Bioinformatics Analysis Screened and Identified Key Genes, miRNAs and TFs as Potential Biomarkers for Progression of Rheumatoid Arthritis

Bioinformatics Analysis Screened and Identified Key Genes, miRNAs and TFs as Potential Biomarkers for Progression of Rheumatoid Arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics Analysis Screened and Identified Key Genes, miRNAs and TFs as Potential Biomarkers for Progression of Rheumatoid Arthritis Basavaraj Vastrad, Shivaling Pattanashetti, Veeresh Sadashivanavar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7663291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Elderly patients are prone to rheumatoid arthritis (RA), which may cause reduce quality of life. However, the molecular pathogenesis of RA has not been fully elucidated, and current treatments remain inadequate. Therefore, it is important to explore the molecular mechanism of RA. Next generation sequancing (NGS) data of RA (GSE274996) was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in cases of RA and normal controls, and the Gene Ontology (GO) and and REACTOME pathway enrichment analysis were performed using the DESeq2 R/Bioconductor software package and g:Profiler, respectively. Analysis and visualization of protein-protein interaction networks (PPI) were carried out with IID and Cytoscape. miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drugs associated with hub genes. The diagonstic value of hub genes were assessed by receiver operating characteristic curve (ROC). Total of 958 DEGs were identified between RA and normal control in GSE274996, including 479 up-regulated and 479 down-regulated genes. These genes were enriched in multicellular organismal process, cytosol, enzyme binding, signal transduction, organelle organization, membrane, electron transfer activity and metabolism. A total of hub genes were collected, including MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27, miRNAs were predicted including hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-411-3p and hsa-miR-34c-5p, TFs were predicted including ESR1, FOS, EN1 and FOXL1 and 4 drugs molecules were predicted including Atorvastatin, Mefloquine, Oxprenolol and Acarbose. Evaluation of MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG, TRIM27, hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-411-3p hsa-miR-34c-5p, ESR1, FOS, EN1 and FOXL1 as potential biomarkers can contribute to the subsequent theoretical analysis of potential molecular mechanisms and development of RA, so that the diagnosis of RA might be more accurate, and it is possible to provide therapeutic and prognostic medicine targets. Bioinformatics Computational Biology Drug Discovery, Design, & Development Rheumatology Rheumatoid arthritis Differentially expressed genes Bioinformatics analysis Next generation sequancing analysis biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease in which persistent synovial inflammation, autoantibody production, joint destruction, and systemic complications [D'Orazio et al 2024 ]. RA affects around 31.7 million of the adult population and more common in women [GBD 2021], and often promote angiogenesis [Maruotti et al 2006 ], pannus formation [Bresnihan, 1999 ], fibroblast-like synoviocyte (FLS) proliferation [Mousavi et al 2021 ], and cartilage destruction [Tateiwa et al 2019 ]. The most prevalent clinical manifestations of RA include symmetrical polyarthritis, morning stiffness, swelling & tenderness, reduced range of motion and common deformities. Furthermore, RA might cause complications such as osteoporosis [Baker et al 2022 ], rheumatoid nodules [Highton et al 2007 ], Sjögren's Syndrome [Kim et al 2020 ], infections [Joo et al 2019 ], carpal tunnel syndrome [Smerilli et al 2021 ], cardiovascular diseases [Ferreira et al 2021 ], lung disease [Wang et al 2024 ], lymphoma [Wang et al 2024 ], diabetes mellitus [Inamo et al 2021 ], obesity [Marchand et al 2021 ], hypertension [Al-Ahmari et al 2022], neurological disorders [Maiuolo et al 2021 ], inflammation [del Rincón et al 2015 ], oxidative stress [Zamudio-Cuevas et al 2022 ], stroke [Al-Ewaidat and Naffaa, 2024 ] and autoimmune disease [Simon et al 2017 ]. Therefore, we aimed to further explore the molecular pathogenesis of RA and identify specific molecular targets. The underlying complex molecular mechanisms of RA pose a special challenge to daily clinical practice. NSAIDs like for example aspirin, diclofenac, or ibuprofen [Thakur et al 2018 ] as well as glucocorticoids like prednisolone [Doumen et al 2023 ] can improve the symptoms of RA, but their therapeutic effects are still far from satisfactory. DMARDs like for example methotrexate, hydrochloroquine, and sulfadiazine may be effective in relieving the symptoms of RA [Hoes et al 2010 ], but additional clinical investigation is warranted. Based on the aforementioned, there is a need for the optimization of the current treatment plan for RA. Consequently, it is crucial to fully understand the molecular mechanism and pathogenesis of RA to improve the early diagnosis, treatment, and prognosis of these special patients. Bioinformatics analysis of next generation sequencing (NGS) data is widely used in the investigation of the molecular mechanism of various diseases [Pujar et al. 2022 ; Prashanth et al. 2021 ]. The biomarkers that are being used for the etiological diagnosis of RA include genetic markers and signaling pathways. The genetic markers include HLA-DRB1 [Wysocki et al. 2020 ], HLA-DPB1 [Yang et al. 2021 ], HLA-DQB1 [Wu et al. 2017 ], PTPN22 [Abbasifard et al. 2020 ] and STAT4 [Gao et al. 2020 ], whereas signaling pathways include NF-κB signaling pathway [Liao et al. 2025 ], Jak/STAT signaling pathway [Simon et al. 2021 ], MAPK signaling pathways [Li et al. 2017 ], PI3K/AKT/mTOR signaling pathway [Bobkova et al. 2025 ] and JAK/STAT signal pathway [Malemud et al. 2018] were responsible for occurrence of RA. However, biomarkers can help clinicians in characterizing the severity, and diagnosis and prognosis of the RA in early diagnosis and intervention. Therefore, studying and discovering the precise molecular mechanisms of RA is key for advancement of therapeutic strategies. In this investigation, to identify DEGs between RA and normal control samples, NGS dataset (GSE274996) [Fresneda Alarcon et al. 2025 ] was downloaded from Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ) [Clough and Barrett, 2016 ]. Subsequently, Gene ontology (GO) and REACTOME pathway enrichment analysis of DEGs was undertaken with g:Profiler. Analysis and visualization of PPI network were carried out with IID and Cytoscape. Then, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drug molecules associated with hub genes. To validate that these hub genes can serve as biomarkers of RA, we determine each hub gene’s receiver operating characteristic (ROC) curve area and expression levels in the RA and the normal control samples. This investigation will improve our considerate of the molecular mechanisms of RA and contribute genomic-targeted therapy options for RA. Materials and Methods Next generation sequencing data source GSE274996 [Fresneda Alarcon et al. 2025 ] NGS dataset was downloaded from the GEO database based on a GPL28038, DNBSEQ-G400 (Homo sapiens). The dataset contained 48 blood neutrophils samples, including blood neutrophils of 43 samples of RA patients and blood neutrophils of 5 normal control samples. Identification of DEGs The R bioconductor package DESeq2 [Love et al. 2014 ] was used to analyze the DEGs between RA and normal control samples in the NGS data of GSE274996. The adjusted P-value and [log⁡FC] were calculated. The Benjamini & Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [Solari et al. 2017]. The statistically significant DEGs were identified according to adj P 1.15 for up regulated genes and [log⁡FC] < -0.605 for down regulated genes. The ggplot2 package of R software was used to generate the heat maps, highlighting the major regions of DEGs. Volcano diagram was generated by gplot based on R language. GO and pathway enrichment analyses of DEGs GO ( http://www.geneontology.org ) [Thomas, 2017 ] is a premier bioinformatics program for high-quality functional gene annotation in three conditions: biological process (BP), cellular component (CC), and molecular function (MF). g:Profiler ( http://biit.cs.ut.ee/gprofiler/ ) [Reimand et al. 2007 ] is an online website that provides a comprehensive set of functional annotation tools to understand the biological meaning behind a large list of genes. The REACTOME ( https://reactome.org/ ) [Fabregat et al. 2018 ] is a resource of databases for the clarification of high-level features and effects of biological systems. In the current investigation, the functional enrichment analyses of the statistically significant DEGs, including GO analysis and REACTOME pathway enrichment analysis, were conducted using g:Profiler, with the cut-off criterion of P-value < 0.05 Construction of the PPI network and module analysis The PPI network was constructed using the Human Integrated Protein-Protein Interaction rEference (HiPPIE, http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/index.php ) [Schaefer et al. 2013 ] online database. An open-source bioinformatics software platform, Cytoscape (version 3.10.3) ( http://www.cytoscape.org/ ) [Shannon et al. 2003] is used to visualize molecular interaction networks. The node degree [Luo et al. 2017 ], betweenness [Li et al. 2017 ], stress [Gilbert et al. 2021 ] and closeness [Li et al. 2020 ] algorithms of Network Analyzer in Cytoscape was used to explore hub genes. Using Cytoscape to map the PPI network and using PEWCC [Zaki et al 2013 ] to identify the most important modules in the PPI network. Construction of the miRNA-hub gene regulatory network MiRNAs can play a role in maintaining physiological stability by regulating the expression of hub genes. Mapping of the hub genes to their corresponding miRNAs was performed using miRNet database ( https://www.mirnet.ca/ ) [Fan et al 2018], an online platform for visualization that facilitates the search for miRNA- hub gene interactions in gene regulatory networks. Each hub gene was identified as miRNAs with a degree. Finally, these hub genes and miRNAs were mapped by Cytoscape (version 3.10.3) ( http://www.cytoscape.org/ ) [Shannon et al. 2003]. Construction of the TF-hub gene regulatory network TFs can play a role in maintaining physiological stability by regulating the expression of hub genes. Mapping of the hub genes to their corresponding TFs was performed using NetworkAnalyst database ( https://www.networkanalyst.ca/ ) [Zhou et al 2019 ], an online platform for visualization that facilitates the search for TF- hub gene interactions in gene regulatory networks. Each hub gene was identified as TFs with a degree. Finally, these hub genes and TFs were mapped by Cytoscape (version 3.10.3) ( http://www.cytoscape.org/ ) [Shannon et al. 2003]. Construction of the drug-hub gene interaction network Mapping of the hub genes to their corresponding drug molecules was performed using NetworkAnalyst database ( https://www.networkanalyst.ca/ ) [Zhou et al 2019 ], an online platform for visualization that facilitates the search for drug- hub gene interactions in gene interaction networks. Each hub gene was identified as drug molecules with a degree. Finally, these hub genes and drug molecules were mapped by Cytoscape (version 3.10.3) ( http://www.cytoscape.org/ ) [Shannon et al. 2003]. We used the DrugBank database to retrieve the drugs targeting the hub genes of RA. Types of drug mechanism of action include activation, inhibition and unknown. Receiver operating characteristic curve (ROC) analysis To evaluate the diagnostic value of hub genes more comprehensively, ROC curve was performed. To evaluate the ability of these hub genes to distinguish blood neutrophils of RA samples from blood neutrophils of normal control samples, we extracted the expression profiles of hub genes in normal samples and RA samples. We plotted ROC curves for each hub gene using the “pROC” R package [Robin et al 2011 ]. The receiver operator characteristic curves were plotted and area under curve (AUC) was calculated separately to evaluate the diagnostic value of hub genes. A AUC > 0.9 considered that the model had a acceptable fitting effect. Results Identification of DEGs The NGS dataset GSE274996 was obtained from the public database GEO. A total of 958 DEGs were identified from GSE274996 dataset (479 up regulated and 479 down regulated genes) with a threshold of adj P 1.15 for up regulated genes and [log⁡FC] < -0.605 (Table 1). A total landscape of gene expression in GSE274996 was presented in a volcano plot (Fig. 1). The heat map displayed the DEGs from GSE274996 is shown in Fig. 2 GO and pathway enrichment analyses of DEGs To gain insight into the BP, CC and MF, and of the DEGs products, we performed a gene ontology analysis. The GO analysis extracted from RA patients and normal control subjects revealed that DEGs were significantly enriched in the following BP: multicellular organismal process, biological regulation, organelle organization and positive regulation of cellular process (Table 2, Fig. 3 and Fig. 4). The CC analysis revealed that DEGs were predominantly located in the cytosol, plasma membrane, membrane and intracellular membrane-bounded organelle (Table 2, Fig. 3 and Fig. 4). In the MF category, the DEGs were mainly enriched in enzyme binding, identical protein binding, electron transfer activity and cytoskeletal protein binding (Table, Fig. and Fig.). REACTOME pathway enrichment analysis revealed that the up-regulated genes were significantly enriched in signal transduction, signaling by GPCR, metabolism and metabolism of carbohydrates (Table 3, Fig. 3 and Fig. 4). Construction of the PPI network and module analysis A PPI network of the DEGs was constructed using the online website HiPPIE and software Cytoscape. The PPI network contained 6673 nodes and 14642 edges (Fig. 5). According to high node degree, betweenness, stress and closeness levels, the top hub nodes (5 up regulated and 5 down regulated) were: MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27 (Table 4). A significant module 1 was subsequently constructed with 25 nodes and 59 edges (Fig. 6). Subsequent functional enrichment analysis revealed that the genes in this module were mainly enriched in biological regulation, signal transduction, multicellular organismal process and cytosol (Fig. 7). A significant module 2 was subsequently constructed with 28 nodes and 86 edges (Fig. 8). Subsequent functional enrichment analysis revealed that the genes in this module were mainly enriched in HIV Infection, diseases of signal transduction by growth factor and positive regulation of cellular process (Fig. 9). Construction of the miRNA-hub gene regulatory network MiRNAs essential roles in the regulation of gene expression. The miRNAs and hub gene regulatory networks are built using Cytoscape to predict miRNAs targeting hub genes based on the miRNet database. MiRNA-hub gene regulatory network comprising 2869 nodes [hub gene:415, miRNA: 2454] and 52276 edges (Fig. 10). 407 miRNAs (ex; hsa-miR-5094) collectively targeted HIF1A, 347 miRNAs (ex; hsa-miR-20a-5p) collectively targeted MKI67, 347 miRNAs (ex; hsa-miR-499a-5p) collectively targeted MYC, 320 miRNAs (ex; hsa-miR-3065-5p) collectively targeted MAPK6, 315 miRNAs (ex; hsa-miR-573) collectively targeted TFRC, 293 miRNAs (ex; hsa-miR-411-3p) collectively targeted RAC1, 223 miRNAs (ex; hsa-miR-34c-5p) collectively targeted SQSTM1, 221 miRNAs (ex; hsa-miR-22-3p) collectively targeted SMARCA4, 208 miRNAs (ex; hsa-miR-454-3p) collectively targeted PPP2CB and 179 miRNAs (ex; hsa-miR-30c-1-3p) collectively targeted BSG and listed in Table 5. Construction of the TF-hub gene regulatory network TFs essential roles in the regulation of gene expression. The TFs and hub gene regulatory networks are built using Cytoscape to predict TFs targeting hub genes based on the NetworkAnalyst database. TF-hub gene regulatory network comprising 508 nodes [hub gene: 97, TF: 411] and 3416 edges (Fig. 11). 15 TFs (ex; ESR1) collectively targeted TFRC, 14 TFs (ex; FOS) collectively targeted TRAF1, 11 TFs (ex; NFYA) collectively targeted SNCA, 8 TFs (ex; RELA) collectively targeted HIF1A, 8 TFs (ex; JUN) collectively targeted DPP4, 17 TFs (ex; EN1) collectively targeted SQSTM1, 15 TFs (ex; FOXL1) collectively targeted SMARCA4, 13 TFs (ex; HINFP) collectively targeted LMO2, 9 TFs (ex; NFIC) collectively targeted STUB1 and 9 TFs (ex; USF2) collectively targeted STK11 and listed in Table 5. Construction of the drug-hub gene interaction network Drugs might play essential roles in the regulation of gene function. The drug molecules and hub gene regulatory networks are built using Cytoscape to predict drug molecules targeting hub genes based on the NetworkAnalyst database (Fig. 12 and Fig. 13). 53 drug molecules (ex; Atorvastatin) collectively targeted DPP4, 15 drug molecules (ex; Mefloquine) collectively targeted ADORA2A, 10 drug molecules (ex; Vindesine) collectively targeted TUBB1, 9 drug molecules (ex; Rasagiline) collectively targeted BCL2, 9 drug molecules (ex; 2,6-dicarboxynaphthalene) collectively targeted HBB, 65 drug molecules (ex; Oxprenolol) collectively targeted ADRB2, 13 drug molecules (ex; Acarbose) collectively targeted AMY2A, 9 drug molecules (ex; FAMOXADONE) collectively targeted UQCRC1, 5 drug molecules (ex; Ixabepilone) collectively targeted TUBB3 and 5 drug molecules (ex; Sunitinib) collectively targeted CSF1R and listed in Table 6. Receiver operating characteristic curve (ROC) analysis We explored the predictive ability of hub genes on the occurrence and development of RA through the ROC curve of diagnostic efficacy verification. The higher the AUC value, the better the predictive ability. The results showed that the AUC values of the six core genes were MYC-AUC:0.927, MKI67-AUC:0.915, MAPK6-AUC:0.919, HSPA9-AUC:0.907, ANLN-AUC:0.899, SQSTM1-AUC:0.926, ARRB1-AUC:0.923, RAC1-AUC:0.919, BSG-AUC:0.909 and TRIM27-AUC:0.911 (Fig. 14). Therefore, we hypothesize that MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27 might be biomarkers for RA. Discussion At present, an increasing number of investigations have shown that the systemic auto inflammatory response state and immune cells play an important role in the occurrence and development of RA. Low detection rate in the early stage, and and insufficient effective treatment contribute to the invariably poor prognosis of patients with RA. Therefore, advancement of diagnostic and prognostic biomarkers and therapeutic targets are key to improve diagnosis accuracy and outcome of RA patients in the clinic. After integrated NGS data analysis of RA, a total of 958 DEGs including 479 up regulated and 479 down regulated genes between RA and normal control samples were identified. XIST (X inactive specific transcript) [Yu et al. 2023 ], NR4A3 [Murphy and Crean, 2022 ] and NAV2 [Wang et al. 2021 ] appears to play an important role in RA. XIST (X inactive specific transcript) [Chen et al. 2021 ], GREM2 [Liang et al. 2025 ] and WNT3 [Xu et al. 2020 ] have been proposed as biomarkers for osteoporosis. XIST (X inactive specific transcript) [Mao et al. 2024 ] has been proposed as novel biomarker for Sjögren's Syndrome. Regulation of SLC4A1 [Zhu et al. 2022 ] levels might be a novel treatment option against infections. XIST (X inactive specific transcript) [Haybar et al. 2024 ], NR4A3 [Peng et al. 2024 ], EDA (ectodysplasin A) [Toprak et al. 2023 ], EGR3 [Li et al. 2025 ], GREM2 [Sanders et al. 2016 ] and NAV2 [Rong et al. 2025 ] have been reported to be associated with cardiovascular diseases. NR4A3 [Dou et al. 2025 ], EGR3 [Zhang et al. 2025 ], GREM2 [Huan et al. 2021 ] and SLC4A1 [Zhu et al. 2022 ] have been identified as potential biomarkers for lung diseases. XIST (X inactive specific transcript) [Liu et al. 2020 ] is a known prognostic biomarker for lymphoma. XIST (X inactive specific transcript) [Sohrabifar et al. 2022 ], NR4A3 [Peng et al. 2024 ], EDA (ectodysplasin A) [Bayliss et al. 2021 ] and GREM2 [Ni et al. 2025 ] have been proved to participate in the progression of diabetes mellitus. A previous study indicates that XIST (X inactive specific transcript) [Wu et al. 2022 ], EDA (ectodysplasin A) [Awazawa et al. 2017 ], GREM2 [Liu et al. 2022 ] and TWIST2 [Yang et al. 2018 ] takes part in the progression of obesity. Previous studies have also revealed that XIST (X inactive specific transcript) [Carman et al. 2024 ], NR4A3 [Ma et al. 2021 ], NAV2 [McNeill et al. 2010 ] and WNT3 [Yin] et al. 2020 ] are an important biomarker for hypertension. The aberrant expression of XIST (X inactive specific transcript) [Chanda and Mukhopadhyay, 2020 ], NR4A3 [He et al. 2024 ], EGR3 [Marballi et al. 2022 ], GREM2 [Frazer et al. 2024 ], HBD (hemoglobin subunit delta) [Derakhshani et al. 2021 ], CHD5 [Parenti et al. 2021 ], MAST1 [Sloboda et al. 2023 ] and CLEC3B [Kolicheski et al. 2020 ] have been revealed to play an important role in the development of neurological disorders. XIST (X inactive specific transcript) [Wang et al. 2025 ], NR4A3 [He et al. 2024 ], EGR3 [Kwon et al. 2021 ], NAV2 [Wang et al. 2023 ] and TWIST2 [Ding et al. 2022 ] might be a favorable prognostic biomarkers and a therapeutic targets in inflammation. XIST (X inactive specific transcript) [Wen et al. 2020 ], NR4A3 [Zhu et al. 2025 ] and TWIST2 [Song et al. 2021 ] have been found to be altered expressed in oxidative stress. XIST (X inactive specific transcript) [Andrade, 2024 ], NR4A3 [Li et al. 2020 ], EGR3 [Morita et al. 2016 ] and HBD (hemoglobin subunit delta) [Derakhshani et al. 2021 ] have been demonstrated to be altered expressed in autoimmune diseases. On these results, we suggest that significant DEGs might play an essential role in the onset and progression of RA. In order to investigate the biological meaning behind these DEGs, we performed GO and REACTOME pathway enrichment analysis. Signaling pathways include signal transduction [Zhu et al. 2023 ], signaling by GPCR [Shu et al. 2017 ], signaling by receptor tyrosine kinases [Okamoto and Kobayashi, 2011 ], signaling by interleukins [Sharma et al. 2017 ], metabolism [Chimenti et al. 2015 ], metabolism of carbohydrates [Dzisiow, 1958 ], HIV infection [Liang et al. 2017 ], Neddylation [Sendo et al. 2024 ] and antigen processing: ubiquitination& proteasome degradation [Ruscitti et al. 2024 ] were linked with RA. CD177 [Kaundal et al. 2021 ], MMP19 [Sedlacek et al. 1998 ], FASLG (Fas ligand) [Calmon-Hamaty et al. 2015 ], CTH (cystathionine gamma-lyase) [Wu et al. 2019 ], ICOS (inducible T cell costimulator) [Wang et al. 2018 ], GPR15 [Fernández-Ruiz et al. 2022 ], CCL2 [Moadab et al. 2021 ], DPP4 [Han et al. 2021 ], MMP8 [Schmalz et al. 2019 ], SDC4 [Cai et al. 2020 ], CD28 [García-Chagollán et al. 2020 ], IL15 [Kurowska et al. 2020 ], CCR7 [Van Raemdonck et al. 2020 ], CD83 [Kristensen et al. 2017 ], CCR4 [Tanaka et al. 2024 ], NR4A1 [Murphy and Crean, 2022 ], LEF1 [Zhang et al. 2023 ], ITK (IL2 inducible T cell kinase) [Chen et al. 2023 ], SLC7A5 [Xu et al. 2020 ], THBS1 [Chen et al. 2025 ], MAL (mal, T cell differentiation protein) [Sheedy et al. 2008 ], CENPF (centromere protein F) [Dong et al. 2022 ], ADORA2A [Soukup et al. 2017 ], CD226 [Gibson et al. 2021 ], IL21R [Chen et al. 2025 ], POU2AF1 [Romo-García et al. 2019 ], ARID5B [Tagawa et al. 2024 ], AREG (amphiregulin) [Zhang et al. 2025 ], IL7R [Meyer et al. 2022 ], PNP (purine nucleoside phosphorylase) [Arduini et al. 2019 ], SFRP1 [Huang et al. 2024 ], FOXP3 [Hashemi et al. 2018 ], CDKN1A [Gang et al. 2018 ], MFGE8 [Liu et al. 2025 ], IL10RA [Yang et al. 2017 ], STAT4 [Gao et al. 2020 ], SUCNR1 [Chen et al. 2025 ], AMIGO2 [Miao et al. 2024 ], MMD (monocyte to macrophage differentiation associated) [Mahmoudi et al. 2022 ], FCRL1 [Yang et al. 2021 ], CYP51A1 [Mosavi et al. 2024 ], HIF1A [Chen et al. 2025 ], PRDX4 [Aihaiti et al. 2022 ], TESPA1 [Yao et al. 2015 ], BCL2 [Kielbassa et al. 2023 ], E2F1 [Dai et al. 2025 ], PTX3 [Targońska-Stępniak and Drelich-Zbroja], 2024 ], KL (klotho) [Ji et al. 2025 ], CD22 [Bednar et al. 2019 ], PTGS2 [Abbasi et al. 2025 ], SAV1 [Guo et al. 2024 ], TNFAIP3 [Tang et al. 2023 ], EDN1 [Panoulas et al. 2008 ], IGF2BP2 [Xu et al. 2025 ], EFNB2 [Hu et al. 2015 ], TOB1 [Chen et al. 2020 ], RECK (reversion inducing cysteine rich protein with kazal motifs) [van Lent et al. 2005 ], DUSP5 [Moon et al. 2014 ], C9ORF72 [Fredi et al. 2019 ], LOXL1 [Hu et al. 2024 ], TRIB1 [Wu et al. 2022 ], OLFM4 [Ren et al. 2021 ], KLF12 [García-Bermúdez et al. 2011 ], PFKFB3 [PFKFB3 et al. 2022], FHL1 [Friese et al. 2003 ], KLF9 [Huang et al. 2022 ], BIRC3 [Meng et al. 2024 ], RCAN3 [Park et al. 2017 ], OPTN (optineurin) [Lee et al. 2020 ], EXOSC4 [Yao et al 2025 ], TRAF1 [Tang et al 2025 ], RASGRP3 [Golinski et al 2015 ], FCRL2 [Khanzadeh et al 2016 ], PIAS2 [Xiao et al 2016], CCL20 [Migita et al 2009 ], CXCL2 [Wang et al 2021 ], CXCL5 [Tejera-Segura et al 2019 ], CCL28 [Chen et al 2015 ], CCL3L1 [Ben Kilani et al 2016 ], CCRL2 [Galligan et al 2004 ], CSF1R [Hu et al 2019 ], PYCARD (PYD and CARD domain containing) [Geng et al 2024 ], FSCN1 [Chen et al 2022 ], TFEB (transcription factor EB) [Xu and Pan, 2021 ], PDLIM2 [Wang et al 2022 ], GHRL (ghrelin and obestatin prepropeptide) [Ozgen et al 2011 ], ARRB1 [Li et al 2013 ], PIN1 [Ma et al 2025 ], MAP2K2 [Krawczyk et al 2023 ], TMEM187 [Khalifa et al 2017 ], ZNF804A [Fattah et al 2022 ], LTB (lymphotoxin beta) [Sun et al 2024 ], SEMA4B [Martínez-Ramos et al 2025 ], ITGA5 [Huang et al 2020 ], GSDMD (gasdermin D) [Ren et al 2023 ], PLEKHO1 [He et al 2019 ], TRPA1 [Lowin et al 2023 ], F12 [McLaren et al 2022], MPG (N-methylpurine DNA glycosylase) [Huang et al 2015 ], SLC19A1 [Imamura et al 2016 ], CSK (C-terminal Src kinase) [Remuzgo-Martínez et al 2017 ], UNC13D [Schulert et al 2018 ], USP6 [Eisenberg et al 2022 ], NAPRT (nicotinate phosphoribosyltransferase) [Lei et al 2022 ], PSMB9 [Li et al 2022 ], PSMB5 [Wu et al 2024 ] and HYAL1 [Imundo et al 2021] have been reported to be involved in the progression of RA. The altered expression of FASLG (Fas ligand) [Jones, 2015 ], CCL2 [Fatehi et al. 2017 ], DPP4 [Huang et al. 2024 ], SNCA (synuclein alpha) [Figueroa et al. 2020 ], CCR4 [Araujo-Pires et al. 2015 ], NR4A1 [Yang et al. 2024 ], LEF1 [Zhang et al. 2025 ], ITGB3 [Yu et al. 2022 ], THBS1 [Li et al. 2025 ], IL1A [Zupan et al. 2012 ], CSF1R [Wei et al. 2006 ], TUBB3 [Nakamura et al. 2018 ], ADRB2 [Krasnova et al. 2025 ], TFEB (transcription factor EB) [Wang et al. 2023 ], ARRB1 [Boutin et al. 2020 ], TRIM27 [Kim et al. 2025 ], SLC4A2 [Wu et al. 2008 ], PIN1 [Islam et al. 2017 ], DVL1 [Lin et al. 2025 ] and SIRT7 [Fukuda et al. 2018 ] plays a positive role in progression of osteoporosis. Previous studies have shown that FASLG (Fas ligand) [Tsuzaka et al. 2007 ], ICOS (inducible T cell costimulator) [Li et al. 2022 ], CCL2 [Chivasso et al. 2024 ], DPP4 [Mascolo et al. 2016 ], MMP8 [Määttä et al. 2006 ], SNCA (synuclein alpha) [Alvarez-Castelao et al. 2014 ], CD28 [López-Villalobos et al. 2019 ], IL15 [Sisto et al. 2017 ], CCR7 [Pan et al. 2025 ], CCR4 [Shimizu et al. 2004 ], ARRB1 [Hu et al. 2011 ], PICK1 [Ji et al. 2024 ] and PIN1 [Ishii et al. 2023 ] plays an important role in the development of Sjögren's syndrome. CD177 [Agidigbi et al. 2025 ], FASLG (Fas ligand) [Dockrell et al. 2003], FOSL1 [Cai et al. 2017 ], CTH (cystathionine gamma-lyase) [Jin et al. 2025 ], HBB (hemoglobin subunit beta) [Li et al. 2013 ], ICOS (inducible T cell costimulator) [Mani et al. 2024 ], GPR15 [Hayn et al. 2021 ], CCL2 [Howe et al. 2017 ], GJB2 [Li et al. 2013 ], GJB6 [Ross et al. 2007 ], CSF1R [Combes et al. 2021 ], PPP2CB [Li et al. 2025 ], MAP1S [Shi et al. 2016 ], TUBB3 [Shi et al. 2022 ], PYCARD (PYD and CARD domain containing) [Uusi-Mäkelä et al. 2025 ], ADRB2 [Sharma et al. 2025 ], FSCN1 [Chen et al. 2019 ], TFEB (transcription factor EB) [Jassey et al. 2024 ], EHMT2 [Shin et al. 2019 ] and RBM14 [Wang et al. 2024 ] were significantly regulated in patients with infections. ALAS2 [He et al. 2024 ], FASLG (Fas ligand) [Szymanowski et al. 2014 ], FOSL1 [Zhao et al. 2025 ], CTH (cystathionine gamma-lyase) [Kolluru et al. 2022 ], CCL2 [Gholamalizadeh et al. 2024 ], MEOX1 [Schumacher et al. 2021 ], COL19A1 [Xu et al. 2025 ], EGR2 [Bo et al. 2022 ], AXIN2 [Zheng et al. 2024 ], DPP4 [Chen et al. 2022 ], BBC3 [Lee et al. 2025 ], COX20 [Zhang et al. 2022 ], FHL3 [Guo et al. 2025 ], PPP2CB [An et al. 2025 ], ADRB2 [Castaño-Amores et al. 2023 ], FSCN1 [Zhang et al. 2024 ], TFEB (transcription factor EB) [Yan et al. 2024 ], EHMT2 [Xiao et al. 2022 ], FBXW5 [Hui et al. 2021 ] and SCAP (SREBF chaperone) [Chen et al. 2011 ] play an important role in the pathogenesis of cardiovascular diseases. CD177 [Li et al. 2024 ], MMP19 [Fan et al. 2023 ], FASLG (Fas ligand) [Kopiński et al. 2011 ], CALD1 [Wu et al. 2014], ICOS (inducible T cell costimulator) [Sakthivel et al. 2016 ], CCL2 [Matsuda et al. 2021 ], MEOX1 [Zhao et al. 2024 ], DPP4 [Yen et al. 2025 ], MMP8 [Hu et al. 2020 ], SDC4 [Zhu et al. 2023 ] BBC3 [Liu et al. 2017 ], CSF1R [Oldham et al. 2023 ], ADRB2 [Wan et al. 2023 ], TFEB (transcription factor EB) [Liu et al. 2019 ], BBS1 [Viehl et al. 2023 ], ARRB1 [Huang et al. 2024 ], TRIM27 [Zhu et al. 2025 ], PICK1 [Qian et al. 2025 ], PIN1 [Shen et al. 2012 ] and SIRT7 [Chen et al. 2022 ] were correlates positively with the incidence of lung diseases. FASLG (Fas ligand) [Villa-Morales et al. 2007 ], ICOS (inducible T cell costimulator) [Chavez et al. 2023 ], CCL2 [Guilloton et al. 2012 ], AXIN2 [Fu et al. 2020 ], SNCA (synuclein alpha) [Chen et al. 2024 ], CD28 [Sakamoto et al. 2022 ], IL15 [Gordon et al. 2024 ], CCR7 [Chen et al. 2025 ], CD83 [Aladily et al. 2019 ], MS4A1 [Jiang et al. 2021 ], CSF1R [Gao et al. 2022 ], ENKD1 [Song et al. 2023 ], TUBB3 [Zamò et al. 2014 ], PYCARD (PYD and CARD domain containing) [Su et al. 2022 ], MCM5 [Liu et al. 2025 ], EHMT2 [Wang et al. 2021 ], FUZ (fuzzy planar cell polarity protein) [Chen et al. 2018 ], PDLIM2 [Wurster et al. 2017 ], HOOK2 [Wang et al. 2019 ] and GHRL (ghrelin and obestatin prepropeptide) [Kasprzak and Adamek, 2022 ] might play an important role in the pathophysiology of lymphoma. FASLG (Fas ligand) [Yolcu et al. 2017 ], FOSL1 [Zhou et al. 2021 ], HBB (hemoglobin subunit beta) [Liu et al. 2022 ], CALD1 [Śnit et al. 2017 ], ICOS (inducible T cell costimulator) [Savastio et al. 2020 ], CCL2 [Mir et al. 2024 ], ACVR1C [Emdin et al. 2019 ], DPP4 [Barchetta et al. 2019 ], MMP8 [de Morais et al. 2018 ], SDC4 [Feng et al. 2025 ], ADRB2 [Kim et al. 2002 ], TFEB (transcription factor EB) [Song et al. 2019 ], HOOK2 [Rodríguez-Rodero et al. 2017 ], GHRL (ghrelin and obestatin prepropeptide) [Cowan et al. 2016 ], POC1A [Li et al. 2023 ], TRIM27 [Zaman et al. 2013 ], PICK1 [Andersen et al. 2022 ], PIN1 [Chellappan et al. 2025 ], CAPN10 [Smail and Mohamad, 2023 ] and DVL1 [Cheng et al. 2024 ] have been confirmed to be a potential target in diabetes mellitus. MMP19 [Pendás et al. 2004 ], FASLG (Fas ligand) [Blüher et al. 2014 ], CTH (cystathionine gamma-lyase) [Lu et al. 2024 ], CCL2 [Wu and Ma, 2024 ], STX1A [Romeo et al. 2008 ], DPP4 [Guo et al. 2024 ], MMP8 [Lauhio et al. 2016 ], SAMSN1 [Zhou et al. 2025 ], CD28 [Berillo et al. 2024 ], IL15 [Pérez-López et al. 2018 ], ADRB2 [Tan and Mitra, 2020 ], TFEB (transcription factor EB) [Kim et al. 2021 ], SCAP (SREBF chaperone) [Zheng et al. 2021 ], HOOK2 [Rodríguez-Rodero et al. 2017 ], BBS1 [Mykytyn et al. 2002 ], GHRL (ghrelin and obestatin prepropeptide) [Guo et al. 2007 ], RGS14 [Vatner et al. 2025 ], PICK1 [Fadahunsi et al. 2024 ], PIN1 [Bianchi and Manco, 2022 ] and CAPN10 [Cheverud et al. 2010 ] expression might be regarded as an indicator of susceptibility to obesity. FASLG (Fas ligand) [Karthikeyan et al. 2012 ], CTH (cystathionine gamma-lyase) [Katsouda et al. 2023 ], ICOS (inducible T cell costimulator) [Bellan et al. 2022 ], CCL2 [Kashyap et al. 2018 ], AXIN2 [Nie et al. 2019 ], DPP4 [Suzuki et al. 2024 ], PER1 [Min et al. 2024 ], MMP8 [Deng et al. 2025 ], SDC4 [Lipphardt et al. 2020 ], CD28 [Berillo et al. 2024 ], ADRB2 [Maamor et al. 2024 ], TFEB (transcription factor EB) [Chen et al. 2025 ], SCAP (SREBF chaperone) [Yang et al. 2017 ], ARRB1 [Sun et al. 2018 ], PIN1 [Yuan et al. 2023 ], CAPN10 [Zhou et al. 2010 ], SIRT7 [Zhou et al. 2025 ], DAPK3 [Xue et al. 2022 ], SMARCA4 [Ma et al. 2019 ] and NDUFC2 [Gallo et al. 2023 ] genes are a potential biomarkers for the detection and prognosis of hypertension. FASLG (Fas ligand) [Ethell and Buhler, 2003 ], FOSL1 [Ma et al. 2022 ], ICOS (inducible T cell costimulator) [Bjursten et al. 2021 ], DNAAF1 [Miao et al. 2016 ], GPR15 [Ammitzbøll et al. 2019 ], CCL2 [Xiromerisiou et al. 2022 ], STX1A [Luppe et al. 2023 ], EGR2 [Funalot et al. 2012 ], AXIN2 [Fancy et al. 2011 ], DPP4 [Al-Badri et al. 2018 ], MAD1L1 [Sokolov et al. 2023 ], CLN6 [Talbot et al. 2020 ], CSF1R [Hu et al. 2021 ], NDUFS7 [Zhang et al. 2025 ], TUBB3 [Puri et al. 2023 ], PYCARD (PYD and CARD domain containing) [Liu et al. 2023 ], TFEB (transcription factor EB) [Yang et al. 2023 ], EHMT2 [Carvalho et al. 2025 ] and FUZ (fuzzy planar cell polarity protein) [Chen et al. 2018 ] have been demonstrated to accelerate neurological disorders. CD177 [Yang et al. 2019,] FASLG (Fas ligand) [Sayani et al. 2004 ], FOSL1 [Ma et al. 2022 ], LAMB3 [Liu et al. 2024 ], CTH (cystathionine gamma-lyase) [Jin et al. 2025 ], GPR15 [Jegodzinski et al. 2020 ], CCL2 [Pozzi et al. 2024], GJB2 [Zhang et al. 2023 ], EGR2 [Symonds et al. 2023], DPP4 [Hellenthal et al. 2025 ], BBC3 [Zhang et al. 2016 ], B9D2 [Wang et al. 2025 ], MAP7 [Wang et al. 2022 ], CLN6 [Kay and Palmer, 2013 ], ZMYND10 [Cho et al. 2018 ,] CSF1R [Hume et al. 2025 ], NME3 [Flentie et al. 2018 ], ENKD1 [Zhang et al. 2025 ], MAP1S [Shi et al. 2023 ] and KRT18 [Xiao et al. 2025 ] expression might be regarded as an indicator of susceptibility to inflammation. Altered expression of ALAS2 [He et al. 2024 ], FASLG (Fas ligand) [Soni et al. 2018 ], CCL2 [Zheng et al. 2018], EGR2 [Huang et al. 2022 ], DPP4 [Lee et al. 2024 ], PER1 [Zhu et al. 2024 ], MMP8 [da Silva-Neto et al. 2022 ], SNCA (synuclein alpha) [Sola et al. 2020 ], CD28 [Liu et al. 2019 ], IL15 [Chen et al. 2019 ], BBC3 [Liu et al. 2017 ], COX20 [Keerthiraju et al. 2019 ], FHL3 [Guo et al. 2025 ], CLN6 [Kanninen et al. 2013 ], CSF1R [Hu et al. 2020 ], NDUFS7 [Zhang et al. 2025 ], NME3 [Chen et al. 2020 ], MAP1S [Yue et al. 2017 ], TUBB3 [Mariani et al. 2011 ] and ADRB2 [Wan et al. 2011] are associated with prognosis in patients with oxidative stress. FASLG (Fas ligand) [Rossin et al. 2019 ], FOSL1 [Li et al. 2024 ], ICOS (inducible T cell costimulator) [Ban et al. 2003 ], GPR15 [Zhao et al. 2022 ], CCL2 [Rafei and Galipeau, 2010 ], MEOX1 [Jiao et al. 2025 ], EGR2 [Dai et al. 2022 ], DPP4 [Huang et al. 2022 ], PER1 [Zhu et al. 2025 ], MMP8 [Nygårdas and Hinkkanen, 2002 ], FHL3 [Guo et al. 2025 ], MAP7 [Navarro-Barriuso et al. 2019 ], CLN6 [Poppens et al. 2019 ], CSF1R [Nissen et al. 2018 ], PPP2CB [Li et al. 2025 ], ADRB2 [Chu et al. 2009 ], DUSP23 [Balada et al. 2017 ], FSCN1 [Chen et al. 2022 ], TFEB (transcription factor EB) [Xia et al. 2022 ] and EHMT2 [Pollin et al. 2024 ] have been reported to be altered expressed in autoimmune diseases. GO and REACTOME pathway enrichment analysis provides novel biological indicators, in addition to molecular mechanisms and targets for predicting clinical prognosis of RA patients, which requires further clinical studys by multi-omics anlysis validation. PPI and module analysis suggest that hub genes detected in the present study might be involvement in RA progression. MYC is a key controler of cell growth and metabolism. Its deregulation plays a central role in autoimmune diseases [Mountz et al. 1985 ]. MYC might be considered as a novel biomarker for RA. MKI67 gene encodes Ki-67, a nuclear proliferation protein critical for cell cycle progression and chromosome organization. Its deregulation plays a central role in RA [Pessler et al. 2008 ]. MAPK6 is an atypical MAP kinase with essential roles in cell growth, survival, motility, and differentiation [Tan et al. 2017 ]. MAPK6 might influence RA by regulating proliferation or migration of T cells and macrophages. MAPK6 might emerging as a promising novel therapeutic target for RA. HSPA9 is a mitochondrial chaperone critical for protein folding, mitochondrial function, stress responses, and cell survival [Han et al. 2024 ]. Its abnormal regulation is might be associated with autoimmune disease. HSPA9 might be novel therapeutic target for RA. ANLN is an actin-binding scaffold protein essential for cell division and actin remodeling [Li et al. 2020 ]. ANLN might be novel diagnostic biomarker and a potential therapeutic target.for RA. RSL1D1 nucleolar protein that controls ribosome biogenesis, cell cycle, apoptosis, and senescence [Jiang et al. 2022 ]. RSL1D1 might emerging as novel therapeutic target and prognostic biomarker for RA. DDX21 is a nucleolar RNA helicase essential for ribosome biogenesis, RNA metabolism, and innate immunity [Xiao et al. 2024 ]. DDX21 might act as an autoantigen in systemic autoimmune diseases. DDX21 might emerging as novel prognostic biomarker and therapeutic target for RA. SQSTM1 is a scaffold protein essential for autophagy, oxidative stress defense, and inflammatory signaling [Hou et al. 2025 ]. SQSTM1 might novel biomarker and therapeutic target at the crossroads of protein degradation and cell signaling in RA. ARRB1 is a scaffold protein regulating GPCR desensitization, endocytosis, and downstream signaling [Cahill et al. 2017 ]. Its dysregulation contributes to RA [Li et al 2013 ]. RAC1 is a master controler of cytoskeletal dynamics, proliferation, migration, ROS production, and gene expression [Ma et al. 2023 ]. Its dysregulation might be implicated in RA. Its dysregulation is implicated in autoimmune disease. RAC1 might represents novel biomarker and a promising drug target for RA. BSG is a transmembrane glycoprotein associated in matrix remodeling, metabolism, immune regulation, and pathogen entry [Fu et al. 2023 ]. BSG might be novel therapeutic target and biomarker for RA. TRIM27 is an E3 ubiquitin ligase and transcriptional regulator involved in cell survival, apoptosis, immune response, and DNA repair [Zaman et al. 2013 ]. Dysregulation might contributes to autoimmune disease. TRIM27 might be an novel biomarker and therapeutic target for RA. POLR2E encodes a shared subunit of RNA polymerases I, II, and III, key for mRNA transcription and cellular viability. Dysregulation of POLR2E might contributes to autoimmune disease. POLR2E might represents a novel biomarker of transcriptional activity and a potential therapeutic target in RA. POLR2I encodes a zinc finger subunit of RNA polymerase II, important for transcription fidelity, DNA repair, and mRNA synthesis. Dysregulation might linked to autoimmune disease. POLR2I might serves as novel biomarker and a therapeutic target in RA. POLR2F encodes a shared subunit of RNA polymerases I, II, and III, key for mRNA, rRNA, and tRNA transcription. Its dysfunction might be linked to autoimmune disease. POLR2F might be novel biomarker and therapeutic target in RA. LMO2 is a LIM-domain transcriptional cofactor essential for hematopoiesis and angiogenesis [Ganta and Annex, 2017 ]. Its altered expression might contributing indirectly to autoimmune disease. Clinically, LMO2 might serves as novel biomarker and a therapeutic target in RA. This investigation approach identified the possible hub genes that were highly correlated with the PPI network to identify the novel target genes might be involved in the pathogenesis of RA and listed in Suplimentary Table S1. The role of miRNAs and TFs in RA that we have identified still needs to be further explored. In addition, there are still limited studies related to miRNAs and TFs in the RA. It is evident that miRNA-hub gene regulatory network, TF-hub gene regulatory network play a key part in the development of RA. hsa-miR-34c-5p [Jiang et al. 2021 ], ESR1 [Pawlik et al. 2012 ], FOS [Huber et al. 2020 ], RELA [Yang et al. 2021 ], JUN [Lai et al. 2020 ], NFIC [Jia et al. 2023 ] and USF2 [Hu et al. 2020 ] could serve as a potential therapeutic targets for RA. This investigation suggests that hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-499a-5p, hsa-miR-3065-5p, hsa-miR-573, hsa-miR-411-3p, hsa-miR-22-3p, hsa-miR-454-3p, hsa-miR-30c-1-3p, NFYA, EN1, FOXL1 and HINFP might be a novel therapeutic targets for RA, and the related molecular mechanism is worthy of further investigations. While our study provides significant insights into the action of drug on expression of hub genes. Our findings suggest that drugs- Atorvastatin, Mefloquine, Vindesine, Rasagiline, 2,6-dicarboxynaphthalene, Oxprenolol, Acarbose, Famoxadone, Ixabepilone and Sunitinib concurrently target to hub genes include DPP4, ADORA2A, TUBB1, BCL2, HBB, ADRB2, AMY2A, UQCRC1, TUBB3 and CSF1R, potentially controlling the development of RA. Conclusions Bioinformatics analysis of NGS data is a useful technique to explore the molecular mechanism and pathogenesis of RA. There were numerous genes that were differentially expressed in the RA and normal control groups. These hub genes, miRNA and TFs migt play important roles in the onset and development of RA and serve as therapeutic targets. Abbreviations RA Rheumatoid Arthritis DEGs Differentially expressed genes NGS Next generation sequencing GEO Gene expression omnibus GO Gene ontology PPI Protein-protein interaction miRNA Micro ribonuclic acid TF Transcription factor ROC Receiver operating characteristic curve MYC MYC proto-oncogene, bHLH transcription factor MKI67 Marker of proliferation Ki-67 MAPK6 Mitogen-activated protein kinase 6 HSPA9 Heat shock protein family A (Hsp70) member 9 ANLN Anillin, actin binding protein SQSTM1 Sequestosome 1 ARRB1 Arrestin beta 1 RAC1 Rac family small GTPase 1 BSG Basigin (Ok blood group) TRIM27 Tripartite motif containing 27 Declarations Acknowledgement I thanks very much to Wright HL, University of Liverpool, Institute of Life Course and Medical Sciences, Liverpool, United Kingdom, the author who deposited their NGS dataset GSE274996, into the public GEO database. Funding The authors received no financial support for the research Conflict of interest The authors declare that they have no conflict of interest. Ethical approval Not applicable Consent to participate Not applicable Written Consent for publication Not applicable Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE274996) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE274996] Code availability Not applicable Author Contributions B. V. - Writing original draft, and review and editing S.P. - Formal analysis and validation V.S. - Resources and investigation K,P. - Investigation and validation C. V. - Software and investigation References Abbasi H, Sharif M, John P, Bhatti A (2025) Integrated Network Pharmacology and Molecular Modeling Approach for Potential PTGS2 Inhibitors against Rheumatoid Arthritis. 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Clin Chim Acta 411(15–16):1126–1131. 10.1016/j.cca.2010.04.012 Zhu F, Ji Y, You Q, Dong Q, Tang Y, Zhang Y (2025) Asiaticoside Mitigates Chronic Obstructive Pulmonary Disease by Modulating TRIM27 Stability and Activating PGC-1α/Nrf2 Signaling. Appl Biochem Biotechnol Published online July 16. 10.1007/s12010-025-05288-z Zhu H, Cai C, Yu Y, Zhou Y, Yang S, Hu Y, Zhu Y, Zhou J, Zhao J, Ma H et al (2024) Quercetin-Loaded Bioglass Injectable Hydrogel Promotes m6A Alteration of Per1 to Alleviate Oxidative Stress for Periodontal Bone Defects. Adv Sci (Weinh) 11(29):e2403412. 10.1002/advs.202403412 Zhu M, Ding Q, Lin Z, Fu R, Zhang F, Li Z, Zhang M, Zhu Y (2023) New Targets and Strategies for Rheumatoid Arthritis: From Signal Transduction to Epigenetic Aspect. Biomolecules 13(5):766. 10.3390/biom13050766 Zhu M, Ma X, Huang J, Lu FG, Chen Y, Hu J, Cheng L, Zhang B, Liu W, Li L (2022) Extracellular vesicle-derived miR-1249-5p regulates influenza A virus-induced acute lung injury in RAW246.7 cells through targeting SLC4A1. Microbes Infect 24(8):104998. 10.1016/j.micinf.2022.104998 Zhu M, Ma X, Huang J, Lu FG, Chen Y, Hu J, Cheng L, Zhang B, Liu W, Li L (2022) Extracellular vesicle-derived miR-1249-5p regulates influenza A virus-induced acute lung injury in RAW246.7 cells through targeting SLC4A1. Microbes Infect 24(8):104998. 10.1016/j.micinf.2022.104998 Zhu W, Chen G, Xiao Z, Wang M, Li Z, Shi Y, Luo X, Li Z, Huang H, Chen X et al (2025) Circadian Rhythm Disruption Exacerbates Autoimmune Uveitis: The Essential Role of PER1 in Treg Cell Metabolic Support for Stability and Function. Adv Sci (Weinh) 12(10):e2400004. 10.1002/advs.202400004 Zhu Z, Dou X, Chen Q, Lu Y (2025) Knockdown of Nr4a3 mitigates acute pancreatitis-induced injury by modulating Btg2 to reduce oxidative stress, mitochondrial damage, and apoptosis. Int Immunopharmacol. 10.1016/j.intimp.2025.115269 Zhu Z, Ling X, Zhou H, Xie J (2023) Syndecan-4 is the key proteoglycan involved in mediating sepsis-associated lung injury. Heliyon 9(8):e18600 Published 2023 Jul 23. 10.1016/j.heliyon.2023.e18600 Zupan J, Pristovšek N, Mencej-Bedrač S, Komadina R, Preželj J, Marc J (2012) Interleukin-1α gene variants influence bone mineral density and the risk of osteoporotic hip fractures in elderly Slovenian people. Clin Chem Lab Med 50(8):1379–1385. 10.1515/cclm-2011-0589 Tables Tables are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx SupplementaryTableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7663291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517954834,"identity":"ee95b56a-59b1-45da-8e1f-e9bdeb24bc90","order_by":0,"name":"Basavaraj Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag 582101, Karnataka, India.","correspondingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":517954835,"identity":"930969d6-be21-4798-b1dd-fab73021d6fe","order_by":1,"name":"Shivaling Pattanashetti","email":"","orcid":"https://orcid.org/0009-0003-9246-1604","institution":"Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag 582101, Karnataka, India.","correspondingAuthor":false,"prefix":"","firstName":"Shivaling","middleName":"","lastName":"Pattanashetti","suffix":""},{"id":517954836,"identity":"00dbbd97-8fc8-4212-a72b-f3f3afb9ec21","order_by":2,"name":"Veeresh Sadashivanavar","email":"","orcid":"https://orcid.org/0009-0002-1054-8996","institution":"Department of Pharmacology, Manipal College of Pharamaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal 576104, Karanataka, India.","correspondingAuthor":false,"prefix":"","firstName":"Veeresh","middleName":"","lastName":"Sadashivanavar","suffix":""},{"id":517954837,"identity":"0ebc38bc-e3a3-46a2-b1ee-a51b7d5232c4","order_by":3,"name":"KSR Pai","email":"","orcid":"https://orcid.org/0000-0002-2017-9533","institution":"Department of Pharmacology, Manipal College of Pharamaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal 576104, Karanataka, India","correspondingAuthor":false,"prefix":"","firstName":"KSR","middleName":"","lastName":"Pai","suffix":""},{"id":517954838,"identity":"1c5bc62e-b5a0-4560-bb89-23e34bd10e34","order_by":4,"name":"Chanabasayya Vastrad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCRQemw2QYGw8QKQWZpCWNJCWBpK0HAYz8Wrhn9387HFhG0Nif//5Yx9+lJ23W9t+GGhLjU00TkvuHDM3ngnUMuNGMvPMnnO3k7edSQRqOZaW24BDi4FEgpk0bxuDMcMNZmYG3rbbyWYHgFoYGw7j0ZL+DaxF/vxhZsa/beeSzc4/JKQlB2yLnMGBZGZm3rYDdmY3CNgicSOnTJrnnISc4Y1kY2aZc8kJZjeAtiTg8Qv/jPRt0jxlNjxy5w8+ZnxTZmdvdj794YMPNTY4tcAsg7MSwSoT8CtHBfakKB4Fo2AUjIKRAQDSZlsK1y3XFwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3615-4450","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India.","correspondingAuthor":true,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""}],"badges":[],"createdAt":"2025-09-20 07:44:52","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7663291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7663291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92013084,"identity":"1cefa7cb-c218-4ef4-bd43-192861f8c88f","added_by":"auto","created_at":"2025-09-23 15:59:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143525,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/bc2d6d94d943134b5c481cda.docx"},{"id":92012020,"identity":"970d5d1c-dd97-4b54-a990-1693d08f5058","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7663291.json","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/bae9f9007d9eeec4a1f550c4.json"},{"id":92012022,"identity":"041947e7-b847-4d53-a8bb-f85af95f7906","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":939276,"visible":true,"origin":"","legend":"","description":"","filename":"rs76632910enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/17719077df10991cf465b04b.xml"},{"id":92012032,"identity":"0e880422-8b73-47cf-ae14-7719a00bf20e","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":927385,"visible":true,"origin":"","legend":"","description":"","filename":"rs76632910structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/819f50585d5c2218f7136553.xml"},{"id":92013667,"identity":"18623c2b-af1a-48c2-9a59-2ff7b0e4a706","added_by":"auto","created_at":"2025-09-23 16:07:04","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":952450,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/023b9a2f4113ddced534d6c6.html"},{"id":92011299,"identity":"0d964223-b9d3-4685-95ed-6af0968c7f0e","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32145,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/bea11f8d9e9e7412f8f23ab5.jpg"},{"id":92012019,"identity":"98bd4bbc-ea92-490d-951d-7647f16eb157","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37651,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A5 = Normal control samples; B1 – B 43 = RA samples)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/bb8e3a9a086f0b404991eea5.jpg"},{"id":92013083,"identity":"31786196-c105-4c76-9e42-67d308c27bf0","added_by":"auto","created_at":"2025-09-23 15:59:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174277,"visible":true,"origin":"","legend":"\u003cp\u003eGO and REACTOME pathway enrichment analysis for up regulated genes. p \u0026lt; 0.05. Abbreviations: BP, biological process; CC, cell component; MF, molecular function. GO, Gene Ontology; REAC, REACTOME. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/ba1dadedfcdeb9eb470acfa1.jpg"},{"id":92011311,"identity":"7ea72054-5cdf-413c-9f03-0dbfd6915d8a","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188958,"visible":true,"origin":"","legend":"\u003cp\u003eGO and REACTOME pathway enrichment analysis for down regulated genes. p \u0026lt; 0.05. Abbreviations: BP, biological process; CC, cell component; MF, molecular function. GO, Gene Ontology; REAC, REACTOME. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/6479124857eb47f4e07b465d.jpg"},{"id":92013089,"identity":"228d9945-73e3-446d-be59-bbe63e94dc43","added_by":"auto","created_at":"2025-09-23 15:59:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":497208,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs. Up regulated genes are marked in parrot green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/74b06aacff3c4d48266fab5d.jpg"},{"id":92011307,"identity":"f5bc5aff-1899-4f67-91a7-d64a55fa066f","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51130,"visible":true,"origin":"","legend":"\u003cp\u003eModules 1 was isolated form PPI of up regulated genes. Module 1 has 25nodes and 59 edges for up regulated genes. Up regulated genes are marked in green\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/4377ca502f02ecd60f8ed37b.jpg"},{"id":92013666,"identity":"9e2254f5-60d9-4a58-b908-4e2d4da68ede","added_by":"auto","created_at":"2025-09-23 16:07:04","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":129318,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 1. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/aa81be1c228dbef59e293cf1.jpg"},{"id":92012027,"identity":"03c050dd-6309-4625-befa-4d07a098a2fe","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":57105,"visible":true,"origin":"","legend":"\u003cp\u003eModule 2 was isolated form PPI of up regulated genes. Module 2 has 28 nodes and 86 edges for down regulated genes. Down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/65aac8ef6b5cbe7f45c4b791.jpg"},{"id":92011313,"identity":"8d92619b-21b5-4daa-8e4c-3c3c84e5b247","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119860,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 2. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/9db977a2777dfaef5df64d42.jpg"},{"id":92012030,"identity":"be3ecb1e-6994-45cc-8b54-b56a3b200490","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":527894,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - miRNA regulatory network. The light purple color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/4e59b0244baa844829ebed13.jpg"},{"id":92013668,"identity":"b6411975-cafb-405a-a146-a11f5897d9fb","added_by":"auto","created_at":"2025-09-23 16:07:04","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":303585,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - TF regulatory network. The ash color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/9b775d601670fcd310b0acdd.jpg"},{"id":92013086,"identity":"0d1f1553-1c33-4389-b108-9963fe2805e8","added_by":"auto","created_at":"2025-09-23 15:59:04","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":65070,"visible":true,"origin":"","legend":"\u003cp\u003eDrug-hub gene interaction network. The ash color rectangle nodes represent the drug molecule; up regulated genes are marked in green\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/2c6c3831e409772f0986f434.jpg"},{"id":92011304,"identity":"01ffcbc9-9fa8-4fde-8ea3-ea71f6cb6584","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":96733,"visible":true,"origin":"","legend":"\u003cp\u003eDrug-hub gene interaction network. The ash color rectangle nodes represent the drug molecule; down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/5290862f7f5415cf65322645.jpg"},{"id":92011318,"identity":"069f6d28-37f5-4d44-842a-29300c8c12e0","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":46310,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) MYC B) MKI67 C) MAPK6 D) HSPA9 E) ANLN F) SQSTM1G) ARRB1 H) RAC1 I) BSG J) TRIM27\u003c/p\u003e","description":"","filename":"Picture14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/22a1d618ed08e169d7831198.jpg"},{"id":92014503,"identity":"2abe649c-dbfe-46e8-8977-e07941d1eaca","added_by":"auto","created_at":"2025-09-23 16:15:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3820702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/787d1dce-2ddc-4626-b68b-2e2f59795b13.pdf"},{"id":92012024,"identity":"f22bca67-de01-4441-a27f-83827b1f6c97","added_by":"auto","created_at":"2025-09-23 15:51:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":176132,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/f04cfd43acf645de5f23513c.docx"},{"id":92011301,"identity":"bb1c60fc-56f9-4e68-a798-b11b6407e897","added_by":"auto","created_at":"2025-09-23 15:43:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12535,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7663291/v1/9d7ea9d6ade7e5fa4b2d2779.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBioinformatics Analysis Screened and Identified Key Genes, miRNAs and TFs as Potential Biomarkers for Progression of Rheumatoid Arthritis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic, systemic autoimmune disease in which persistent synovial inflammation, autoantibody production, joint destruction, and systemic complications [D'Orazio et al \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. RA affects around 31.7\u0026nbsp;million of the adult population and more common in women [GBD 2021], and often promote angiogenesis [Maruotti et al \u003cspan citationid=\"CR275\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], pannus formation [Bresnihan, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e], fibroblast-like synoviocyte (FLS) proliferation [Mousavi et al \u003cspan citationid=\"CR292\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], and cartilage destruction [Tateiwa et al \u003cspan citationid=\"CR385\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. The most prevalent clinical manifestations of RA include symmetrical polyarthritis, morning stiffness, swelling \u0026amp; tenderness, reduced range of motion and common deformities. Furthermore, RA might cause complications such as osteoporosis [Baker et al \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], rheumatoid nodules [Highton et al \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], Sj\u0026ouml;gren's Syndrome [Kim et al \u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], infections [Joo et al \u003cspan citationid=\"CR189\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], carpal tunnel syndrome [Smerilli et al \u003cspan citationid=\"CR360\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], cardiovascular diseases [Ferreira et al \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], lung disease [Wang et al \u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], lymphoma [Wang et al \u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], diabetes mellitus [Inamo et al \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], obesity [Marchand et al \u003cspan citationid=\"CR272\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], hypertension [Al-Ahmari et al 2022], neurological disorders [Maiuolo et al \u003cspan citationid=\"CR267\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], inflammation [del Rinc\u0026oacute;n et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], oxidative stress [Zamudio-Cuevas et al \u003cspan citationid=\"CR460\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], stroke [Al-Ewaidat and Naffaa, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and autoimmune disease [Simon et al \u003cspan citationid=\"CR356\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. Therefore, we aimed to further explore the molecular pathogenesis of RA and identify specific molecular targets.\u003c/p\u003e\u003cp\u003eThe underlying complex molecular mechanisms of RA pose a special challenge to daily clinical practice. NSAIDs like for example aspirin, diclofenac, or ibuprofen [Thakur et al \u003cspan citationid=\"CR387\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] as well as glucocorticoids like prednisolone [Doumen et al \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] can improve the symptoms of RA, but their therapeutic effects are still far from satisfactory. DMARDs like for example methotrexate, hydrochloroquine, and sulfadiazine may be effective in relieving the symptoms of RA [Hoes et al \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], but additional clinical investigation is warranted. Based on the aforementioned, there is a need for the optimization of the current treatment plan for RA. Consequently, it is crucial to fully understand the molecular mechanism and pathogenesis of RA to improve the early diagnosis, treatment, and prognosis of these special patients.\u003c/p\u003e\u003cp\u003eBioinformatics analysis of next generation sequencing (NGS) data is widely used in the investigation of the molecular mechanism of various diseases [Pujar et al. \u003cspan citationid=\"CR318\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Prashanth et al. \u003cspan citationid=\"CR317\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. The biomarkers that are being used for the etiological diagnosis of RA include genetic markers and signaling pathways. The genetic markers include HLA-DRB1 [Wysocki et al. \u003cspan citationid=\"CR423\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], HLA-DPB1 [Yang et al. \u003cspan citationid=\"CR440\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], HLA-DQB1 [Wu et al. \u003cspan citationid=\"CR416\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], PTPN22 [Abbasifard et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and STAT4 [Gao et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], whereas signaling pathways include NF-κB signaling pathway [Liao et al. \u003cspan citationid=\"CR238\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], Jak/STAT signaling pathway [Simon et al. \u003cspan citationid=\"CR355\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MAPK signaling pathways [Li et al. \u003cspan citationid=\"CR231\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], PI3K/AKT/mTOR signaling pathway [Bobkova et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and JAK/STAT signal pathway [Malemud et al. 2018] were responsible for occurrence of RA. However, biomarkers can help clinicians in characterizing the severity, and diagnosis and prognosis of the RA in early diagnosis and intervention. Therefore, studying and discovering the precise molecular mechanisms of RA is key for advancement of therapeutic strategies.\u003c/p\u003e\u003cp\u003eIn this investigation, to identify DEGs between RA and normal control samples, NGS dataset (GSE274996) [Fresneda Alarcon et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] was downloaded from Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Clough and Barrett, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e]. Subsequently, Gene ontology (GO) and REACTOME pathway enrichment analysis of DEGs was undertaken with g:Profiler. Analysis and visualization of PPI network were carried out with IID and Cytoscape. Then, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drug molecules associated with hub genes. To validate that these hub genes can serve as biomarkers of RA, we determine each hub gene\u0026rsquo;s receiver operating characteristic (ROC) curve area and expression levels in the RA and the normal control samples. This investigation will improve our considerate of the molecular mechanisms of RA and contribute genomic-targeted therapy options for RA.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eNext generation sequencing data source\u003c/h2\u003e\u003cp\u003eGSE274996 [Fresneda Alarcon et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] NGS dataset was downloaded from the GEO database based on a GPL28038, DNBSEQ-G400 (Homo sapiens). The dataset contained 48 blood neutrophils samples, including blood neutrophils of 43 samples of RA patients and blood neutrophils of 5 normal control samples.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of DEGs\u003c/h3\u003e\n\u003cp\u003eThe R bioconductor package DESeq2 [Love et al. \u003cspan citationid=\"CR253\" class=\"CitationRef\"\u003e2014\u003c/span\u003e] was used to analyze the DEGs between RA and normal control samples in the NGS data of GSE274996. The adjusted P-value and [log⁡FC] were calculated. The Benjamini \u0026amp; Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [Solari et al. 2017]. The statistically significant DEGs were identified according to adj P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and [log⁡FC]\u0026thinsp;\u0026gt;\u0026thinsp;1.15 for up regulated genes and [log⁡FC] \u0026lt; -0.605 for down regulated genes. The ggplot2 package of R software was used to generate the heat maps, highlighting the major regions of DEGs. Volcano diagram was generated by gplot based on R language.\u003c/p\u003e\n\u003ch3\u003eGO and pathway enrichment analyses of DEGs\u003c/h3\u003e\n\u003cp\u003eGO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Thomas, \u003cspan citationid=\"CR388\" class=\"CitationRef\"\u003e2017\u003c/span\u003e] is a premier bioinformatics program for high-quality functional gene annotation in three conditions: biological process (BP), cellular component (CC), and molecular function (MF). g:Profiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biit.cs.ut.ee/gprofiler/\u003c/span\u003e\u003cspan address=\"http://biit.cs.ut.ee/gprofiler/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Reimand et al. \u003cspan citationid=\"CR322\" class=\"CitationRef\"\u003e2007\u003c/span\u003e] is an online website that provides a comprehensive set of functional annotation tools to understand the biological meaning behind a large list of genes. The REACTOME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Fabregat et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] is a resource of databases for the clarification of high-level features and effects of biological systems. In the current investigation, the functional enrichment analyses of the statistically significant DEGs, including GO analysis and REACTOME pathway enrichment analysis, were conducted using g:Profiler, with the cut-off criterion of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003ch3\u003eConstruction of the PPI network and module analysis\u003c/h3\u003e\n\u003cp\u003eThe PPI network was constructed using the Human Integrated Protein-Protein Interaction rEference (HiPPIE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/index.php\u003c/span\u003e\u003cspan address=\"http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Schaefer et al. \u003cspan citationid=\"CR339\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] online database. An open-source bioinformatics software platform, Cytoscape (version 3.10.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Shannon et al. 2003] is used to visualize molecular interaction networks. The node degree [Luo et al. \u003cspan citationid=\"CR256\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], betweenness [Li et al. \u003cspan citationid=\"CR231\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], stress [Gilbert et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and closeness [Li et al. \u003cspan citationid=\"CR225\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] algorithms of Network Analyzer in Cytoscape was used to explore hub genes. Using Cytoscape to map the PPI network and using PEWCC [Zaki et al \u003cspan citationid=\"CR456\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] to identify the most important modules in the PPI network.\u003c/p\u003e\n\u003ch3\u003eConstruction of the miRNA-hub gene regulatory network\u003c/h3\u003e\n\u003cp\u003eMiRNAs can play a role in maintaining physiological stability by regulating the expression of hub genes. Mapping of the hub genes to their corresponding miRNAs was performed using miRNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Fan et al 2018], an online platform for visualization that facilitates the search for miRNA- hub gene interactions in gene regulatory networks. Each hub gene was identified as miRNAs with a degree. Finally, these hub genes and miRNAs were mapped by Cytoscape (version 3.10.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Shannon et al. 2003].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the TF-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eTFs can play a role in maintaining physiological stability by regulating the expression of hub genes. Mapping of the hub genes to their corresponding TFs was performed using NetworkAnalyst database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Zhou et al \u003cspan citationid=\"CR479\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], an online platform for visualization that facilitates the search for TF- hub gene interactions in gene regulatory networks. Each hub gene was identified as TFs with a degree. Finally, these hub genes and TFs were mapped by Cytoscape (version 3.10.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Shannon et al. 2003].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConstruction of the drug-hub gene interaction network\u003c/h3\u003e\n\u003cp\u003eMapping of the hub genes to their corresponding drug molecules was performed using NetworkAnalyst database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Zhou et al \u003cspan citationid=\"CR479\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], an online platform for visualization that facilitates the search for drug- hub gene interactions in gene interaction networks. Each hub gene was identified as drug molecules with a degree. Finally, these hub genes and drug molecules were mapped by Cytoscape (version 3.10.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Shannon et al. 2003]. We used the DrugBank database to retrieve the drugs targeting the hub genes of RA. Types of drug mechanism of action include activation, inhibition and unknown.\u003c/p\u003e\n\u003ch3\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the diagnostic value of hub genes more comprehensively, ROC curve was performed. To evaluate the ability of these hub genes to distinguish blood neutrophils of RA samples from blood neutrophils of normal control samples, we extracted the expression profiles of hub genes in normal samples and RA samples. We plotted ROC curves for each hub gene using the \u0026ldquo;pROC\u0026rdquo; R package [Robin et al \u003cspan citationid=\"CR326\" class=\"CitationRef\"\u003e2011\u003c/span\u003e]. The receiver operator characteristic curves were plotted and area under curve (AUC) was calculated separately to evaluate the diagnostic value of hub genes. A AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9 considered that the model had a acceptable fitting effect.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of DEGs\u003c/h2\u003e\u003cp\u003eThe NGS dataset GSE274996 was obtained from the public database GEO. A total of 958 DEGs were identified from GSE274996 dataset (479 up regulated and 479 down regulated genes) with a threshold of adj P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and [log⁡FC]\u0026thinsp;\u0026gt;\u0026thinsp;1.15 for up regulated genes and [log⁡FC] \u0026lt; -0.605 (Table\u0026nbsp;1). A total landscape of gene expression in GSE274996 was presented in a volcano plot (Fig.\u0026nbsp;1). The heat map displayed the DEGs from GSE274996 is shown in Fig.\u0026nbsp;2\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eGO and pathway enrichment analyses of DEGs\u003c/h2\u003e\u003cp\u003eTo gain insight into the BP, CC and MF, and of the DEGs products, we performed a gene ontology analysis. The GO analysis extracted from RA patients and normal control subjects revealed that DEGs were significantly enriched in the following BP: multicellular organismal process, biological regulation, organelle organization and positive regulation of cellular process (Table\u0026nbsp;2, Fig.\u0026nbsp;3 and Fig.\u0026nbsp;4). The CC analysis revealed that DEGs were predominantly located in the cytosol, plasma membrane, membrane and intracellular membrane-bounded organelle (Table\u0026nbsp;2, Fig.\u0026nbsp;3 and Fig.\u0026nbsp;4). In the MF category, the DEGs were mainly enriched in enzyme binding, identical protein binding, electron transfer activity and cytoskeletal protein binding (Table, Fig. and Fig.). REACTOME pathway enrichment analysis revealed that the up-regulated genes were significantly enriched in signal transduction, signaling by GPCR, metabolism and metabolism of carbohydrates (Table\u0026nbsp;3, Fig.\u0026nbsp;3 and Fig.\u0026nbsp;4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the PPI network and module analysis\u003c/h2\u003e\u003cp\u003eA PPI network of the DEGs was constructed using the online website HiPPIE and software Cytoscape. The PPI network contained 6673 nodes and 14642 edges (Fig.\u0026nbsp;5). According to high node degree, betweenness, stress and closeness levels, the top hub nodes (5 up regulated and 5 down regulated) were: MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27 (Table\u0026nbsp;4). A significant module 1 was subsequently constructed with 25 nodes and 59 edges (Fig.\u0026nbsp;6). Subsequent functional enrichment analysis revealed that the genes in this module were mainly enriched in biological regulation, signal transduction, multicellular organismal process and cytosol (Fig.\u0026nbsp;7). A significant module 2 was subsequently constructed with 28 nodes and 86 edges (Fig.\u0026nbsp;8). Subsequent functional enrichment analysis revealed that the genes in this module were mainly enriched in HIV Infection, diseases of signal transduction by growth factor and positive regulation of cellular process (Fig.\u0026nbsp;9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the miRNA-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eMiRNAs essential roles in the regulation of gene expression. The miRNAs and hub gene regulatory networks are built using Cytoscape to predict miRNAs targeting hub genes based on the miRNet database. MiRNA-hub gene regulatory network comprising 2869 nodes [hub gene:415, miRNA: 2454] and 52276 edges (Fig.\u0026nbsp;10). 407 miRNAs (ex; hsa-miR-5094) collectively targeted HIF1A, 347 miRNAs (ex; hsa-miR-20a-5p) collectively targeted MKI67, 347 miRNAs (ex; hsa-miR-499a-5p) collectively targeted MYC, 320 miRNAs (ex; hsa-miR-3065-5p) collectively targeted MAPK6, 315 miRNAs (ex; hsa-miR-573) collectively targeted TFRC, 293 miRNAs (ex; hsa-miR-411-3p) collectively targeted RAC1, 223 miRNAs (ex; hsa-miR-34c-5p) collectively targeted SQSTM1, 221 miRNAs (ex; hsa-miR-22-3p) collectively targeted SMARCA4, 208 miRNAs (ex; hsa-miR-454-3p) collectively targeted PPP2CB and 179 miRNAs (ex; hsa-miR-30c-1-3p) collectively targeted BSG and listed in Table\u0026nbsp;5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the TF-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eTFs essential roles in the regulation of gene expression. The TFs and hub gene regulatory networks are built using Cytoscape to predict TFs targeting hub genes based on the NetworkAnalyst database. TF-hub gene regulatory network comprising 508 nodes [hub gene: 97, TF: 411] and 3416 edges (Fig.\u0026nbsp;11). 15 TFs (ex; ESR1) collectively targeted TFRC, 14 TFs (ex; FOS) collectively targeted TRAF1, 11 TFs (ex; NFYA) collectively targeted SNCA, 8 TFs (ex; RELA) collectively targeted HIF1A, 8 TFs (ex; JUN) collectively targeted DPP4, 17 TFs (ex; EN1) collectively targeted SQSTM1, 15 TFs (ex; FOXL1) collectively targeted SMARCA4, 13 TFs (ex; HINFP) collectively targeted LMO2, 9 TFs (ex; NFIC) collectively targeted STUB1 and 9 TFs (ex; USF2) collectively targeted STK11 and listed in Table\u0026nbsp;5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the drug-hub gene interaction network\u003c/h2\u003e\u003cp\u003eDrugs might play essential roles in the regulation of gene function. The drug molecules and hub gene regulatory networks are built using Cytoscape to predict drug molecules targeting hub genes based on the NetworkAnalyst database (Fig.\u0026nbsp;12 and Fig.\u0026nbsp;13). 53 drug molecules (ex; Atorvastatin) collectively targeted DPP4, 15 drug molecules (ex; Mefloquine) collectively targeted ADORA2A, 10 drug molecules (ex; Vindesine) collectively targeted TUBB1, 9 drug molecules (ex; Rasagiline) collectively targeted BCL2, 9 drug molecules (ex; 2,6-dicarboxynaphthalene) collectively targeted HBB, 65 drug molecules (ex; Oxprenolol) collectively targeted ADRB2, 13 drug molecules (ex; Acarbose) collectively targeted AMY2A, 9 drug molecules (ex; FAMOXADONE) collectively targeted UQCRC1, 5 drug molecules (ex; Ixabepilone) collectively targeted TUBB3 and 5 drug molecules (ex; Sunitinib) collectively targeted CSF1R and listed in Table\u0026nbsp;6.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h2\u003e\u003cp\u003eWe explored the predictive ability of hub genes on the occurrence and development of RA through the ROC curve of diagnostic efficacy verification. The higher the AUC value, the better the predictive ability. The results showed that the AUC values of the six core genes were MYC-AUC:0.927, MKI67-AUC:0.915, MAPK6-AUC:0.919, HSPA9-AUC:0.907, ANLN-AUC:0.899, SQSTM1-AUC:0.926, ARRB1-AUC:0.923, RAC1-AUC:0.919, BSG-AUC:0.909 and TRIM27-AUC:0.911 (Fig.\u0026nbsp;14). Therefore, we hypothesize that MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27 might be biomarkers for RA.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, an increasing number of investigations have shown that the systemic auto inflammatory response state and immune cells play an important role in the occurrence and development of RA. Low detection rate in the early stage, and and insufficient effective treatment contribute to the invariably poor prognosis of patients with RA. Therefore, advancement of diagnostic and prognostic biomarkers and therapeutic targets are key to improve diagnosis accuracy and outcome of RA patients in the clinic. After integrated NGS data analysis of RA, a total of 958 DEGs including 479 up regulated and 479 down regulated genes between RA and normal control samples were identified. XIST (X inactive specific transcript) [Yu et al. \u003cspan citationid=\"CR452\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], NR4A3 [Murphy and Crean, \u003cspan citationid=\"CR293\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and NAV2 [Wang et al. \u003cspan citationid=\"CR407\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] appears to play an important role in RA. XIST (X inactive specific transcript) [Chen et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], GREM2 [Liang et al. \u003cspan citationid=\"CR237\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and WNT3 [Xu et al. \u003cspan citationid=\"CR431\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] have been proposed as biomarkers for osteoporosis. XIST (X inactive specific transcript) [Mao et al. \u003cspan citationid=\"CR270\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] has been proposed as novel biomarker for Sj\u0026ouml;gren's Syndrome. Regulation of SLC4A1 [Zhu et al. \u003cspan citationid=\"CR486\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] levels might be a novel treatment option against infections. XIST (X inactive specific transcript) [Haybar et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NR4A3 [Peng et al. \u003cspan citationid=\"CR310\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EDA (ectodysplasin A) [Toprak et al. \u003cspan citationid=\"CR389\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EGR3 [Li et al. \u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], GREM2 [Sanders et al. \u003cspan citationid=\"CR336\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] and NAV2 [Rong et al. \u003cspan citationid=\"CR330\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] have been reported to be associated with cardiovascular diseases. NR4A3 [Dou et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EGR3 [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], GREM2 [Huan et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and SLC4A1 [Zhu et al. \u003cspan citationid=\"CR486\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] have been identified as potential biomarkers for lung diseases. XIST (X inactive specific transcript) [Liu et al. \u003cspan citationid=\"CR246\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] is a known prognostic biomarker for lymphoma. XIST (X inactive specific transcript) [Sohrabifar et al. \u003cspan citationid=\"CR362\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], NR4A3 [Peng et al. \u003cspan citationid=\"CR310\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EDA (ectodysplasin A) [Bayliss et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and GREM2 [Ni et al. \u003cspan citationid=\"CR297\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] have been proved to participate in the progression of diabetes mellitus. A previous study indicates that XIST (X inactive specific transcript) [Wu et al. \u003cspan citationid=\"CR414\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], EDA (ectodysplasin A) [Awazawa et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], GREM2 [Liu et al. \u003cspan citationid=\"CR249\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and TWIST2 [Yang et al. \u003cspan citationid=\"CR441\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] takes part in the progression of obesity. Previous studies have also revealed that XIST (X inactive specific transcript) [Carman et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NR4A3 [Ma et al. \u003cspan citationid=\"CR262\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NAV2 [McNeill et al. \u003cspan citationid=\"CR279\" class=\"CitationRef\"\u003e2010\u003c/span\u003e] and WNT3 [Yin] et al. \u003cspan citationid=\"CR450\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] are an important biomarker for hypertension. The aberrant expression of XIST (X inactive specific transcript) [Chanda and Mukhopadhyay, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NR4A3 [He et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EGR3 [Marballi et al. \u003cspan citationid=\"CR234\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], GREM2 [Frazer et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], HBD (hemoglobin subunit delta) [Derakhshani et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CHD5 [Parenti et al. \u003cspan citationid=\"CR306\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MAST1 [Sloboda et al. \u003cspan citationid=\"CR358\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and CLEC3B [Kolicheski et al. \u003cspan citationid=\"CR205\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] have been revealed to play an important role in the development of neurological disorders. XIST (X inactive specific transcript) [Wang et al. \u003cspan citationid=\"CR399\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], NR4A3 [He et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EGR3 [Kwon et al. \u003cspan citationid=\"CR212\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NAV2 [Wang et al. \u003cspan citationid=\"CR409\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and TWIST2 [Ding et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] might be a favorable prognostic biomarkers and a therapeutic targets in inflammation. XIST (X inactive specific transcript) [Wen et al. \u003cspan citationid=\"CR413\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NR4A3 [Zhu et al. \u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and TWIST2 [Song et al. \u003cspan citationid=\"CR368\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] have been found to be altered expressed in oxidative stress. XIST (X inactive specific transcript) [Andrade, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NR4A3 [Li et al. \u003cspan citationid=\"CR225\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], EGR3 [Morita et al. \u003cspan citationid=\"CR289\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] and HBD (hemoglobin subunit delta) [Derakhshani et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] have been demonstrated to be altered expressed in autoimmune diseases. On these results, we suggest that significant DEGs might play an essential role in the onset and progression of RA.\u003c/p\u003e\u003cp\u003eIn order to investigate the biological meaning behind these DEGs, we performed GO and REACTOME pathway enrichment analysis. Signaling pathways include signal transduction [Zhu et al. \u003cspan citationid=\"CR485\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], signaling by GPCR [Shu et al. \u003cspan citationid=\"CR354\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], signaling by receptor tyrosine kinases [Okamoto and Kobayashi, \u003cspan citationid=\"CR301\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], signaling by interleukins [Sharma et al. \u003cspan citationid=\"CR346\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], metabolism [Chimenti et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], metabolism of carbohydrates [Dzisiow, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1958\u003c/span\u003e], HIV infection [Liang et al. \u003cspan citationid=\"CR236\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], Neddylation [Sendo et al. \u003cspan citationid=\"CR344\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and antigen processing: ubiquitination\u0026amp; proteasome degradation [Ruscitti et al. \u003cspan citationid=\"CR333\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] were linked with RA. CD177 [Kaundal et al. \u003cspan citationid=\"CR195\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MMP19 [Sedlacek et al. \u003cspan citationid=\"CR343\" class=\"CitationRef\"\u003e1998\u003c/span\u003e], FASLG (Fas ligand) [Calmon-Hamaty et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Wu et al. \u003cspan citationid=\"CR419\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], ICOS (inducible T cell costimulator) [Wang et al. \u003cspan citationid=\"CR401\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], GPR15 [Fern\u0026aacute;ndez-Ruiz et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CCL2 [Moadab et al. \u003cspan citationid=\"CR287\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], DPP4 [Han et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MMP8 [Schmalz et al. \u003cspan citationid=\"CR340\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], SDC4 [Cai et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CD28 [Garc\u0026iacute;a-Chagoll\u0026aacute;n et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], IL15 [Kurowska et al. \u003cspan citationid=\"CR211\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CCR7 [Van Raemdonck et al. \u003cspan citationid=\"CR393\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CD83 [Kristensen et al. \u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], CCR4 [Tanaka et al. \u003cspan citationid=\"CR381\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NR4A1 [Murphy and Crean, \u003cspan citationid=\"CR293\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], LEF1 [Zhang et al. \u003cspan citationid=\"CR468\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ITK (IL2 inducible T cell kinase) [Chen et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], SLC7A5 [Xu et al. \u003cspan citationid=\"CR431\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], THBS1 [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAL (mal, T cell differentiation protein) [Sheedy et al. \u003cspan citationid=\"CR347\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], CENPF (centromere protein F) [Dong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ADORA2A [Soukup et al. \u003cspan citationid=\"CR370\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], CD226 [Gibson et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], IL21R [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], POU2AF1 [Romo-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR329\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], ARID5B [Tagawa et al. \u003cspan citationid=\"CR377\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], AREG (amphiregulin) [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], IL7R [Meyer et al. \u003cspan citationid=\"CR281\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PNP (purine nucleoside phosphorylase) [Arduini et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], SFRP1 [Huang et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FOXP3 [Hashemi et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], CDKN1A [Gang et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], MFGE8 [Liu et al. \u003cspan citationid=\"CR244\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], IL10RA [Yang et al. \u003cspan citationid=\"CR442\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], STAT4 [Gao et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SUCNR1 [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], AMIGO2 [Miao et al. \u003cspan citationid=\"CR283\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MMD (monocyte to macrophage differentiation associated) [Mahmoudi et al. \u003cspan citationid=\"CR266\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FCRL1 [Yang et al. \u003cspan citationid=\"CR440\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CYP51A1 [Mosavi et al. \u003cspan citationid=\"CR290\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], HIF1A [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PRDX4 [Aihaiti et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], TESPA1 [Yao et al. \u003cspan citationid=\"CR448\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], BCL2 [Kielbassa et al. \u003cspan citationid=\"CR200\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], E2F1 [Dai et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PTX3 [Targońska-Stępniak and Drelich-Zbroja], \u003cspan citationid=\"CR384\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], KL (klotho) [Ji et al. \u003cspan citationid=\"CR178\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CD22 [Bednar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], PTGS2 [Abbasi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SAV1 [Guo et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TNFAIP3 [Tang et al. \u003cspan citationid=\"CR382\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EDN1 [Panoulas et al. \u003cspan citationid=\"CR305\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], IGF2BP2 [Xu et al. \u003cspan citationid=\"CR430\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EFNB2 [Hu et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], TOB1 [Chen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], RECK (reversion inducing cysteine rich protein with kazal motifs) [van Lent et al. \u003cspan citationid=\"CR392\" class=\"CitationRef\"\u003e2005\u003c/span\u003e], DUSP5 [Moon et al. \u003cspan citationid=\"CR288\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], C9ORF72 [Fredi et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], LOXL1 [Hu et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TRIB1 [Wu et al. \u003cspan citationid=\"CR414\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], OLFM4 [Ren et al. \u003cspan citationid=\"CR325\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], KLF12 [Garc\u0026iacute;a-Berm\u0026uacute;dez et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], PFKFB3 [PFKFB3 et al. 2022], FHL1 [Friese et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], KLF9 [Huang et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], BIRC3 [Meng et al. \u003cspan citationid=\"CR280\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], RCAN3 [Park et al. \u003cspan citationid=\"CR307\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], OPTN (optineurin) [Lee et al. \u003cspan citationid=\"CR217\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], EXOSC4 [Yao et al \u003cspan citationid=\"CR447\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TRAF1 [Tang et al \u003cspan citationid=\"CR383\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], RASGRP3 [Golinski et al \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], FCRL2 [Khanzadeh et al \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], PIAS2 [Xiao et al 2016], CCL20 [Migita et al \u003cspan citationid=\"CR284\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], CXCL2 [Wang et al \u003cspan citationid=\"CR407\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CXCL5 [Tejera-Segura et al \u003cspan citationid=\"CR386\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CCL28 [Chen et al \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], CCL3L1 [Ben Kilani et al \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], CCRL2 [Galligan et al \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2004\u003c/span\u003e], CSF1R [Hu et al \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], PYCARD (PYD and CARD domain containing) [Geng et al \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FSCN1 [Chen et al \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], TFEB (transcription factor EB) [Xu and Pan, \u003cspan citationid=\"CR432\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], PDLIM2 [Wang et al \u003cspan citationid=\"CR405\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], GHRL (ghrelin and obestatin prepropeptide) [Ozgen et al \u003cspan citationid=\"CR303\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], ARRB1 [Li et al \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], PIN1 [Ma et al \u003cspan citationid=\"CR263\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAP2K2 [Krawczyk et al \u003cspan citationid=\"CR209\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TMEM187 [Khalifa et al \u003cspan citationid=\"CR198\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], ZNF804A [Fattah et al \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], LTB (lymphotoxin beta) [Sun et al \u003cspan citationid=\"CR373\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], SEMA4B [Mart\u0026iacute;nez-Ramos et al \u003cspan citationid=\"CR274\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ITGA5 [Huang et al \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], GSDMD (gasdermin D) [Ren et al \u003cspan citationid=\"CR324\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], PLEKHO1 [He et al \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], TRPA1 [Lowin et al \u003cspan citationid=\"CR254\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], F12 [McLaren et al 2022], MPG (N-methylpurine DNA glycosylase) [Huang et al \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], SLC19A1 [Imamura et al \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], CSK (C-terminal Src kinase) [Remuzgo-Mart\u0026iacute;nez et al \u003cspan citationid=\"CR323\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], UNC13D [Schulert et al \u003cspan citationid=\"CR341\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], USP6 [Eisenberg et al \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], NAPRT (nicotinate phosphoribosyltransferase) [Lei et al \u003cspan citationid=\"CR218\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PSMB9 [Li et al \u003cspan citationid=\"CR234\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PSMB5 [Wu et al \u003cspan citationid=\"CR417\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and HYAL1 [Imundo et al 2021] have been reported to be involved in the progression of RA. The altered expression of FASLG (Fas ligand) [Jones, \u003cspan citationid=\"CR188\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], CCL2 [Fatehi et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], DPP4 [Huang et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], SNCA (synuclein alpha) [Figueroa et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CCR4 [Araujo-Pires et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], NR4A1 [Yang et al. \u003cspan citationid=\"CR439\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], LEF1 [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ITGB3 [Yu et al. \u003cspan citationid=\"CR453\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], THBS1 [Li et al. \u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], IL1A [Zupan et al. \u003cspan citationid=\"CR491\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], CSF1R [Wei et al. \u003cspan citationid=\"CR412\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], TUBB3 [Nakamura et al. \u003cspan citationid=\"CR295\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], ADRB2 [Krasnova et al. \u003cspan citationid=\"CR208\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TFEB (transcription factor EB) [Wang et al. \u003cspan citationid=\"CR409\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ARRB1 [Boutin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], TRIM27 [Kim et al. \u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SLC4A2 [Wu et al. \u003cspan citationid=\"CR415\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], PIN1 [Islam et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], DVL1 [Lin et al. \u003cspan citationid=\"CR239\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and SIRT7 [Fukuda et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] plays a positive role in progression of osteoporosis. Previous studies have shown that FASLG (Fas ligand) [Tsuzaka et al. \u003cspan citationid=\"CR390\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], ICOS (inducible T cell costimulator) [Li et al. \u003cspan citationid=\"CR234\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CCL2 [Chivasso et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], DPP4 [Mascolo et al. \u003cspan citationid=\"CR276\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], MMP8 [M\u0026auml;\u0026auml;tt\u0026auml; et al. \u003cspan citationid=\"CR265\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], SNCA (synuclein alpha) [Alvarez-Castelao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], CD28 [L\u0026oacute;pez-Villalobos et al. \u003cspan citationid=\"CR252\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], IL15 [Sisto et al. \u003cspan citationid=\"CR357\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], CCR7 [Pan et al. \u003cspan citationid=\"CR304\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CCR4 [Shimizu et al. \u003cspan citationid=\"CR352\" class=\"CitationRef\"\u003e2004\u003c/span\u003e], ARRB1 [Hu et al. \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], PICK1 [Ji et al. \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and PIN1 [Ishii et al. \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] plays an important role in the development of Sj\u0026ouml;gren's syndrome. CD177 [Agidigbi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], FASLG (Fas ligand) [Dockrell et al. 2003], FOSL1 [Cai et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Jin et al. \u003cspan citationid=\"CR186\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], HBB (hemoglobin subunit beta) [Li et al. \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], ICOS (inducible T cell costimulator) [Mani et al. \u003cspan citationid=\"CR269\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], GPR15 [Hayn et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CCL2 [Howe et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], GJB2 [Li et al. \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], GJB6 [Ross et al. \u003cspan citationid=\"CR331\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], CSF1R [Combes et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], PPP2CB [Li et al. \u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAP1S [Shi et al. \u003cspan citationid=\"CR350\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], TUBB3 [Shi et al. \u003cspan citationid=\"CR351\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PYCARD (PYD and CARD domain containing) [Uusi-M\u0026auml;kel\u0026auml; et al. \u003cspan citationid=\"CR391\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ADRB2 [Sharma et al. \u003cspan citationid=\"CR263\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], FSCN1 [Chen et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], TFEB (transcription factor EB) [Jassey et al. \u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EHMT2 [Shin et al. \u003cspan citationid=\"CR353\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and RBM14 [Wang et al. \u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] were significantly regulated in patients with infections. ALAS2 [He et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FASLG (Fas ligand) [Szymanowski et al. \u003cspan citationid=\"CR376\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], FOSL1 [Zhao et al. \u003cspan citationid=\"CR474\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Kolluru et al. \u003cspan citationid=\"CR206\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CCL2 [Gholamalizadeh et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MEOX1 [Schumacher et al. \u003cspan citationid=\"CR342\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], COL19A1 [Xu et al. \u003cspan citationid=\"CR430\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EGR2 [Bo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], AXIN2 [Zheng et al. \u003cspan citationid=\"CR476\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], DPP4 [Chen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], BBC3 [Lee et al. \u003cspan citationid=\"CR216\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], COX20 [Zhang et al. \u003cspan citationid=\"CR466\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FHL3 [Guo et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PPP2CB [An et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ADRB2 [Casta\u0026ntilde;o-Amores et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], FSCN1 [Zhang et al. \u003cspan citationid=\"CR465\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TFEB (transcription factor EB) [Yan et al. \u003cspan citationid=\"CR436\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EHMT2 [Xiao et al. \u003cspan citationid=\"CR428\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FBXW5 [Hui et al. \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and SCAP (SREBF chaperone) [Chen et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e] play an important role in the pathogenesis of cardiovascular diseases. CD177 [Li et al. \u003cspan citationid=\"CR227\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MMP19 [Fan et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], FASLG (Fas ligand) [Kopiński et al. \u003cspan citationid=\"CR207\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], CALD1 [Wu et al. 2014], ICOS (inducible T cell costimulator) [Sakthivel et al. \u003cspan citationid=\"CR335\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], CCL2 [Matsuda et al. \u003cspan citationid=\"CR277\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MEOX1 [Zhao et al. \u003cspan citationid=\"CR473\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], DPP4 [Yen et al. \u003cspan citationid=\"CR449\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MMP8 [Hu et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SDC4 [Zhu et al. \u003cspan citationid=\"CR485\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] BBC3 [Liu et al. \u003cspan citationid=\"CR242\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], CSF1R [Oldham et al. \u003cspan citationid=\"CR302\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ADRB2 [Wan et al. \u003cspan citationid=\"CR397\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TFEB (transcription factor EB) [Liu et al. \u003cspan citationid=\"CR251\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], BBS1 [Viehl et al. \u003cspan citationid=\"CR395\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ARRB1 [Huang et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TRIM27 [Zhu et al. \u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PICK1 [Qian et al. \u003cspan citationid=\"CR320\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PIN1 [Shen et al. \u003cspan citationid=\"CR348\" class=\"CitationRef\"\u003e2012\u003c/span\u003e] and SIRT7 [Chen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] were correlates positively with the incidence of lung diseases. FASLG (Fas ligand) [Villa-Morales et al. \u003cspan citationid=\"CR396\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], ICOS (inducible T cell costimulator) [Chavez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CCL2 [Guilloton et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], AXIN2 [Fu et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SNCA (synuclein alpha) [Chen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CD28 [Sakamoto et al. \u003cspan citationid=\"CR334\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], IL15 [Gordon et al. \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CCR7 [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CD83 [Aladily et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], MS4A1 [Jiang et al. \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CSF1R [Gao et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ENKD1 [Song et al. \u003cspan citationid=\"CR366\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TUBB3 [Zam\u0026ograve; et al. \u003cspan citationid=\"CR459\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], PYCARD (PYD and CARD domain containing) [Su et al. \u003cspan citationid=\"CR371\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], MCM5 [Liu et al. \u003cspan citationid=\"CR244\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EHMT2 [Wang et al. \u003cspan citationid=\"CR407\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], FUZ (fuzzy planar cell polarity protein) [Chen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PDLIM2 [Wurster et al. \u003cspan citationid=\"CR422\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], HOOK2 [Wang et al. \u003cspan citationid=\"CR410\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and GHRL (ghrelin and obestatin prepropeptide) [Kasprzak and Adamek, \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] might play an important role in the pathophysiology of lymphoma. FASLG (Fas ligand) [Yolcu et al. \u003cspan citationid=\"CR451\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], FOSL1 [Zhou et al. \u003cspan citationid=\"CR478\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], HBB (hemoglobin subunit beta) [Liu et al. \u003cspan citationid=\"CR249\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CALD1 [Śnit et al. \u003cspan citationid=\"CR361\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], ICOS (inducible T cell costimulator) [Savastio et al. \u003cspan citationid=\"CR337\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CCL2 [Mir et al. \u003cspan citationid=\"CR286\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ACVR1C [Emdin et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], DPP4 [Barchetta et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], MMP8 [de Morais et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], SDC4 [Feng et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ADRB2 [Kim et al. \u003cspan citationid=\"CR204\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], TFEB (transcription factor EB) [Song et al. \u003cspan citationid=\"CR367\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], HOOK2 [Rodr\u0026iacute;guez-Rodero et al. \u003cspan citationid=\"CR327\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], GHRL (ghrelin and obestatin prepropeptide) [Cowan et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], POC1A [Li et al. \u003cspan citationid=\"CR220\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TRIM27 [Zaman et al. \u003cspan citationid=\"CR457\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], PICK1 [Andersen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PIN1 [Chellappan et al. \u003cspan citationid=\"CR304\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CAPN10 [Smail and Mohamad, \u003cspan citationid=\"CR359\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and DVL1 [Cheng et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] have been confirmed to be a potential target in diabetes mellitus. MMP19 [Pend\u0026aacute;s et al. \u003cspan citationid=\"CR309\" class=\"CitationRef\"\u003e2004\u003c/span\u003e], FASLG (Fas ligand) [Bl\u0026uuml;her et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Lu et al. \u003cspan citationid=\"CR255\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CCL2 [Wu and Ma, \u003cspan citationid=\"CR420\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], STX1A [Romeo et al. \u003cspan citationid=\"CR328\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], DPP4 [Guo et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MMP8 [Lauhio et al. \u003cspan citationid=\"CR214\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], SAMSN1 [Zhou et al. \u003cspan citationid=\"CR481\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CD28 [Berillo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IL15 [P\u0026eacute;rez-L\u0026oacute;pez et al. \u003cspan citationid=\"CR312\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], ADRB2 [Tan and Mitra, \u003cspan citationid=\"CR380\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], TFEB (transcription factor EB) [Kim et al. \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SCAP (SREBF chaperone) [Zheng et al. \u003cspan citationid=\"CR475\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], HOOK2 [Rodr\u0026iacute;guez-Rodero et al. \u003cspan citationid=\"CR327\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], BBS1 [Mykytyn et al. \u003cspan citationid=\"CR294\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], GHRL (ghrelin and obestatin prepropeptide) [Guo et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], RGS14 [Vatner et al. \u003cspan citationid=\"CR394\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PICK1 [Fadahunsi et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PIN1 [Bianchi and Manco, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and CAPN10 [Cheverud et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e] expression might be regarded as an indicator of susceptibility to obesity. FASLG (Fas ligand) [Karthikeyan et al. \u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Katsouda et al. \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ICOS (inducible T cell costimulator) [Bellan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CCL2 [Kashyap et al. \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], AXIN2 [Nie et al. \u003cspan citationid=\"CR298\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], DPP4 [Suzuki et al. \u003cspan citationid=\"CR374\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PER1 [Min et al. \u003cspan citationid=\"CR285\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MMP8 [Deng et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SDC4 [Lipphardt et al. \u003cspan citationid=\"CR240\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CD28 [Berillo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ADRB2 [Maamor et al. \u003cspan citationid=\"CR264\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TFEB (transcription factor EB) [Chen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SCAP (SREBF chaperone) [Yang et al. \u003cspan citationid=\"CR442\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], ARRB1 [Sun et al. \u003cspan citationid=\"CR372\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PIN1 [Yuan et al. \u003cspan citationid=\"CR454\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CAPN10 [Zhou et al. \u003cspan citationid=\"CR482\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], SIRT7 [Zhou et al. \u003cspan citationid=\"CR481\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], DAPK3 [Xue et al. \u003cspan citationid=\"CR435\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SMARCA4 [Ma et al. \u003cspan citationid=\"CR258\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and NDUFC2 [Gallo et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] genes are a potential biomarkers for the detection and prognosis of hypertension. FASLG (Fas ligand) [Ethell and Buhler, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], FOSL1 [Ma et al. \u003cspan citationid=\"CR259\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ICOS (inducible T cell costimulator) [Bjursten et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], DNAAF1 [Miao et al. \u003cspan citationid=\"CR282\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], GPR15 [Ammitzb\u0026oslash;ll et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CCL2 [Xiromerisiou et al. \u003cspan citationid=\"CR429\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], STX1A [Luppe et al. \u003cspan citationid=\"CR257\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EGR2 [Funalot et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], AXIN2 [Fancy et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], DPP4 [Al-Badri et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], MAD1L1 [Sokolov et al. \u003cspan citationid=\"CR363\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CLN6 [Talbot et al. \u003cspan citationid=\"CR378\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CSF1R [Hu et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NDUFS7 [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TUBB3 [Puri et al. \u003cspan citationid=\"CR319\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], PYCARD (PYD and CARD domain containing) [Liu et al. \u003cspan citationid=\"CR247\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TFEB (transcription factor EB) [Yang et al. \u003cspan citationid=\"CR438\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EHMT2 [Carvalho et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and FUZ (fuzzy planar cell polarity protein) [Chen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] have been demonstrated to accelerate neurological disorders. CD177 [Yang et al. 2019,] FASLG (Fas ligand) [Sayani et al. \u003cspan citationid=\"CR338\" class=\"CitationRef\"\u003e2004\u003c/span\u003e], FOSL1 [Ma et al. \u003cspan citationid=\"CR259\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], LAMB3 [Liu et al. \u003cspan citationid=\"CR241\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CTH (cystathionine gamma-lyase) [Jin et al. \u003cspan citationid=\"CR186\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], GPR15 [Jegodzinski et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CCL2 [Pozzi et al. 2024], GJB2 [Zhang et al. \u003cspan citationid=\"CR468\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EGR2 [Symonds et al. 2023], DPP4 [Hellenthal et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], BBC3 [Zhang et al. \u003cspan citationid=\"CR470\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], B9D2 [Wang et al. \u003cspan citationid=\"CR399\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAP7 [Wang et al. \u003cspan citationid=\"CR405\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CLN6 [Kay and Palmer, \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], ZMYND10 [Cho et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e,] CSF1R [Hume et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], NME3 [Flentie et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], ENKD1 [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAP1S [Shi et al. \u003cspan citationid=\"CR349\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and KRT18 [Xiao et al. \u003cspan citationid=\"CR426\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] expression might be regarded as an indicator of susceptibility to inflammation. Altered expression of ALAS2 [He et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FASLG (Fas ligand) [Soni et al. \u003cspan citationid=\"CR369\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], CCL2 [Zheng et al. 2018], EGR2 [Huang et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], DPP4 [Lee et al. \u003cspan citationid=\"CR215\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PER1 [Zhu et al. \u003cspan citationid=\"CR484\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MMP8 [da Silva-Neto et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SNCA (synuclein alpha) [Sola et al. \u003cspan citationid=\"CR364\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CD28 [Liu et al. \u003cspan citationid=\"CR251\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], IL15 [Chen et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], BBC3 [Liu et al. \u003cspan citationid=\"CR242\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], COX20 [Keerthiraju et al. \u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], FHL3 [Guo et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CLN6 [Kanninen et al. \u003cspan citationid=\"CR190\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], CSF1R [Hu et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NDUFS7 [Zhang et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], NME3 [Chen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], MAP1S [Yue et al. \u003cspan citationid=\"CR455\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], TUBB3 [Mariani et al. \u003cspan citationid=\"CR273\" class=\"CitationRef\"\u003e2011\u003c/span\u003e] and ADRB2 [Wan et al. 2011] are associated with prognosis in patients with oxidative stress. FASLG (Fas ligand) [Rossin et al. \u003cspan citationid=\"CR332\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], FOSL1 [Li et al. \u003cspan citationid=\"CR227\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ICOS (inducible T cell costimulator) [Ban et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], GPR15 [Zhao et al. \u003cspan citationid=\"CR472\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CCL2 [Rafei and Galipeau, \u003cspan citationid=\"CR321\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], MEOX1 [Jiao et al. \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EGR2 [Dai et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], DPP4 [Huang et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PER1 [Zhu et al. \u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MMP8 [Nyg\u0026aring;rdas and Hinkkanen, \u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], FHL3 [Guo et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], MAP7 [Navarro-Barriuso et al. \u003cspan citationid=\"CR296\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CLN6 [Poppens et al. \u003cspan citationid=\"CR315\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CSF1R [Nissen et al. \u003cspan citationid=\"CR299\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PPP2CB [Li et al. \u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ADRB2 [Chu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], DUSP23 [Balada et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], FSCN1 [Chen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], TFEB (transcription factor EB) [Xia et al. \u003cspan citationid=\"CR424\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and EHMT2 [Pollin et al. \u003cspan citationid=\"CR314\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] have been reported to be altered expressed in autoimmune diseases. GO and REACTOME pathway enrichment analysis provides novel biological indicators, in addition to molecular mechanisms and targets for predicting clinical prognosis of RA patients, which requires further clinical studys by multi-omics anlysis validation.\u003c/p\u003e\u003cp\u003ePPI and module analysis suggest that hub genes detected in the present study might be involvement in RA progression. MYC is a key controler of cell growth and metabolism. Its deregulation plays a central role in autoimmune diseases [Mountz et al. \u003cspan citationid=\"CR291\" class=\"CitationRef\"\u003e1985\u003c/span\u003e]. MYC might be considered as a novel biomarker for RA. MKI67 gene encodes Ki-67, a nuclear proliferation protein critical for cell cycle progression and chromosome organization. Its deregulation plays a central role in RA [Pessler et al. \u003cspan citationid=\"CR313\" class=\"CitationRef\"\u003e2008\u003c/span\u003e]. MAPK6 is an atypical MAP kinase with essential roles in cell growth, survival, motility, and differentiation [Tan et al. \u003cspan citationid=\"CR379\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. MAPK6 might influence RA by regulating proliferation or migration of T cells and macrophages. MAPK6 might emerging as a promising novel therapeutic target for RA. HSPA9 is a mitochondrial chaperone critical for protein folding, mitochondrial function, stress responses, and cell survival [Han et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. Its abnormal regulation is might be associated with autoimmune disease. HSPA9 might be novel therapeutic target for RA. ANLN is an actin-binding scaffold protein essential for cell division and actin remodeling [Li et al. \u003cspan citationid=\"CR225\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. ANLN might be novel diagnostic biomarker and a potential therapeutic target.for RA. RSL1D1 nucleolar protein that controls ribosome biogenesis, cell cycle, apoptosis, and senescence [Jiang et al. \u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. RSL1D1 might emerging as novel therapeutic target and prognostic biomarker for RA. DDX21 is a nucleolar RNA helicase essential for ribosome biogenesis, RNA metabolism, and innate immunity [Xiao et al. \u003cspan citationid=\"CR427\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. DDX21 might act as an autoantigen in systemic autoimmune diseases. DDX21 might emerging as novel prognostic biomarker and therapeutic target for RA. SQSTM1 is a scaffold protein essential for autophagy, oxidative stress defense, and inflammatory signaling [Hou et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. SQSTM1 might novel biomarker and therapeutic target at the crossroads of protein degradation and cell signaling in RA. ARRB1 is a scaffold protein regulating GPCR desensitization, endocytosis, and downstream signaling [Cahill et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. Its dysregulation contributes to RA [Li et al \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2013\u003c/span\u003e]. RAC1 is a master controler of cytoskeletal dynamics, proliferation, migration, ROS production, and gene expression [Ma et al. \u003cspan citationid=\"CR261\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. Its dysregulation might be implicated in RA. Its dysregulation is implicated in autoimmune disease. RAC1 might represents novel biomarker and a promising drug target for RA. BSG is a transmembrane glycoprotein associated in matrix remodeling, metabolism, immune regulation, and pathogen entry [Fu et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. BSG might be novel therapeutic target and biomarker for RA. TRIM27 is an E3 ubiquitin ligase and transcriptional regulator involved in cell survival, apoptosis, immune response, and DNA repair [Zaman et al. \u003cspan citationid=\"CR457\" class=\"CitationRef\"\u003e2013\u003c/span\u003e]. Dysregulation might contributes to autoimmune disease. TRIM27 might be an novel biomarker and therapeutic target for RA. POLR2E encodes a shared subunit of RNA polymerases I, II, and III, key for mRNA transcription and cellular viability. Dysregulation of POLR2E might contributes to autoimmune disease. POLR2E might represents a novel biomarker of transcriptional activity and a potential therapeutic target in RA. POLR2I encodes a zinc finger subunit of RNA polymerase II, important for transcription fidelity, DNA repair, and mRNA synthesis. Dysregulation might linked to autoimmune disease. POLR2I might serves as novel biomarker and a therapeutic target in RA. POLR2F encodes a shared subunit of RNA polymerases I, II, and III, key for mRNA, rRNA, and tRNA transcription. Its dysfunction might be linked to autoimmune disease. POLR2F might be novel biomarker and therapeutic target in RA. LMO2 is a LIM-domain transcriptional cofactor essential for hematopoiesis and angiogenesis [Ganta and Annex, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. Its altered expression might contributing indirectly to autoimmune disease. Clinically, LMO2 might serves as novel biomarker and a therapeutic target in RA. This investigation approach identified the possible hub genes that were highly correlated with the PPI network to identify the novel target genes might be involved in the pathogenesis of RA and listed in Suplimentary Table S1.\u003c/p\u003e\u003cp\u003eThe role of miRNAs and TFs in RA that we have identified still needs to be further explored. In addition, there are still limited studies related to miRNAs and TFs in the RA. It is evident that miRNA-hub gene regulatory network, TF-hub gene regulatory network play a key part in the development of RA. hsa-miR-34c-5p [Jiang et al. \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], ESR1 [Pawlik et al. \u003cspan citationid=\"CR308\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], FOS [Huber et al. \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], RELA [Yang et al. \u003cspan citationid=\"CR440\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], JUN [Lai et al. \u003cspan citationid=\"CR213\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NFIC [Jia et al. \u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and USF2 [Hu et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] could serve as a potential therapeutic targets for RA. This investigation suggests that hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-499a-5p, hsa-miR-3065-5p, hsa-miR-573, hsa-miR-411-3p, hsa-miR-22-3p, hsa-miR-454-3p, hsa-miR-30c-1-3p, NFYA, EN1, FOXL1 and HINFP might be a novel therapeutic targets for RA, and the related molecular mechanism is worthy of further investigations.\u003c/p\u003e\u003cp\u003eWhile our study provides significant insights into the action of drug on expression of hub genes. Our findings suggest that drugs- Atorvastatin, Mefloquine, Vindesine, Rasagiline, 2,6-dicarboxynaphthalene, Oxprenolol, Acarbose, Famoxadone, Ixabepilone and Sunitinib concurrently target to hub genes include DPP4, ADORA2A, TUBB1, BCL2, HBB, ADRB2, AMY2A, UQCRC1, TUBB3 and CSF1R, potentially controlling the development of RA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBioinformatics analysis of NGS data is a useful technique to explore the molecular mechanism and pathogenesis of RA. There were numerous genes that were differentially expressed in the RA and normal control groups. These hub genes, miRNA and TFs migt play important roles in the onset and development of RA and serve as therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRheumatoid Arthritis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNext generation sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene expression omnibus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtein-protein interaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emiRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMicro ribonuclic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscription factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMYC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMYC proto-oncogene, bHLH transcription factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMKI67\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMarker of proliferation Ki-67\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAPK6\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMitogen-activated protein kinase 6\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHSPA9\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHeat shock protein family A (Hsp70) member 9\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANLN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnillin, actin binding protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSQSTM1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequestosome 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARRB1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArrestin beta 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRAC1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRac family small GTPase 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBSG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBasigin (Ok blood group)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTRIM27\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTripartite motif containing 27\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thanks very much to Wright HL, University of Liverpool, Institute of Life Course and Medical Sciences, Liverpool, United Kingdom, the author who deposited their NGS dataset GSE274996, into the public GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWritten Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE274996) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE274996]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. V. \u0026nbsp; \u0026nbsp;- Writing original draft, and review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.P. \u0026nbsp; \u0026nbsp; - Formal analysis and validation\u003c/p\u003e\n\u003cp\u003eV.S. \u0026nbsp; - Resources and investigation\u003c/p\u003e\n\u003cp\u003eK,P. \u0026nbsp; - Investigation and validation\u003c/p\u003e\n\u003cp\u003eC. V. \u0026nbsp; - Software and investigation\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi H, Sharif M, John P, Bhatti A (2025) Integrated Network Pharmacology and Molecular Modeling Approach for Potential PTGS2 Inhibitors against Rheumatoid Arthritis. 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Clin Chem Lab Med 50(8):1379\u0026ndash;1385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/cclm-2011-0589\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2011-0589\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"KLE CGollege of Pharamacy, Gadag 582101, Karanataka, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, Differentially expressed genes, Bioinformatics analysis, Next generation sequancing analysis, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7663291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7663291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElderly patients are prone to rheumatoid arthritis (RA), which may cause reduce quality of life. However, the molecular pathogenesis of RA has not been fully elucidated, and current treatments remain inadequate. Therefore, it is important to explore the molecular mechanism of RA. Next generation sequancing (NGS) data of RA (GSE274996) was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in cases of RA and normal controls, and the Gene Ontology (GO) and and REACTOME pathway enrichment analysis were performed using the DESeq2 R/Bioconductor software package and g:Profiler, respectively. Analysis and visualization of protein-protein interaction networks (PPI) were carried out with IID and Cytoscape. miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drugs associated with hub genes. The diagonstic value of hub genes were assessed by receiver operating characteristic curve (ROC). Total of 958 DEGs were identified between RA and normal control in GSE274996, including 479 up-regulated and 479 down-regulated genes. These genes were enriched in multicellular organismal process, cytosol, enzyme binding, signal transduction, organelle organization, membrane, electron transfer activity and metabolism. A total of hub genes were collected, including MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG and TRIM27, miRNAs were predicted including hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-411-3p and hsa-miR-34c-5p, TFs were predicted including ESR1, FOS, EN1 and FOXL1 and 4 drugs molecules were predicted including Atorvastatin, Mefloquine, Oxprenolol and Acarbose. Evaluation of MYC, MKI67, MAPK6, HSPA9, ANLN, SQSTM1, ARRB1, RAC1, BSG, TRIM27, hsa-miR-5094, hsa-miR-20a-5p, hsa-miR-411-3p hsa-miR-34c-5p, ESR1, FOS, EN1 and FOXL1 as potential biomarkers can contribute to the subsequent theoretical analysis of potential molecular mechanisms and development of RA, so that the diagnosis of RA might be more accurate, and it is possible to provide therapeutic and prognostic medicine targets.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Analysis Screened and Identified Key Genes, miRNAs and TFs as Potential Biomarkers for Progression of Rheumatoid Arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 15:42:59","doi":"10.21203/rs.3.rs-7663291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55048375,"name":"Bioinformatics"},{"id":55048376,"name":"Computational Biology"},{"id":55048377,"name":"Drug Discovery, Design, \u0026 Development"},{"id":55048378,"name":"Rheumatology"}],"tags":[],"updatedAt":"2025-09-23T15:42:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 15:42:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7663291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7663291","identity":"rs-7663291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}