. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017;45(D1):D877-D887.
13. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(Web Server issue):W77-W83.
14. Zhou Y, Zhou B, Pache L. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523-.
15. Fan N, Yuan S, Hai Y. Identifying the potential role of IL-1β in the molecular mechanisms of disc degeneration using gene expression profiling and bioinformatics analysis. J Orthop Surg (Hong Kong). 2022;30(1):23094990211068203-.
16. Thomas DM, Kannabiran C, Balasubramanian D. Identification of key genes and pathways in persistent hyperplastic primary vitreous of the eye using bioinformatic analysis. Front Med (Lausanne). 2021;8():690594-.
17. Tang L, Huang L, Lai Y. Network pharmacology and bioinformatics analyses identify the intersection genes and mechanism of Huang Bai for recurrent aphthous stomatitis. Int J Immunopathol Pharmacol. 2022;36():3946320221129134-.
18. Jia P, Kao CF, Kuo PH, Zhao Z. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol. 2011;5(Suppl):S12-.
19. Liu M, Fan R, Liu X, Cheng F, Wang J. Pathways and networks-based analysis of candidate genes associated with nicotine addiction. PLoS One. 2015;10(5):e0127438-.
20. Guo P, Meng C, Zhang S. Network-based analysis on the genes and their interactions reveals link between schizophrenia and Alzheimer’s disease. Neuropharmacology. 2024;244():109802-.
21. Killcoyne S, Carter GW, Smith J, Boyle J. Cytoscape: a community-based framework for network modeling. Methods Mol Biol. 2009;563():219-239.
22. Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24(2):282-284.
23. Gabriel SE. Cardiovascular morbidity and mortality in rheumatoid arthritis. Am J Med. 2008;121(10 ):S9-S14.
24. Yang X, Chang Y, Wei W. Endothelial dysfunction and inflammation: immunity in rheumatoid arthritis. Mediators Inflamm. 2016;2016():6813016-.
25. Maiuolo J, Muscoli C, Gliozzi M. Endothelial dysfunction and extra-articular neurological manifestations in rheumatoid arthritis. Biomolecules. 2021;11(1):81-.
26. Avina-Zubieta JA, Thomas J, Sadatsafavi M, Lehman AJ, Lacaille D. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2012;71(9):1524-1529.
27. Fragoulis GE, Panayotidis I, Nikiphorou E. Cardiovascular risk in rheumatoid arthritis and mechanistic links: from pathophysiology to treatment. Curr Vasc Pharmacol. 2020;18(5):431-446.
28. DeMizio DJ, Geraldino-Pardilla LB. Autoimmunity and inflammation link to cardiovascular disease risk in rheumatoid arthritis. Rheumatol Ther. 2020;7(1):19-33.
29. Navarro-Millán I, Yang S, DuVall SL. Association of Hyperlipidaemia, inflammation and serological status and coronary heart disease among patients with rheumatoid arthritis: data from the National Veterans Health Administration. Ann Rheum Dis. 2016;75(2):341-347.
30. Zhang J, Chen L, Delzell E. The association between inflammatory markers, serum lipids and the risk of cardiovascular events in patients with rheumatoid arthritis. Ann Rheum Dis. 2014;73(7):1301-1308.
31. Fagerberg L, Hallström BM, Oksvold P. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. 2014;13(2):397-406.
32. Bo M, Jasemi S, Uras G, Erre GL, Passiu G, Sechi LA. Role of infections in the pathogenesis of rheumatoid arthritis: focus on mycobacteria. Microorganisms. 2020;8(10):1459-.
33. Reali E, Ferrando-Martinez S, Catalfamo M. Editorial: The interplay between immune activation and cardiovascular disease during infection, autoimmunity and aging: the role of T cells. Front Immunol. 2021;12():719517-.
34. Jones KL, Maguire JJ, Davenport AP. Chemokine receptor CCR5: from AIDS to atherosclerosis. Br J Pharmacol. 2011;162(7):1453-1469.
35. Wu CL, Yin R, Wang SN, Ying R. A review of CXCL1 in cardiac fibrosis. Front Cardiovasc Med. 2021;8():674498-.
36. Lu X, Wang Z, Ye D. The role of CXC chemokines in cardiovascular diseases. Front Pharmacol. 2021;12():765768-.
37. Lee JH, Kim B, Jin WJ, Kim HH, Ha H, Lee ZH. Pathogenic roles of CXCL10 signaling through CXCR3 and TLR4 in macrophages and T cells: relevance for arthritis. Arthritis Res Ther. 2017;19(1):163-.
38. Yang J, Zhang Y, Liang J, Yang X, Liu L, Zhao H. Fibronectin-1 is a dominant mechanism for rheumatoid arthritis via the mediation of synovial fibroblasts activity. Front Cell Dev Biol. 2022;10():1010114-.
39. Xiong T, Han S, Pu L. Bioinformatics and machine learning methods to identify FN1 as a novel biomarker of aortic valve calcification. Front Cardiovasc Med. 2022;9():832591-.
40. Gwon SY, Rhee KJ, Sung HJ. Gene and protein expression profiles in a mouse model of collagen-induced arthritis. Int J Med Sci. 2018;15(1):77-85.
41. Makki N, Thiel KW, Miller FJ. The epidermal growth factor receptor and its ligands in cardiovascular disease. Int J Mol Sci. 2013;14(10):20597-20613.
42. Larsen AK, Ouaret D, El Ouadrani K, Petitprez A. Targeting EGFR and VEGF(R) pathway cross-talk in tumor survival and angiogenesis. Pharmacol Ther. 2011;131(1):80-90.
43. Li Z, Xu M, Li R. Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses. Biosci Rep. 2020;40(9):BSR20201713-.
44. Zhang L, Yang P, Chen J. CD44 connects autophagy decline and ageing in the vascular endothelium. Nat Commun. 2023;14(1):5524-.
45. Krolikoski M, Monslow J, Puré E. The CD44-HA axis and inflammation in atherosclerosis: a temporal perspective. Matrix Biol. 2019;78():201-218.
46. Gorantla S, Gorantla G, Saha RN, Singhvi G. CD44 receptor-targeted novel drug delivery strategies for rheumatoid arthritis therapy. Expert Opin Drug Deliv. 2021;18(11):1553-1557.