Abstract Background Rheumatoid arthritis (RA) is a systemic autoimmune disorder marked by chronic synovial inflammation and progressive joint damage. Increasing evidence points to non-coding RNAs, particularly circular RNAs (circRNAs), as crucial regulators of immune and inflammatory responses in RA. However, their functional roles and clinical relevance remain incompletely understood. Methods We conducted an integrative analysis combining bioinformatics and experimental validation to investigate the expression profiles of circRNAs and their host genes in RA. Transcriptomic datasets ([29]GSE124373, [30]GSE169082, [31]GSE189338) were analyzed to identify differentially expressed mRNAs, circRNAs, and miRNAs in PBMCs of RA patients. Functional enrichment, protein–protein interaction (PPI) network construction, and competing endogenous RNA (ceRNA) regulatory analyses were performed. Subsequently, qPCR validation was carried out in clinical samples from 25 RA patients and 25 healthy controls. Results Analysis revealed 1366 differentially expressed mRNAs, 47 circRNAs, and 223 miRNAs. Notably, hsa_circ_0092125 and its host gene G6PD were significantly upregulated in RA samples. The ceRNA network indicated their involvement in immune-inflammatory pathways. qPCR validation confirmed elevated expression of hsa_circ_0092125 (fold change = 4.35, P = 0.0114) and G6PD (fold change = 2.23, P = 0.0048). ROC (Receiver Operating Characteristic) curve analysis demonstrated moderate diagnostic value, particularly for G6PD (area under the curve (AUC) = 0.7824). Conclusion Our integrative bioinformatics and experimental approach identify hsa_circ_0092125 and G6PD as potential biomarkers for RA. These findings enhance our understanding of the molecular mechanisms underlying RA pathogenesis and suggest new avenues for biomarker development and targeted therapies. Keywords: Rheumatoid arthritis, circRNA, hsa_circ_0092125, G6PD, Gene expression profiling, Bioinformatics analysis, qPCR validation, Biomarkers Highlights * • hsa_circ_0092125 and G6PD are upregulated in rheumatoid arthritis (RA) patients. * • G6PD demonstrated notable diagnostic potential with an area under the curve (AUC) of 0.7824 in receiver operating characteristic (ROC) analysis. * • The ceRNA regulatory function of hsa_circ_0092125 in RA pathogenesis was highlighted. 1. Introduction Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by an abnormal immune response targeting synovial tissue, leading to inflammation, joint deformity, and loss of function. It affects approximately 1 % of the global population, with a higher prevalence in women and peak onset between the ages of 40 and 70. RA is associated with a reduced life expectancy of 3–10 years, primarily due to complications such as cardiovascular disease and secondary infections [[32]1]. The heterogeneity of RA phenotypes suggests that both genetic and environmental factors contribute to disease susceptibility, with an estimated heritability of approximately 66 % [[33]2]. Despite significant advancements in understanding RA pathogenesis, its precise etiology remains unclear [[34]3]. Several conventional biomarkers, such as rheumatoid factor (RF), anti-CCP antibodies, and CRP have been widely used for RA diagnosis; however, they lack sufficient sensitivity and specificity [[35]4]. Thus, integrative research approaches utilizing both in vitro and in vivo models are essential for identifying diagnostic biomarkers and elucidating the immune mechanisms underlying RA. Non-coding RNAs (ncRNAs), which constitute over 98 % of the human genome, play crucial roles in regulating autoimmunity and inflammation [[36]4,[37]5]. Recent advances in bioinformatics and high-throughput sequencing have facilitated the identification of numerous ncRNAs implicated in autoimmune diseases [[38]6]. Among them, circular RNAs (circRNAs) represent a novel class of endogenous ncRNAs characterized by a covalently closed-loop structure. Unlike linear RNAs, circRNAs lack 5′ and 3′ ends, rendering them resistant to exonuclease degradation and conferring a prolonged half-life exceeding 48 h. Additionally, circRNAs exhibit tissue-specific expression and evolutionary conservation, highlighting their potential as stable molecular regulators in autoimmune diseases [[39]7]. Given their tissue-specific expression patterns, regulatory functions in immune signaling, and remarkable stability, non-coding RNAs, including circular RNAs, have garnered attention as promising biomarkers for autoimmune diseases such as rheumatoid arthritis [[40]8]. The advent of RNA sequencing (RNA-seq) has enabled the identification of differentially expressed genes (DEGs), circRNAs, and microRNAs (miRNAs), while bioinformatics tools have revolutionized the study of disease-associated transcriptomes. Integrative bioinformatics analyses facilitate the construction of regulatory networks, such as the competing endogenous RNA (ceRNA) network, and the identification of key molecular interactions and signaling pathways driving RA pathogenesis. hsa_circ_0092125 was selected for further investigation based on its significant upregulation in the bioinformatic analysis and its central role within the predicted ceRNA regulatory network. Although functional studies on this circRNA are currently limited, prior reports of its dysregulation in diseases such as oral squamous cell carcinoma suggest that it may exert context-dependent regulatory effects, potentially relevant to immune-mediated conditions such as RA [[41]9].This study employs RNA-seq data derived from peripheral blood mononuclear cells (PBMCs) of RA patients and aims to identify novel biomarkers and elucidate the underlying molecular mechanisms of the disease. Through this integrative approach, we investigate the roles of circRNAs, miRNAs, and mRNAs in RA, focusing on their potential as diagnostic markers and therapeutic targets. 2. Materials and methods 2.1. Data collection and identification of DEGs We used three datasets ([42]GSE124373, [43]GSE169082, and [44]GSE189338) to obtain mRNA expression data from the NCBI Gene Expression Omnibus (GEO) database ([45]https://www.ncbi.nlm.nih.gov/geo/) [[46]10]. The first dataset ([47]GSE124373) provides non-coding RNA profiling data through microarray analysis, comparing PBMC samples from 28 RA patients and 18 healthy controls (HC). The second dataset ([48]GSE169082) includes 7 samples, comprising 4 from RA patients and 3 from HC. The third dataset ([49]GSE189338) presents the expression profile of circular RNA (circRNA) in PBMCs of RA patients. Differential expression analysis was conducted using GEO2R, with DEGs identified based on a threshold of |Log2 Fold Change| > 1 and an adjusted p-value <0.001. For miRNA differential expression, the limma package [[50]11] was used, considering a significance threshold of p-value ≤0.05. CircRNA differential expression analysis was performed using a cutoff of |Log2 Fold Change| > 1 and a p-value <0.005 to identify significant circRNAs. 2.2. Gene functional enrichment analysis and integration of protein-protein interaction (PPI) networks To investigate the biological significance of differentially expressed mRNAs (DEMs), DAVID (v2023q4) [[51]12] was used for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The STRING database was utilized to conduct a PPI network analysis of differentially expressed mRNAs, applying a minimum interaction score threshold of 0.40 [[52]13]. To represent the PPI network, Cytoscape software was used, and hub genes were determined utilizing the Degree algorithm featured in the CytoHubba plugin [[53]14]. 2.3. Construction of a ceRNA network For circRNA analysis, the CircMine database was used to retrieve information on differentially expressed circRNAs (DECs) and predict miRNAs targeting these circRNAs [[54]15]. Moreover, the StarBase database (v2.0) was used to predict miRNA-target interactions involving the hub genes [[55]16]. 2.4. Blood collection, RNA extraction, and real-time PCR analysis We collected fasting blood samples from 25 RA patients and 25 HCs at Shahid Mohammadi Hospital, Hormozgan University of Medical Sciences. Using the Ficoll density gradient centrifugation method, we isolated PBMCs from blood samples, which were processed within 2 h of collection to ensure optimal viability. The procedure involved diluting blood 1:1 with PBS and carefully layering it onto 5 mL of Ficoll solution, followed by centrifugation at 3000 RPM for 20 min. The PBMC layer was isolated and washed twice with PBS at 3000 RPM for 5 min each to minimize contamination. Total RNA extraction was performed from the PBMC pellet, followed by cDNA synthesis using a cDNA synthesis kit (Sinaclon, Tehran, Iran). Quantitative PCR (qPCR) was conducted using the Mic qPCR system and SYBR Green master mix (Ampliqon, Denmark). To validate the expression levels of the circRNA hsa_circ_0092125 and its host gene G6PD, qPCR was performed with B2M as the internal reference gene. Detailed primer sequences for the analyzed genes are provided in [56]Table 1. Table 1. List of the top 15 hub genes identified from the PPI network analysis in RA. The table includes gene symbols, full gene descriptions, gene types, Ensembl Gene IDs, log2 fold changes, adjusted p-values (padj), and raw p-values derived from differential expression analysis. All listed genes are significantly upregulated in RA patients compared to healthy controls, highlighting their potential roles in RA pathogenesis. Symbol Description GeneType EnsemblGeneID log2FoldChange padj pvalue CD68 CD68 molecule protein-coding ENSG00000129226 1.1599819 0.0000376 0.00000191 CXCL8 C-X-C motif chemokine ligand 8 protein-coding ENSG00000169429 6.7427091 2.19E-11 1.59E-13 FOS Fos proto-oncogene, AP-1 transcription factor subunit protein-coding ENSG00000170345 2.1976694 1.04E-09 1.18E-11 GAPDH glyceraldehyde-3-phosphate dehydrogenase protein-coding ENSG00000111640 1.2692191 7.13E-09 9.39E-11 HIF1A hypoxia inducible factor 1 subunit alpha protein-coding ENSG00000100644 1.6909649 4.56E-11 3.65E-13 ICAM1 intercellular adhesion molecule 1 protein-coding ENSG00000090339 3.2117445 3.33E-19 7.33E-22 IL1A interleukin 1 alpha protein-coding ENSG00000115008 6.8660297 0.000268 0.00002 IL1B interleukin 1 beta protein-coding ENSG00000125538 7.2262708 1.14E-15 4.36E-18 JUN Jun proto-oncogene, AP-1 transcription factor subunit protein-coding ENSG00000177606 4.8212122 3.39E-35 1.68E-38 MYC MYC proto-oncogene, bHLH transcription factor protein-coding ENSG00000136997 1.1460997 0.00000153 4.51E-08 MYD88 MYD88 innate immune signal transduction adaptor protein-coding ENSG00000172936 1.1346723 0.000000578 1.47E-08 NFKBIA NFKB inhibitor alpha protein-coding ENSG00000100906 4.5275376 1.49E-33 9.59E-37 PTGS2 prostaglandin-endoperoxide synthase 2 protein-coding ENSG00000073756 4.1026924 3.97E-19 9.03E-22 TLR2 toll like receptor 2 protein-coding ENSG00000137462 1.5499023 2.22E-11 1.63E-13 TNF tumor necrosis factor protein-coding ENSG00000232810 3.9356815 0.000163 0.000011 [57]Open in a new tab 2.5. Data visualization and statistics Statistical analysis was performed using GraphPad Prism 8.4.3 (GraphPad Software, Boston, Massachusetts, USA). Data normality was assessed using the Shapiro-Wilk test, and due to the non-parametric nature of the data, the Mann-Whitney U test was applied for two-group comparisons. Spearman's rank correlation coefficient was used for correlation analysis. Quantitative real-time PCR (qPCR) data were analyzed using the Pfaffl method, which accounts for differences in amplification efficiency between target and reference genes, improving upon the ΔΔCt method by incorporating efficiency corrections. For data visualization, Cytoscape was used to construct and analyze PPI and ceRNA networks, enabling the visualization of complex biological interactions [[58]17], while SRPlot was employed to generate statistical plots, providing detailed visualization of gene expression and enrichment analysis [[59]18]. 3. Results 3.1. DEGs in the RA A total of 1366 DEMs were identified, exhibiting significant alterations in PBMCs between patients with RA and HC. Among these, 919 DEMs showed up-regulation, while 447 were down-regulated. Additionally, 47 DECs were found in RA PBMCs compared to the control group. Of these, 7 circRNAs were significantly up-regulated, whereas 39 were down-regulated. From the [60]GSE124373 dataset, we identified 223 differentially expressed miRNAs (DEMIs), including 124 up-regulated and 99 down-regulated miRNAs. Volcano plots were utilized to illustrate and visually represent the expression differences of these DEMs, DEMIs, and DECs within datasets ([61]Fig. 1A–C). Hierarchical clustering heat maps highlighted expression profiles, and plots showcased expression patterns of DEMs among samples ([62]Fig. 1D). Fig. 1. [63]Fig. 1 [64]Open in a new tab Differential expression analysis of genes in RA compared to HC. (A) Volcano plots illustrating the differential expression of circRNAs ([65]GSE189338), (B) miRNAs ([66]GSE124373), and (C) mRNAs ([67]GSE169082) in RA compared to the HC group. Genes meeting significance thresholds are colored, while non-significant genes are shown in gray. (D) Heatmaps reveal the hierarchical clustering of hub gene expression, providing insight into the variations in expression among individual samples. Red shading indicates up-regulated genes, while green shading indicates down-regulated ones. Each column represents a sample: RA1–RA3 are patients, N1–N3 are healthy controls. 3.2. Functional enrichment analysis of DEMs To gain insights into the biological significance of DEMs, a pathway enrichment analysis was conducted. The results revealed several significantly enriched biological processes, cellular components, and molecular functions associated with both up-regulated and down-regulated DEMs. Among the biological processes, up-regulated DEMs were primarily associated with immune response pathways, including inflammatory response, cellular response to lipopolysaccharide, cytokine-mediated signaling pathway, apoptotic process, neutrophil chemotaxis, and innate immune response, highlighting their potential role in immune regulation and inflammatory mechanisms. On the other hand, down-regulated DEMs were significantly enriched in pathways related to transcriptional regulation and immune modulation, such as regulation of DNA-templated transcription, regulation of transcription by RNA polymerase II, positive regulation of natural killer cell-mediated cytotoxicity, stimulatory C-type lectin receptor signaling pathway, and regulation of natural killer cell activation. Additionally, pathways associated with sensory perception, such as the detection of chemical stimulus involved in the sensory perception of bitter taste, were also enriched in down-regulated DEMs ([68]Fig. 2A and B). Fig. 2. [69]Fig. 2 [70]Open in a new tab Comprehensive functional enrichment analysis was conducted to elucidate the roles of DEMs and implicated pathways in RA. (A) GO enrichment analysis for BP, CC, and MF of up-regulated DEMs. Bubble size represents the number of genes, while color indicates the -log10(P-value). (B) GO enrichment analysis of down-regulated DEMs. (C) KEGG pathway analysis illustrating the most significantly enriched pathways, with up-regulated DEMs shown at the top and down-regulated DEMs at the bottom. Bar lengths reflect gene counts, and shading intensity represents the degree of statistical significance. In terms of cellular components, up-regulated DEMs were enriched in extracellular and membrane-related components, including extracellular exosome, plasma membrane, ficolin-1-rich granule membrane, tertiary granule membrane, secretory granule membrane, and cell surface, suggesting their involvement in extracellular signaling and immune cell interactions. Conversely, down-regulated DEMs were associated with nuclear and extracellular matrix components, such as the nucleus, external side of the plasma membrane, and extracellular matrix, indicating their potential role in transcriptional regulation and extracellular interactions ([71]Fig. 2A and B). Molecular function analysis further supported these findings. Up-regulated DEMs were significantly enriched in functions related to protein binding, DNA-binding transcription factor activity, identical protein binding, protein tyrosine/threonine phosphatase activity, MAP kinase tyrosine phosphatase activity, and inhibitory MHC class I receptor activity, emphasizing their role in transcriptional regulation and immune signaling. Meanwhile, down-regulated DEMs were associated with RNA polymerase II cis-regulatory region sequence-specific DNA binding, DNA-binding transcription factor activity, metal ion binding, bitter taste receptor activity, MHC class I protein complex binding, and carbohydrate-binding, suggesting their involvement in transcriptional control, immune recognition, and sensory perception ([72]Fig. 2A and B). A KEGG pathway enrichment analysis was conducted to further explore the functional roles of DEMs. Among the up-regulated DEMs, key enriched pathways included Osteoclast differentiation, NF-kappa B signaling pathway, Lipid and atherosclerosis, Leishmaniasis, Pertussis, TNF signaling pathway, IL-17 signaling pathway, Legionellosis, and C-type lectin receptor signaling pathway. These findings suggest a strong association between up-regulated DEMs and immune response activation, inflammatory signaling, and infectious diseases. Additionally, pathways such as Rheumatoid arthritis, MAPK signaling pathway, Tuberculosis, Hepatitis B, and Chagas disease were also significantly enriched, further indicating their involvement in immune-mediated disorders and pathogenic responses ([73]Fig. 2C). On the other hand, down-regulated DEMs were primarily enriched in pathways such as Herpes simplex virus 1 infection, Antigen processing and presentation, Taste transduction, and Natural killer cell-mediated cytotoxicity. The strong enrichment in Herpes simplex virus 1 infection suggests a potential role of these DEMs in antiviral defense mechanisms, while the involvement in Antigen processing and presentation and Natural killer cell-mediated cytotoxicity indicates a possible impact on immune surveillance and adaptive immunity. Additionally, enrichment in Taste transduction suggests an unexpected role of down-regulated DEMs in sensory processes ([74]Fig. 2C). 3.3. Identification of hub genes and ceRNA network construction To explore the interactions among proteins encoded by DEMs, a PPI network was constructed using the STRING database, comprising 777 nodes and 5561 edges. Subsequently, CytoHubba, a plug-in for Cytoscape, was used to identify hub genes within the PPI network. As depicted in [75]Fig. 3A and B, the Degree method was employed to rank the top 100 hub genes, from which the top 15 were selected for further investigation. These included CD68, CXCL8, FOS, GAPDH, HIF1A, ICAM1, IL1A, IL1B, JUN, MYC, MYD88, NFKBIA, PTGS2, TLR2, and TNF ([76]Table 1). Fig. 3. [77]Fig. 3 [78]Open in a new tab Protein-protein interaction (PPI) network analysis of DEMs in RA. (A) Comprehensive PPI network displaying 100 hub genes associated with RA. Node color intensity corresponds to connectivity degree, with red indicating nodes of highest connectivity. (B) The top 15 hub genes include CD68, CXCL8, FOS, GAPDH, HIF1A, ICAM1, IL1A, IL1B, JUN, MYC, MYD88, NFKBIA, PTGS2, TLR2, and TNF. To examine potential regulatory relationships, we analyzed the interactions between up-regulated DECs (as indicated by logFC values and detailed in [79]Table 2), DEMIs, and the top 15 hub genes using the StarBase database. We then predicted miRNA targets for circRNAs and identified mRNAs targeted by miRNAs ([80]Fig. 4A and B). By integrating these circRNA-miRNA and miRNA-mRNA interactions, we constructed a circRNA-miRNA-mRNA regulatory network, eliminating any unconnected nodes to refine the structure. Finally, we analyzed the ceRNA network to identify regulatory axes associated with hub genes, focusing on negative regulation within circRNA-miRNA or miRNA-mRNA interactions ([81]Fig. 4C). Table 2. List of upregulated circRNAs identified in RA patients through differential expression analysis. The table provides details including circRNA names, CircBase and circBank identifiers, host genes, genomic locations (Hg19 and Hg38), log2 fold changes (logFC), and p-values. Notably, hsa_circ_0092125 (derived from the G6PD gene) and several other circRNAs exhibited significant upregulation, suggesting their potential involvement in RA pathogenesis and their relevance for further ceRNA network analysis. circRNA Circbase ID circBank ID Host_Gene Hg19 Hg38 logFC P.Value hsa_circRNA_100914 hsa_circ_0023903 hsa_circPICALM_026 PICALM chr11:85692171–85742653 chr11:85981128–86031611 1.590504 0.001597 hsa_circRNA_009012 hsa_circ_0009012 hsa_circSTX1A_009 STX1A chr7:73114707-73115261 chr7:73700377-73700931 1.498336 0.000559 hsa_circRNA_074595 hsa_circ_0074595 hsa_circANXA6_032 ANXA6 chr5:150496687-150502572 chr5:151117126-151123011 1.422021 0.001652 hsa_circRNA_008267 hsa_circ_0008267 hsa_circSDHAP2_004 SDHAP2 chr3:195415403-195416309 chr3:195688532-195689438 1.400338 0.000125 hsa_circRNA_074598 hsa_circ_0074598 hsa_circANXA6_023 ANXA6 chr5:150496687-150512748 chr5:151117126-151133187 1.230382 0.002177 hsa_circRNA_103516 hsa_circ_0003692 hsa_circFNDC3B_039 FNDC3B chr3:171969049-172028671 chr3:172251259-172310881 1.227183 0.002449 hsa_circRNA_100912 hsa_circ_0003695 hsa_circPICALM_088 PICALM chr11:85692171–85695016 chr11:85981128–85983973 1.207339 0.002857 hsa_circRNA_105041 hsa_circ_0092125 hsa_circG6PD_014 G6PD chrX:153762552-153762711 chrX:154534337-154534496 1.032154 0.001645 [82]Open in a new tab Fig. 4. [83]Fig. 4 [84]Open in a new tab (A) circRNA–miRNA interaction network illustrating predicted binding relationships between key circRNAs and their target miRNAs. Blue diamonds represent key circRNAs, while green rounded rectangles indicate miRNAs. (B) miRNA–mRNA regulatory network showing target interactions between RA-associated miRNAs and the top 15 hub genes. Red circles correspond to the top 15 hub genes identified in the PPI network, and green rectangles represent miRNAs predicted to target these genes. (C) Alluvial diagram visualizing integrated circRNA–miRNA–mRNA regulatory axes, highlighting key molecular interactions potentially involved in RA pathogenesis. 3.4. Up-regulation of hsa_circ_0092125 and G6PD in RA Through analysis of the constructed regulatory network, hsa_circ_0092125 and its host gene, G6PD, emerged as key targets for further examination. Comparative expression analysis revealed significant upregulation of both genes in the PBMCs of RA patients relative to HCs, with hsa_circ_0092125 (P = 0.0114, fold change = 4.35) and G6PD (P = 0.0048, fold change = 2.23) exhibiting notable increases, as presented in [85]Fig. 5A and B. To determine whether hsa_circ_0092125 and G6PD expression levels in PBMCs could serve as biomarkers for RA disease progression and patient health monitoring, we assessed their correlations with multiple clinical variables. [86]Table 3 outlines the clinical features of the RA patient cohort, while detailed correlation analyses are provided in Supplementary File 1. We specifically examined their relationships with RF and anti-CCP levels; however, the findings did not reach statistical significance, indicating that these gene expressions are not strongly associated with the clinical features analyzed ([87]Table 4). Fig. 5. [88]Fig. 5 [89]Open in a new tab Differential expression of hsa_circ_0092125 and G6PD in RA patients and HC. (A) Box plot showing the relative expression of hsa_circ_0092125, significantly up-regulated in RA patients compared to HC (∗P < 0.05). (B) Box plot displaying the relative expression of RSU1 significantly down-regulated in RA patients compared to HC (∗∗∗P = 0.0004). (C) The ROC curve for hsa_circ_0092125 with an area under the curve (AUC) of 0.7072 indicates its diagnostic potential. (D) The ROC curve for G6PD with an AUC of 0.7824 demonstrates higher diagnostic accuracy. Table 3. Summary of demographic and laboratory characteristics of the study cohort, including blood cell counts, biochemical markers, and inflammatory indicators, from a study group. Values represent averages with corresponding ranges to highlight variability across participants. Variable N Average (range) Age (years) 25 49.4 (24–76) WBC (1000/μL) 21 7.3 (4.08–13.6) RBC (Mil/Cumm) 21 4.89 (3.48–7.35) Hemoglobin (g/dL) 21 11.76 (7–14.1) Hematocrit (%) 21 37.16 (23.2–45.8) MCV (fl) 21 75.60 (32.4–96.) MCH (pg) 21 24.26 (16.71–31.3) MCHC (g/dL) 21 31.41 (30.17–34.5) RDW-CV (%) 21 16.07 (13.4–23) Platelets (1000/μL) 21 283.14 (135–408) MPV (fl) 14 10.93 (8.8–15.9) PDW (%) 14 15.97 (15–17.1) PCT (ml/L) 14 1.75 (0.223–4.18) Neutrophils (%) 21 57.06 (39.9–79.2) lymphocyte (%) 21 32.81 (12.2–50.4) CRP (mg/l) 18 11.51 (0.67–63) ESR (mm/hr) 18 29.05 (3–105) Creatinine (mg/dL) 19 0.85 (0.63–1.19) SGOT (IU/L) 18 24.77 (12–45) SGPT (IU/L) 18 22 (11–38) Ca 11 9.18 (7.9–10.6) Ph 11 3.37 (2.6–3.8) [90]Open in a new tab Table 4. Association analysis of hsa_circ_0092125 and G6PD expression levels with clinical features in RA patients. The table presents average expression levels of both markers stratified by gender, rheumatoid factor (RF) status, and anti-cyclic citrullinated peptide (anti-CCP) status, along with corresponding p-values. hsa_circ_0092125 G6PD Gender Male 15.9 6.004 Female 10.02 6.070 P value 0.1398 0.7538 RF Positive 31.98 6.295 Negative 10.01 5.740 P value 0.4562 0.810 Anti-CCP Positive 12.84 6.470 Negative 19.25 4.445 P value 0.2624 0.3237 [91]Open in a new tab 3.5. ROC curve analyses To assess the diagnostic utility of these candidate biomarkers, ROC curve analysis was performed. hsa_circ_0092125 yielded an AUC of 0.7072 (95 % CI: 0.5612–0.8532, p = 0.0120), reflecting moderate diagnostic accuracy and indicating its potential value as an adjunctive, rather than standalone, diagnostic marker for RA ([92]Fig. 5C). In contrast, G6PD displayed superior diagnostic accuracy, with an AUC of 0.7824 (95 % CI: 0.6457–0.9191, p = 0.0006), supporting its promise as a reliable biomarker for RA diagnosis ([93]Fig. 5D). 4. Discussion RA is a chronic, systemic autoimmune disorder characterized by persistent synovial inflammation, progressive joint destruction, and a range of extra-articular manifestations such as cardiovascular disease and metabolic dysfunction [[94]19]. Notably, ncRNAs are involved in critical cellular processes within fibroblast-like synoviocytes (FLS), T cells, B cells, and macrophages, contributing to synovial hyperplasia, cytokine overproduction, and joint destruction in RA patients [[95]20]. Recent integrative analyses have identified dysregulated ncRNAs not only as potential biomarkers for early diagnosis and disease activity monitoring but also as promising therapeutic targets, opening avenues for precision medicine in RA. The stability, specificity, and accessibility of ncRNAs in peripheral blood and synovial tissues make them particularly attractive for clinical applications, as validated in studies that integrate bioinformatics predictions with experimental data [[96]21]. Through integrative bioinformatics analysis and experimental validation, we demonstrated a clear upregulation of hsa_circ_0092125, with an almost fourfold increase in RA patients compared to HCs. This substantial elevation strongly implicates hsa_circ_0092125 as a key player in RA-associated immune dysregulation. Additionally, the observed increase in G6PD expression, a critical enzyme for redox balance and metabolic flux through the pentose phosphate pathway (PPP), reinforces the notion that metabolic adaptation is a hallmark of immune cell activation in RA [[97]22]. Our correlation analyses further enriched these observations. While the expression levels of hsa_circ_0092125 and G6PD did not show statistically significant associations with conventional clinical markers such as RF and anti-CCP. This may reflect the biological heterogeneity of RA. These biomarkers may represent distinct immunopathological mechanisms independent of autoantibody production. It is also possible that they could provide diagnostic or prognostic value, particularly in seronegative RA, where traditional serological markers are absent. Further studies in larger, clinically stratified cohorts are needed to explore these possibilities. The moderate diagnostic power revealed by the ROC curve analyses, particularly the AUC of 0.78 for G6PD, supports this premise, indicating these molecules could enhance diagnostic accuracy when used alongside established markers. Our findings align with and extend prior insights into the immunometabolic dysregulation in RA. Zhang et al. (2017) demonstrated that G6PD promoted cellular proliferation and survival in renal cancer by enhancing redox regulation and activating the p-STAT3 signaling pathway. This supports the notion that G6PD may contribute to metabolic adaptation and immune cell activation in RA by maintaining oxidative homeostasis [[98]23]. Notably, RA is increasingly recognized as a disease deeply intertwined with the metabolic reprogramming of immune cells. CD4^+ T cells in RA, for instance, preferentially divert glucose flux through the PPP, a shift that critically depends on G6PD activity [[99]22]. This rerouting generates NADPH, fueling anabolic biosynthesis and supporting the maintenance of redox balance — mechanisms essential for the hyperproliferation and persistence of autoreactive immune cells in RA. The heightened expression of G6PD in our study supports the hypothesis that RA immune cells adopt a metabolically aggressive phenotype to sustain chronic inflammation. Interestingly, while G6PD's role in maintaining redox homeostasis is well-established, its involvement in autoimmune inflammation remains less explored. Gheita et al. (2013) previously reported reduced erythrocyte G6PD activity in RA patients, suggesting a paradox [[100]24]. However, this apparent contradiction is reconcilable when considering cell-type specificity. While erythrocytes rely exclusively on G6PD for oxidative protection, leukocytes in inflammatory states may upregulate G6PD as an adaptive mechanism to cope with elevated oxidative stress and heightened metabolic demands. Supporting this, cancer research has revealed that G6PD overexpression sustains tumor cell proliferation and survival under hostile microenvironmental conditions [[101]23]. Drawing an analogy, our data suggest that RA immune cells may exploit G6PD up-regulation as a metabolic advantage, perpetuating inflammation and tissue damage. The circRNA hsa_circ_0092125 also emerges as a pivotal player. Contrasting its down-regulation in oral squamous cell carcinoma, where low levels correlate with poor prognosis, our study revealed robust upregulation in RA patients [[102]9]. This context-dependent expression pattern highlights the nuanced roles of circRNAs, which can act as either oncogenes or tumor suppressors depending on the cellular environment and disease context. In RA, hsa_circ_0092125 likely functions as a ceRNA, sequestering pro-resolving or anti-inflammatory miRNAs and thus promoting the expression of inflammatory genes. Our network analysis further supports this, indicating potential interactions with miRNAs and hub genes involved in cytokine signaling and immune cell recruitment. These mechanistic insights are bolstered by emerging literature emphasizing the role of circRNAs in regulating immune cell behavior. Tang et al. (2023) detailed how circRNAs influence processes such as cell migration, invasion, and immune escape by acting as miRNA sponges and interacting with RNA-binding proteins [[103]25]. Extrapolating to RA, it is plausible that hsa_circ_0092125 facilitates immune cell infiltration into synovial tissue and augments local inflammatory cascades, contributing to joint destruction. Despite these promising insights, several limitations must be acknowledged. Our cohort size, although sufficient for initial findings, limits the statistical power to detect subtle associations with clinical phenotypes. As RA primarily affects synovial tissue, future studies should validate these findings in synovial fluid or tissue samples to assess their local relevance. Moreover, our study is primarily descriptive; functional validation through loss- and gain-of-function experiments is crucial to confirm the pathogenic roles of hsa_circ_0092125 and G6PD. Further functional validation (e.g., luciferase reporter assays, RIP, knockdown/overexpression) is necessary to confirm the predicted interactions. Additionally, longitudinal studies assessing expression dynamics across disease stages and treatment responses could establish their utility as prognostic biomarkers. Looking ahead, several promising avenues warrant further investigation. Functional characterization using both cellular models and animal systems is essential to unravel how hsa_circ_0092125 and G6PD orchestrate immune responses in RA. Therapeutic exploration of the circRNA-miRNA-mRNA axis could open new avenues, as targeting G6PD has shown promise in oncology for curbing proliferation and drug resistance [[104]26]. Such approaches might be repurposed to attenuate hyperactive immune responses in RA. Furthermore, the integration of multi-omics datasets, including proteomics, metabolomics, and single-cell transcriptomics, could unveil novel regulatory circuits and facilitate precision medicine strategies. Finally, given the centrality of metabolic shifts in autoimmunity, advancing the field of immunometabolism offers substantial potential for therapeutic innovation. Targeting metabolic checkpoints may allow selective modulation of pathogenic immune cells while preserving protective immunity. 5. Conclusion Our integrative approach highlights hsa_circ_0092125 and G6PD as promising biomarkers and potential therapeutic targets in RA. These findings not only advance our understanding of RA pathogenesis but also open new avenues for biomarker-driven patient stratification and targeted intervention strategies. As we continue to unravel the complexities of RA, the convergence of immunometabolism and non-coding RNA regulation promises to yield transformative insights and therapeutic opportunities, moving us closer to precision medicine in autoimmune diseases. Author contributions A.G. conceived and designed the study. A.M. performed clinical examinations and patient selection. A.G. acquired clinical samples. A.G. conducted the data acquisition and analysis and drafted and wrote the manuscript. A.G. and H.K. performed the q-PCR verification experiment. M.S. provided expertise and guidance and participated in the study and discussions. A.M. and M.S. supervised, managed the data, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript and accept personal responsibility for their contribution. Availability of data and materials Existing datasets are available in a publicly accessible repository, known as the GEO Database. This study analyzed publicly available datasets [105]GSE169082, [106]GSE124373, and [107]GSE189338. Ethics approval and consent to participate The study involving human participants was reviewed and approved by the Ethics Committee of the Hormozgan University of Medical Sciences of Bandar Abbas City (Approval Number: IR.HUMS.REC.1403.085). The patients/participants provided written informed consent to participate in this study. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments