RGS2-related non-coding interaction network modulates the NF-Kappa B signaling pathway in MS patients: a systems biology investigation

RGS2-related non-coding interaction network modulates the NF-Kappa B signaling pathway in MS patients: a systems biology investigation | 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 RGS2-related non-coding interaction network modulates the NF-Kappa B signaling pathway in MS patients: a systems biology investigation Parisa Forouzanfar, Mohammad Hashemian, Mojdeh Mahmoudian, Melika Khorsandi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4184298/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 Background: Multiple Sclerosis (MS) is recognized as the most prevalent chronic inflammatory condition that targets the brain and spinal cord. According to the third edition of the Atlas of MS, around 2.8 million individuals worldwide are affected by Multiple Sclerosis, equating to a prevalence of 35.9 cases per 100,000 people. In this study, we evaluated the expression levels of potential biomarkers in a high-throughput MS dataset to find novel highly dysregulated RNAs in MS patients. Furthermore, a novel RNA regulatory network has been visualized to find new non-coding interaction patterns in the MS-related signaling pathways. Methods: Using R Studio, high-throughput gene expression MS datasets were analyzed to find highly dysregulated mRNAs in MS patients. miRNA, lncRNA, and protein interaction analyses were conducted by miRWalk and lncRRIsearch databases. Using visualized interaction networks, pathway enrichment analysis was performed using Enrichr and KEGG. qRT-PCR experiment was performed for the validation of gene expression analyses. Results: RGS2 was found to be significantly upregulated in both microarray (logFC: 1.7667, adj.P. Value: 0.0079) and qRT-PCR analyses (logFC: 4.547, p-value < 0.0001). Similarly, the lncRNAs NCK1-DT (logFC: 2.155, p-value: 0.0132) and ASH1L-AS1 (logFC: 3.345, p-value < 0.0001) exhibited elevated expression in MS samples, suggesting a regulatory impact on RGS2 expression levels. The marked changes in the expression of RGS2, NCK1-DT, and ASH1L-AS1 in MS patients compared to normal samples position them as promising diagnostic biomarkers. Additionally, RGS2 and its associated proteins have been implicated in modulating the NF-Kappa B signaling pathway. MiR-4638-3p was identified to directly downregulate RGS2 expression, while miR-4525 influences the expression of RGS2 and ASH1L-AS1 within a competing endogenous RNA (ceRNA) network. Conclusion: NCK1-DT and ASH1L-AS1 are the two novel diagnostic biomarkers of MS. Mentioned lncRNAs might affect the normal regulatory mechanisms of “NF-Kappa B signaling pathway” through direct and indirect interaction with mRNA RGS2. Cancer Biology long non-coding RNA Multiple Sclerosis Bioinformatics Microarray Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction The most widespread chronic inflammatory disease affecting the brain and spinal cord is known as Multiple Sclerosis (MS) ( 1 ). This condition is defined by the loss of myelin sheath and neurons caused by the attack of autoreactive T cells in the central nervous system (CNS) as well as Failed endogenous remyelination ( 2 , 3 ) inducing the development of demyelinated areas that are called grey matter lesions (GMLs) and white matter lesions (WMLs) all over the CNS ( 4 ) which ultimately results in nontraumatic neurological disability (Cotsapas et al., 2018). Based on the latest edition of Atlas of MS (3rd edition), approximately 2.8 million people are suffering from MS globally (35.9 per 100,000 individuals). Epidemiological investigations reveal that the prevalence and incidence of MS have been rising around the world, and the average age at diagnosis is 32 years. MS is twice as common in women as in men ( 4 , 5 ). Despite the need for more clarity surrounding the precise etiology and pathogenesis of MS, documented evidence reveals that its underlying cause is multifaceted. Contributing factors comprise genetic predisposition, as well as various environmental factors, especially deficient levels of vitamin D, Epstein–Barr virus (EBV) infection, geographic region, obesity, and smoking ( 6 – 10 ). The majority of MS patients (around 85%) experience Relapsing-Remitting MS (RRMS), which is marked by episodes of abrupt neurological impairment followed by complete or partial recovery. After 10–15 years, most RRMS patients develop Secondary Progressive MS (SPMS), which is characterized by ongoing neurological deterioration. In a small percentage of MS patients (about 15%), the disease can be progressive from the onset, which is known as Primary Progressive MS (PPMS) ( 11 – 14 ). Immunopathology of MS is associated with both CD4 + and CD8 + T cells. Myelin components are targeted by pathogenic T helper 17 (Th17), T helper 1 (Th1), and CD8 + autoreactive T cells. Furthermore, local microglia and macrophages become activated in demyelinated lesions. B cells are also involved in the immunopathogenesis of the disease. CD20 + B cells are the most frequently seen in the early stages of the disease, while plasma blasts and plasma cells are the most prominent in the later stages. B cell engagement relates to not only antibody production, but also antigen presentation to T cells and modulation of T cells and myeloid cells function as a result of cytokine secretion ( 13 , 15 , 16 ) Long non-coding RNAs (lncRNAs) are vital contributors to intricate biological processes, and their presence in body fluids, coupled with their specific distribution in tissues, positions these biomolecules as potentially valuable candidates for diagnosing MS ( 17 ). Despite not being translated into proteins, these elements play essential roles in various physiological processes, including cellular differentiation, stress response, aging, cell growth, programmed cell death, and the regulation of gene transcription ( 18 ). Research has revealed that lncRNAs could play a pivotal role in autoimmune diseases by influencing immune cells' activation, differentiation, and imbalanced expression (including T cells, B cells, macrophages, and NK cells). These effects have been observed in autoimmune conditions like psoriasis, rheumatoid arthritis, and systemic lupus erythematosus (SLE) ( 19 ). It is noteworthy that certain lncRNAs have been identified as being dysregulated in peripheral blood mononuclear cells among individuals with MS, indicating their potential involvement in the pathogenesis of the disease ( 20 , 21 ). Our previous studies mentioned the potential biological roles of different lncRNAs in immune-related diseases, including different cancer types, including breast cancer ( 22 – 26 ), gastric cancer ( 27 , 28 ), and MS ( 29 ). Especially about MS, previous investigations mentioned novel regulatory lncRNAs with dysregulation in MS patients. For example, Hosseini et al. identified elevated expression levels of AC007278.2 and IFNG-AS1-001 lncRNAs in the peripheral blood mononuclear cells (PBMC) of 50 individuals with RRMS. Specifically, these increased expressions were observed in patients during the relapsing phase, half of whom were undergoing treatment with interferon beta. In contrast, the lncRNA IFNG-AS1-003 demonstrated higher levels in MS patients during the remitting phase compared to those in the relapsing phase. These findings were in comparison to a control group of healthy individuals ( 30 ). Our study aims to identify new diagnostic biomarkers for MS by analyzing high-throughput data. We employed a systems biology method to analyze non-coding interactions, aiming to discover previously unidentified lncRNAs that might control the expression of key mRNAs and show increased levels in individuals with MS. 2. Materials and Methods 2.1. Microarray data analysis To find novel differentially expressed genes (DEGs) in MS patients, raw data of GSE43591 ( 31 ) was downloaded, normalized (using the affy ( 32 ) package), and analyzed using the limma package in the R programming language environment. Limma employs several statistical concepts effectively tailored for extensive expression analyses. It processes a matrix of expression data, with each row corresponding to a gene or another genomic element pertinent to the study, and each column representing an RNA sample. Limma fits a linear model to the data in each row, leveraging the versatility of linear models for multiple analytical purposes ( 33 ). In this analysis, the expression level of mRNAs in 10 MS samples was analyzed and compared with 10 normal control samples using the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). For the quality control processes of microarray analysis, the Principal Component Analysis (PCA) and correlation analysis between the samples were performed and related plots were drawn using ggplot2 ( 34 ) and pheatmap, respectively. Adjusted P. Value 1 was considered as the threshold of DEG selection. Using ggplot2 and pheatmap packages, the plots of microarray analysis were illustrated in R Studio. 2.2. Interaction Network and Signaling Pathway Analyses The interactions between miRNAs and mRNAs were analyzed using the miRWalk tool ( 35 , 36 ). In this study, miRNAs were selected with the following criteria: location of interaction: 3’UTR region of mRNA (seed region), score: 1, and ordered based on binding energy (lower to higher). lncRNA-miRNA interaction analysis was conducted by lncBase (Version: 3) ( 37 ). All microRNAs in the lncRNA-miRNA interaction analysis were visualized. Direct interactions of mRNA with lncRNAs were conducted using the lncRRIsearch database ( 38 ). Visualization of the non-coding interaction network was performed using Cytoscape (Version: 3.10.1) (Otasek et al., 2019; Shannon et al., 2003).The analysis of protein-protein interactions was carried out using the STRING database. ( 41 ). The interactions of DEGs in microarray analysis were visualized and clustered using the K-mean clustering method, and based on this clustering the DEGs were divided into three different clusters. Using Enrichr, related signaling pathways to the protein interaction network were demonstrated ( 42 – 44 ) for each cluster in the protein interaction network. Using the KEGG ( 45 , 46 ) online database, the crucial signaling pathway was visualized. 2.3. Sample Collection In this case-control study, 40 blood samples were collected from individuals diagnosed with MS and healthy controls (20 samples from MS patients and 20 samples from healthy individuals), following the revised McDonald criteria ( 47 ). The healthy individuals served as the control group and were selected based on the absence of any inflammatory or autoimmune conditions. RNA extraction, cDNA synthesis, and Real-Time PCR experiment Total RNA from blood samples was isolated using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) as per the guidelines provided by the manufacturer. The quality of the isolated RNA was assessed using a nanodrop instrument. Subsequently, cDNA was synthesized from the RNA through reverse transcription using the Roje kit (Iran/Tehran). Both the RNA samples and the resulting cDNAs were preserved at -80°C. Real-time PCR was conducted using SYBR green (qPCRBIO SyGreen Mix Lo-ROX, England) in a Magnetic Induction Cycler (MIC) device. Gene expression levels were normalized to the GAPDH gene, serving as an internal standard. Primers specific to each gene were designed with the aid of oligo software (version 7), and the sequences for these primers designated for Real-Time PCR are detailed in Table 1 . Table 1 Primer sequences of selected RNAs for qRT-PCR experiment. Gene Forward/Reverse Primer Sequence (5'→3') RGS2 Forward AGCTACAAGTGGCTGCTTTAC Reverse CATGAGGCTCTGTGGTGATTTG NCK1-DT Forward GCTGAGTGGATCAGGGACTAAC Reverse GGACCTGGAACCAGCATCAC ASH1L-AS1 Forward CGGTTGACCTGAGCCTACTTC Reverse ATACCCGTGTCGGTCCATTC GAPDH Forward ACAGGGTGGTGGACCTCAT Reverse AGGGGTCTACATGGCAACTG 2.4. Statistical analysis GraphPad Prism (Version 9.0.0) was used for creating graphs and analyzing data from real-time PCR experiments. The normal distribution of the expression data was evaluated using the Kolmogorov-Smirnov test. Differences in the expression levels of lncRNAs ASH1L-AS1 and NCK1-DT between control and MS samples were analyzed using both paired and unpaired t-tests. The construction of the receiver operating characteristic (ROC) curves and the analysis were performed using GraphPad Prism. The specificity and sensitivity of Regulator of G-protein Signaling 2 (RGS2), ASH1L-AS1, and NCK1-DT as diagnostic markers for Multiple Sclerosis were assessed through ROC curves and the area under the curve (AUC) values. AUC values less than 0.7 were deemed not suitable for biomarkers, values between 0.7 and 0.8 were considered acceptable, values between 0.8 and 0.9 were seen as good, and values greater than 0.9 were classified as excellent diagnostic biomarkers. The quantitative expression of lncRNAs between the MS and healthy groups was analyzed using the Mann-Whitney U test. Differential gene expression analysis in microarray data was performed using R Studio (Version 4.3.2). Clinicopathological data frequency tables were created with IBM SPSS Statistics (Version 27.0.1), and common DEGs were identified using Venny online software (v.2.1.0). The study set the significance threshold at an adjusted P-value of less than 0.05. 3. Results 3.1. Microarray Analysis 3.1.1. Evaluation of the Quality of Microarray Samples Microarray analysis of GSE43591 was conducted to identify new dysregulated mRNAs in MS patients. DEGs were determined using the limma package, selecting genes with |logFC| > 1 and an adjusted P-value < 0.05. PCA indicated that the sample labeled "patient 7" was of suboptimal quality and was subsequently excluded (as shown in Fig. 1 ). A correlation heatmap demonstrated distinct clustering of control and patient samples (as depicted in Fig. 2 ). These findings collectively affirm the high quality of the microarray samples in GSE43591. 3.1.2. RGS2 has the highest expression in the high-throughput dataset Through 35214 mRNAs analyzed by R Studio, 32 mRNAs were up-regulated, and 62 mRNAs were low expressed (Table 3 ). Among all DEGs, RGS2 has the most increase in the expression level in MS samples (logFC: 1.7667, adj.P. Value: 0.0079). The volcano plot (Fig. 3 ) shows the distribution of up and down-regulated mRNAs in MS samples. Also, the top 20 up and down-regulated mRNAs are represented in Fig. 4 . Table 3 Top 10 up and down-regulated mRNAs in GSE43591. ID logFC adj.P.Val Gene. Symbol up/down 202388_at 1.766650062 0.007898361 RGS2 up-regulated 221731_x_at 1.51011654 0.0142012 VCAN up-regulated 201739_at 1.509796385 0.003032329 SGK1 up-regulated 217739_s_at 1.490552959 0.018842419 NAMPT up-regulated 216834_at 1.429901587 0.019132056 RGS1 up-regulated 223502_s_at 1.38741612 0.018512132 TNFSF13B up-regulated 238890_at 1.275610591 0.008817609 BRWD1 up-regulated 209795_at 1.238893519 0.004333034 CD69 up-regulated 202643_s_at 1.227120683 0.002205417 TNFAIP3 up-regulated 1557257_at 1.215805974 0.01159354 BCL10 up-regulated 232686_at -1.366588245 0.017672348 SIGLEC17P down-regulated 37145_at -1.393112628 0.047448739 GNLY down-regulated 230464_at -1.465047902 0.024049569 S1PR5 down-regulated 205495_s_at -1.508453998 0.042055898 GNLY down-regulated 215894_at -1.511393342 0.001527875 PTGDR down-regulated 212070_at -1.664695451 0.035022517 GPR56 down-regulated 235811_at -1.732083026 0.002659438 down-regulated 1564139_at -1.796156809 0.018842419 A2M-AS1 down-regulated 205898_at -1.883483227 0.049875794 CX3CR1 down-regulated 210387_at -1.92938721 0.047160596 HIST1H2BG down-regulated 3.2. Bioinformatics Analyses 3.2.1. lncRNAs NCK1-DT and ASH1L-AS1 regulate RGS2, directly and indirectly The analysis of lncRNA interactions suggested that two newly identified lncRNAs, NCK1-DT and ASH1L-AS1, could potentially influence RGS2 expression through direct interactions (see Fig. 5 ). Furthermore, the examination of miRNA interactions identified key miRNAs that regulate RGS2 and two associated lncRNAs. According to the analysis conducted with miRWalk, miR-4638-3p appears to significantly suppress the expression of RGS2 mRNA by directly interacting with its 3’UTR region (energy: -26.8 kcal/mol). Furthermore, based on miRNA interaction analysis of the lncBase database, miR-4525 regulates RGS2 and lncRNA ASH1L-AS1, and this interaction axis shows a competitive endogenous RNA (ceRNA) relation between RGS2 and ASH1L-AS1. Also, miR-365a-3p and miR-365b-3p have direct interaction with lncRNAs NCK1-DT and ASH1L-AS1. So, the two lncRNAs also have ceRNA interaction and could regulate the expression level of each one indirectly (Fig. 5 ). 3.2.2. RGS2 protein interacts with three clusters of DEGs in “NF-Kappa B Signaling” For evaluation of possible interactions between the DEGs in this study, protein interaction analysis was performed using STRING (Fig. 6 a). The K-mean clustering method was performed for the clustering of DEGs, based on interaction patterns. Interacted proteins were divided into three clusters, shown in Fig. 6 a and Table 4 . Using the Enrichr database, related pathways of each cluster were evaluated. Based on KEGG pathway analysis using Enrichr, the first cluster of proteins (in which RGS2 is located) regulates the “NF-kappa B signaling pathway” (Fig. 7 ). Also, the second cluster modulates the “Sphingolipid signaling pathway,” and the third cluster (H2BC5 protein) regulates the “Systemic lupus erythematosus signaling pathway.” Also, an RGS2-specific interaction analysis was performed to find proteins related to RGS2 (Fig. 6 b). Table 4 Protein interaction of DEGs. The proteins in this interaction network are divided into three clusters (using the K-mean clustering method), and each cluster regulates a specific signaling pathway. Number cluster color gene count Proteins Pathway (KEGG) 1 Red 7 BCL10 NF-kappa B CD69 RGS1 RGS2 SGK1 TNFAIP3 TNFSF13B 2 Green 4 ADGRG1 Sphingolipid signaling pathway CX3CR1 GNLY S1PR5 3 Blue 1 H2BC5 Systemic lupus erythematosus 3.3. qRT-PCR Experiment To confirm the findings from the microarray data analysis and assess potential alterations in the expression levels of the two chosen lncRNAs, qRT-PCR experiments were conducted. Results from these experiments indicated that RGS2 (logFC: 4.547, p-value < 0.0001), NCK1-DT (logFC: 2.155, p-value: 0.0132), and ASH1L-AS1 (logFC: 3.345, p-value < 0.0001) exhibited notable upregulation in MS samples relative to the controls (Fig. 8 ). ROC analysis demonstrated that RGS2 (AUC: 0.9975, p-value < 0.0001) and ASH1L-AS1 (AUC: 0.9225, p-value < 0.0001) could serve as excellent diagnostic markers for MS, while NCK1-DT presents as an acceptable marker (AUC: 0.7275, p-value: 0.0138, as depicted in Fig. 9 ). A Pearson correlation analysis was also carried out to explore potential correlations between the – ΔΔCt values obtained from the qRT-PCR experiments. However, no significant correlation was observed between the expression levels of RGS2 and the two lncRNAs under investigation (Fig. 10 ). 4. Discussion Prior research has shed light on RGS2's involvement in neurodegenerative diseases and key signaling pathways. Dusonchet and colleagues demonstrated that RGS2 affects both GTPase and kinase activities of LRRK2, significantly influencing the length of neuronal processes. This role of RGS2 is vital for protecting against damage from the prevalent LRRK2 mutation, G2019S, suggesting a novel action mechanism for GAP proteins, distinct from their known GTPase activity modulation. Such findings position RGS2 as a promising candidate for therapeutic intervention in Parkinson's disease (PD), particularly for conditions related to LRRK2 mutations ( 48 ). As a member of the RGS protein family, RGS2 interacts with Gα components of heterotrimeric G proteins, specifically targeting Gαq and Gαi subunits to slow their GTP to GDP hydrolysis, thereby reducing signal transduction from G-protein-coupled receptors (GPCRs) and playing a crucial role in synaptic plasticity ( 49 ). Moreover, RGS2 directly interacts with adenylyl cyclases, decreasing cyclic AMP (cAMP) production, and influencing GPCR-mediated Akt signaling pathways, highlighting its regulatory impact ( 50 ). Increased RGS2 expression intensifies neurodegeneration caused by mutant Huntingtin (mHtt), with preliminary data suggesting a link to the Erk/MAP kinase signaling pathway. However, the exact mechanisms remain to be fully elucidated ( 51 ). This study further supports the hypothesis that RGS2 upregulation may elevate the risk of neurodegenerative diseases, reinforcing its potential as a therapeutic target ( 52 ). In our research, we found that RGS2 and its interacting network play a significant role in modulating the NF-κB signaling pathway. The NF-κB family of transcription factors is crucial for regulating genes involved in vital cellular functions such as survival, cell death, inflammation, growth, and differentiation ( 53 ). Within the nervous system, NF-κB is uniquely active at high levels in neurons, highlighting its essential role in overseeing specific functions in the central nervous system (CNS). It is a critical regulator of neuronal architecture and profoundly influences neuronal plasticity, which in turn impacts learning, memory, and behavior. Additionally, NF-κB provides neurons with protection against damage from excitotoxicity, oxidative stress, and ischemia. It also contributes to the inflammatory response and cell death in cases of brain injury and stroke. In contrast, glial cells show no inherent NF-κB activity under unstressed conditions, suggesting that NF-κB activation in these cells is mainly triggered by stress or pathological conditions ( 54 ). Our research findings demonstrate that RGS2 interacts with the proteins BCL10, TNFAIP3, and TNFSF13B, which are key regulators of the NF-κB signaling pathway. The activation of NF-κB, a crucial process following T-cell receptor (TCR) stimulation, depends on assembling the CARMA1–Bcl-10–MALT1 (CBM) complex at the cell membrane, a step facilitated by protein kinase Cθ (PKCθ) ( 55 ). This assembly is essential for NF-κB to be subsequently activated by the IKK complex. The critical nature of the CBM complex in this pathway is highlighted by studies on mice lacking CARMA1, Bcl-10, or MALT1, which show reduced NF-κB activation, lower Th1 cytokine production, and decreased T-cell proliferation following TCR activation ( 56 – 58 ). Additionally, genome-wide association studies (GWASs) have identified new genetic loci linked to MS susceptibility in the TNFRSF1A and MALT1 genes, encoding TNF-R1 and MALT1, respectively. These studies have also confirmed TNFAIP3 as a risk gene for MS, underscoring its significance in MS pathogenesis. Based on previous studies, the NCK1-DT is an important regulatory factor for various cancer types, including prostate cancer, lung cancer, and gastric cancer ( 59 ). Also, Xie et al. revealed that ASH1L-AS1 has a significant regulatory role in gastric cancer development via modulation of the “RAS signaling pathway” ( 60 ). However, there was no previous study about the possible roles of NCK1-DT and ASH1L-AS1 in MS development. In this study, for the first time, we evaluated the expression level of two mentioned lncRNAs in MS patients and revealed the up-regulation of NCK1-DT and ASH1L-AS1 in MS samples. Furthermore, for the first time, we evaluated the interaction of NCK1-DT and ASH1L-AS1 with mRNA RGS2. Based on our experimental evaluations, NCK1-DT and ASH1L-AS1 could be considered two potential novel diagnostic biomarkers of MS. RGS2, NCK1-DT, and ASH1L-AS1 as three MS biomarkers have multiple direct and indirect interactions. One of the most important parts of our investigation is the miRNA interaction analysis. Based on our bioinformatics analyses, miR-4638-3p might have a significant suppressor role in the expression level of mRNA RGS2 via direct interaction with the 3’UTR region of this mRNA. Low expression of miR-4638-3p could be one reason for up-regulation of RGS2 in MS patients. Also, there was no previous study about the possible role of miR-4538-3p in MS progression. However, there were two studies about the potential roles of miR-4638-3p in the development of breast cancer. Akshaya et al. 2022 revealed that miR-4638-3p influences the activation of transcription factor-3 induced by transforming growth factor-β1, affecting cell growth, invasion, and programmed cell death in human breast cancer cells ( 61 ). Another study of that research group revealed that miR-4638-3p has a significant role in the bone metastasis of breast cancer cells ( 62 ). Based on our analyses, miR-4525 is one of the potential regulatory non-coding RNAs for the changes in the expression level of RGS2 and ASH1L-AS1. Based on ceRNA theory, when two RNA molecules possess identical microRNA response elements (MREs), they may vie for a common set of miRNAs. Consequently, if the expression of a ceRNA increases, it sequesters more miRNAs (a process known as miRNA sponging), reducing the number of miRNAs available to bind to the mRNA sharing the same MRE. As a result, this leads to the derepression of the specific mRNA ( 63 , 64 ). In this case, RGS2 and lncRNA ASH1L-AS1 share the same MRE for miR-4525, and up-regulation of lncRNA ASH1L-AS1 leads to the high expression of RGS2. So, ASH1L-AS1 might affect the expression level of RGS2 directly and indirectly. Also, ASH1L-AS1 and NCK1-DT have the same indirect interaction via binding affinity to miR-365a-3p and miR-365b-3p with the same mechanism. There were no previous studies about the potential roles of mentioned lncRNAs and miRNAs in MS. However, due to our limitations, we could not validate the direct interactions by experimental approach. So, it is highly recommended that the interaction of NCK1-DT, ASH1L-AS1, miR-4638-3p, and miR-4525 with RGS2 be evaluated using experimental methods (e.g. Luciferase assay or RIP). 5. Conclusion lncRNAs NCK1-DT and ASH1L-AS1 may play a crucial role in modulating the expression of RGS2 mRNA in patients with MS. Observations indicate that RGS2, NCK1-DT, and ASH1L-AS1 are upregulated in MS samples, suggesting their potential utility as diagnostic biomarkers for MS. Furthermore, miR-4525 is involved in regulating the expression levels of RGS2 and ASH1L-AS1 within a ceRNA network. Additionally, miR-365a-3p and miR-365-5p facilitate a ceRNA interaction between NCK1-DT and ASH1L-AS1. This intricate regulatory network influences the “NF-Kappa B signaling pathway”, and any perturbations within this network could disrupt the pathway's normal functioning and elevate the risk of MS progression. Declarations Ethics approval: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences. Consent for publication: Informed consent was obtained from all individual participants included in the study. Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE43591. Conflicts of interest: The authors declare that they have no competing interests. Financial support and sponsorship: Not applicable. Authors’ contribution: Parisa Forouzanfar, Mohammad Hashemian, Mojdeh Mahmoudian, and Melika Khorsandi : Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Parisa Forouzanfar, Mohammad Hashemian, Mojdeh Mahmoudian, and Melika Khorsandi contributed to this study as the first authors. Acknowledgment: None. References Reich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med [Internet]. 2018 Jan 11 [cited 2023 Dec 3];378(2):169–80. Available from: https://pubmed.ncbi.nlm.nih.gov/29320652/ Genc B, Bozan HR, Genc S, Genc K. Stem Cell Therapy for Multiple Sclerosis. Adv Exp Med Biol [Internet]. 2019 [cited 2023 Dec 3];1084:145–74. 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Available from: https://pubmed.ncbi.nlm.nih.gov/21802130/ Additional Declarations The authors declare no competing interests. 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-4184298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285135835,"identity":"9ef19bff-5308-4ef9-b5a4-ed17ac79e3e4","order_by":0,"name":"Parisa Forouzanfar","email":"","orcid":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"","lastName":"Forouzanfar","suffix":""},{"id":285135952,"identity":"df4f549c-0260-4f6a-9b5d-a496ee4671bf","order_by":1,"name":"Mohammad Hashemian","email":"","orcid":"","institution":"Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Hashemian","suffix":""},{"id":285136004,"identity":"2049c693-f2ba-4e86-92ef-72c752d33282","order_by":2,"name":"Mojdeh Mahmoudian","email":"","orcid":"","institution":"Department of Microbiology, Genetics and Immunology, Michigan State University, East Lansing, Michigan, USA","correspondingAuthor":false,"prefix":"","firstName":"Mojdeh","middleName":"","lastName":"Mahmoudian","suffix":""},{"id":285136118,"identity":"9a58ef39-cdd1-431a-b107-32701d57532b","order_by":3,"name":"Melika Khorsandi","email":"","orcid":"","institution":"Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran","correspondingAuthor":false,"prefix":"","firstName":"Melika","middleName":"","lastName":"Khorsandi","suffix":""},{"id":285136395,"identity":"d46f5006-f833-4103-a0c1-368b5828887a","order_by":4,"name":"Mohammad Rezaei","email":"","orcid":"https://orcid.org/0000-0003-3888-5839","institution":"Department of Biology and Biotechnology, University of Pavia, Pavia, Italy","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Rezaei","suffix":""},{"id":285136579,"identity":"db8fce30-4654-4797-97fb-ac3d53f23531","order_by":5,"name":"Mansoureh Azadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3SMWvCQBTA8edyU2JXSyD5BIULBYsU/Cx3HDRLbB07ZDgQ4lLsmiEfwk7F7eAgLilZb+hgKDg5ZJJCIVSDBIdTO3a4//aGH7zHHYDJ9F+zQLjQAwzkMDahC+T2mHT4XwjlewLH5FQ302VZbZ4/g3dnModVJJ+uXj+WK4iG0HWElvRzwq7TfD1apNkYSCYHiXqkHDIGqEv0RBDh2LEczVW4uwVJDMryOSAByNIv1i/KyY9dywA3pJbYK/Idqc8QxZBjc0kaQmOJsQh93onPkTUapJn0F8nDWNBZgH0V+gmdMev0YsGX2kTSu+uxt/J7e4/dIsdVtR263ouetO0fRbQTaf/ABWIymUwmbb8scGMPe1qBDAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2031-4640","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","correspondingAuthor":true,"prefix":"","firstName":"Mansoureh","middleName":"","lastName":"Azadeh","suffix":""}],"badges":[],"createdAt":"2024-03-28 20:02:10","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-4184298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4184298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53890196,"identity":"24273303-c87d-4390-b3f3-4f5e33048da9","added_by":"auto","created_at":"2024-04-01 20:56:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38748,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) illustrating the distinction between normal and patient samples. A) PCA was performed prior to the removal of the \"patient 7\" sample from the microarray dataset. B) PCA following the exclusion of the problematic sample.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/a0f35c60c44b8ded8f3848ec.png"},{"id":53890212,"identity":"2c222789-9fbf-4364-b4d8-5392eb2d664a","added_by":"auto","created_at":"2024-04-01 20:56:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57854,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation heatmap. Evaluation of Pearson correlation between all microarray samples showed the correlation pattern of each group. Hierarchical clustering was performed using the “Pearson” method.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/3c4a46e22f4430cbbba3f424.png"},{"id":53890204,"identity":"b096c774-d924-4570-96c3-e283b54c1fd8","added_by":"auto","created_at":"2024-04-01 20:56:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47864,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot for GSE43591. Up-regulated mRNAs are marked in red, while genes with decreased expression are shown in blue within GSE43591. The plot highlights RGS2 with a black dot to signify its status as an up-regulated mRNA.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/1ab9d4c20e3e9dd6c2955b6f.png"},{"id":53890207,"identity":"853160ce-5b5b-411c-89f9-7d6077d355b2","added_by":"auto","created_at":"2024-04-01 20:56:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103298,"visible":true,"origin":"","legend":"\u003cp\u003eheatmap of top high expressed and down-regulated mRNAs in MS samples. RGS2 is indicated in the plot. The hierarchical clustering of genes was performed using “Pearson” method.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/8229e9aca6b9cb6a0d6e75ee.png"},{"id":53890216,"identity":"465a8021-11b2-4ede-8fd0-32444ff01abd","added_by":"auto","created_at":"2024-04-01 20:56:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":240028,"visible":true,"origin":"","legend":"\u003cp\u003eRNA interaction network revealed novel non-coding interactions of RGS2. Within this interaction framework, miR-4638-3p is capable of diminishing RGS2 expression by directly engaging with RGS2 mRNA's 3’UTR region. Furthermore, lncRNA NCK1-DT directly interacts with RGS2, while lncRNA ASH1L-AS1 is involved in both direct and competing endogenous RNA (ceRNA) mediated interactions with RGS2. Also, lncRNA NCK1-DT has indirect interaction with lncRNA ASH1L-AS1 through binding affinity with miR-365a-3p and miR-365b-3p miRNAs. Yellow nodes are potential regulatory miRNAs, blue color indicates the central RNAs in this study (selected mRNA and lncRNAs), and the red nodes are hub miRNAs in this study.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/c45743b3a0775bac7f7efc05.png"},{"id":53890150,"identity":"48c58186-22a9-4927-8c4b-96a10f286994","added_by":"auto","created_at":"2024-04-01 20:56:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":173771,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of protein interactions conducted with STRING online platform. a) Examination of DEGs identified three interacting protein clusters. The red cluster, regulated by RGS2, plays a pivotal role in the NF-Kappa B signaling pathway's regulation. b) Interaction network of RGS2 protein.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/8f4365dc8b9af1200022e75f.png"},{"id":53890191,"identity":"9294261b-3721-4456-be42-f34d8426238f","added_by":"auto","created_at":"2024-04-01 20:56:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144186,"visible":true,"origin":"","legend":"\u003cp\u003eNF-kappa B signaling pathway. Three proteins from the first cluster of protein interaction analysis (BCL10, TNFAIP3, and TNFSF13B) directly regulate this signaling pathway.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/9da2862ea9b2f7d387d8585d.png"},{"id":53890157,"identity":"5adb9db9-19c9-45eb-954f-cbf3b8922282","added_by":"auto","created_at":"2024-04-01 20:56:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41260,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of qRT-PCR data of RGS2, NCK1-DT, and ASH1L-AS1. Based on this analysis, RGS2, NCK1-DT, and ASH1L-AS1 have significant increases in the expression level in MS samples.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/63ac4ce43e402b3e009eac24.png"},{"id":53890206,"identity":"9f88fb80-d493-4464-b996-e2ae081133b3","added_by":"auto","created_at":"2024-04-01 20:56:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86735,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis revealed that RGS2, NCK1-DT, and ASH1L-AS1 might be the potential diagnostic biomarkers of MS.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/a2d89e6cd589b3500500b198.png"},{"id":53890210,"identity":"b5da5216-86e1-4d8e-a1f0-da29a20f9401","added_by":"auto","created_at":"2024-04-01 20:56:18","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":35592,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation analysis revealed no significant correlation between the expression level of RGS2 with NCK1-DT and ASH1L-AS1 in MS patients.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/8d19061b514d046c35c82467.png"},{"id":53890265,"identity":"9e6e7342-cbe1-4a1d-bbaf-bcc6600a2c5e","added_by":"auto","created_at":"2024-04-01 20:56:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4184298/v1/156b6490-8cc0-4973-b5a0-284bf54901d3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRGS2-related non-coding interaction network modulates the NF-Kappa B signaling pathway in MS patients: a systems biology investigation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe most widespread chronic inflammatory disease affecting the brain and spinal cord is known as Multiple Sclerosis (MS) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This condition is defined by the loss of myelin sheath and neurons caused by the attack of autoreactive T cells in the central nervous system (CNS) as well as Failed endogenous remyelination (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) inducing the development of demyelinated areas that are called grey matter lesions (GMLs) and white matter lesions (WMLs) all over the CNS (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) which ultimately results in nontraumatic neurological disability (Cotsapas et al., 2018). Based on the latest edition of Atlas of MS (3rd edition), approximately 2.8\u0026nbsp;million people are suffering from MS globally (35.9 per 100,000 individuals). Epidemiological investigations reveal that the prevalence and incidence of MS have been rising around the world, and the average age at diagnosis is 32 years. MS is twice as common in women as in men (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the need for more clarity surrounding the precise etiology and pathogenesis of MS, documented evidence reveals that its underlying cause is multifaceted. Contributing factors comprise genetic predisposition, as well as various environmental factors, especially deficient levels of vitamin D, Epstein\u0026ndash;Barr virus (EBV) infection, geographic region, obesity, and smoking (\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The majority of MS patients (around 85%) experience Relapsing-Remitting MS (RRMS), which is marked by episodes of abrupt neurological impairment followed by complete or partial recovery. After 10\u0026ndash;15 years, most RRMS patients develop Secondary Progressive MS (SPMS), which is characterized by ongoing neurological deterioration. In a small percentage of MS patients (about 15%), the disease can be progressive from the onset, which is known as Primary Progressive MS (PPMS) (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImmunopathology of MS is associated with both CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells. Myelin components are targeted by pathogenic T helper 17 (Th17), T helper 1 (Th1), and CD8\u0026thinsp;+\u0026thinsp;autoreactive T cells. Furthermore, local microglia and macrophages become activated in demyelinated lesions. B cells are also involved in the immunopathogenesis of the disease. CD20\u0026thinsp;+\u0026thinsp;B cells are the most frequently seen in the early stages of the disease, while plasma blasts and plasma cells are the most prominent in the later stages. B cell engagement relates to not only antibody production, but also antigen presentation to T cells and modulation of T cells and myeloid cells function as a result of cytokine secretion (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are vital contributors to intricate biological processes, and their presence in body fluids, coupled with their specific distribution in tissues, positions these biomolecules as potentially valuable candidates for diagnosing MS (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Despite not being translated into proteins, these elements play essential roles in various physiological processes, including cellular differentiation, stress response, aging, cell growth, programmed cell death, and the regulation of gene transcription (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Research has revealed that lncRNAs could play a pivotal role in autoimmune diseases by influencing immune cells' activation, differentiation, and imbalanced expression (including T cells, B cells, macrophages, and NK cells). These effects have been observed in autoimmune conditions like psoriasis, rheumatoid arthritis, and systemic lupus erythematosus (SLE) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It is noteworthy that certain lncRNAs have been identified as being dysregulated in peripheral blood mononuclear cells among individuals with MS, indicating their potential involvement in the pathogenesis of the disease (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our previous studies mentioned the potential biological roles of different lncRNAs in immune-related diseases, including different cancer types, including breast cancer (\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), gastric cancer (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and MS (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Especially about MS, previous investigations mentioned novel regulatory lncRNAs with dysregulation in MS patients. For example, Hosseini et al. identified elevated expression levels of AC007278.2 and IFNG-AS1-001 lncRNAs in the peripheral blood mononuclear cells (PBMC) of 50 individuals with RRMS. Specifically, these increased expressions were observed in patients during the relapsing phase, half of whom were undergoing treatment with interferon beta. In contrast, the lncRNA IFNG-AS1-003 demonstrated higher levels in MS patients during the remitting phase compared to those in the relapsing phase. These findings were in comparison to a control group of healthy individuals (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study aims to identify new diagnostic biomarkers for MS by analyzing high-throughput data. We employed a systems biology method to analyze non-coding interactions, aiming to discover previously unidentified lncRNAs that might control the expression of key mRNAs and show increased levels in individuals with MS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Microarray data analysis\u003c/h2\u003e \u003cp\u003eTo find novel differentially expressed genes (DEGs) in MS patients, raw data of GSE43591 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) was downloaded, normalized (using the affy (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) package), and analyzed using the limma package in the R programming language environment. Limma employs several statistical concepts effectively tailored for extensive expression analyses. It processes a matrix of expression data, with each row corresponding to a gene or another genomic element pertinent to the study, and each column representing an RNA sample. Limma fits a linear model to the data in each row, leveraging the versatility of linear models for multiple analytical purposes (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In this analysis, the expression level of mRNAs in 10 MS samples was analyzed and compared with 10 normal control samples using the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). For the quality control processes of microarray analysis, the Principal Component Analysis (PCA) and correlation analysis between the samples were performed and related plots were drawn using ggplot2 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and pheatmap, respectively. Adjusted P. Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as the significance level of this study. Using the mentioned significance criteria, |logFC| \u0026gt; 1 was considered as the threshold of DEG selection. Using ggplot2 and pheatmap packages, the plots of microarray analysis were illustrated in R Studio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Interaction Network and Signaling Pathway Analyses\u003c/h2\u003e \u003cp\u003eThe interactions between miRNAs and mRNAs were analyzed using the miRWalk tool (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In this study, miRNAs were selected with the following criteria: location of interaction: 3\u0026rsquo;UTR region of mRNA (seed region), score: 1, and ordered based on binding energy (lower to higher). lncRNA-miRNA interaction analysis was conducted by lncBase (Version: 3) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). All microRNAs in the lncRNA-miRNA interaction analysis were visualized. Direct interactions of mRNA with lncRNAs were conducted using the lncRRIsearch database (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Visualization of the non-coding interaction network was performed using Cytoscape (Version: 3.10.1) (Otasek et al., 2019; Shannon et al., 2003).The analysis of protein-protein interactions was carried out using the STRING database. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The interactions of DEGs in microarray analysis were visualized and clustered using the K-mean clustering method, and based on this clustering the DEGs were divided into three different clusters. Using Enrichr, related signaling pathways to the protein interaction network were demonstrated (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) for each cluster in the protein interaction network. Using the KEGG (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) online database, the crucial signaling pathway was visualized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Collection\u003c/h2\u003e \u003cp\u003eIn this case-control study, 40 blood samples were collected from individuals diagnosed with MS and healthy controls (20 samples from MS patients and 20 samples from healthy individuals), following the revised McDonald criteria (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The healthy individuals served as the control group and were selected based on the absence of any inflammatory or autoimmune conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRNA extraction, cDNA synthesis, and Real-Time PCR experiment\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Total RNA from blood samples was isolated using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) as per the guidelines provided by the manufacturer. The quality of the isolated RNA was assessed using a nanodrop instrument. Subsequently, cDNA was synthesized from the RNA through reverse transcription using the Roje kit (Iran/Tehran). Both the RNA samples and the resulting cDNAs were preserved at -80\u0026deg;C. Real-time PCR was conducted using SYBR green (qPCRBIO SyGreen Mix Lo-ROX, England) in a Magnetic Induction Cycler (MIC) device. Gene expression levels were normalized to the GAPDH gene, serving as an internal standard. Primers specific to each gene were designed with the aid of oligo software (version 7), and the sequences for these primers designated for Real-Time PCR are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences of selected RNAs for qRT-PCR experiment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward/Reverse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer Sequence (5'\u0026rarr;3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGCTACAAGTGGCTGCTTTAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATGAGGCTCTGTGGTGATTTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eNCK1-DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTGAGTGGATCAGGGACTAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGACCTGGAACCAGCATCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eASH1L-AS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGGTTGACCTGAGCCTACTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATACCCGTGTCGGTCCATTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACAGGGTGGTGGACCTCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGGGGTCTACATGGCAACTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism (Version 9.0.0) was used for creating graphs and analyzing data from real-time PCR experiments. The normal distribution of the expression data was evaluated using the Kolmogorov-Smirnov test. Differences in the expression levels of lncRNAs ASH1L-AS1 and NCK1-DT between control and MS samples were analyzed using both paired and unpaired t-tests. The construction of the receiver operating characteristic (ROC) curves and the analysis were performed using GraphPad Prism. The specificity and sensitivity of Regulator of G-protein Signaling 2 (RGS2), ASH1L-AS1, and NCK1-DT as diagnostic markers for Multiple Sclerosis were assessed through ROC curves and the area under the curve (AUC) values. AUC values less than 0.7 were deemed not suitable for biomarkers, values between 0.7 and 0.8 were considered acceptable, values between 0.8 and 0.9 were seen as good, and values greater than 0.9 were classified as excellent diagnostic biomarkers. The quantitative expression of lncRNAs between the MS and healthy groups was analyzed using the Mann-Whitney U test. Differential gene expression analysis in microarray data was performed using R Studio (Version 4.3.2). Clinicopathological data frequency tables were created with IBM SPSS Statistics (Version 27.0.1), and common DEGs were identified using Venny online software (v.2.1.0). The study set the significance threshold at an adjusted P-value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Microarray Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Evaluation of the Quality of Microarray Samples\u003c/h2\u003e \u003cp\u003eMicroarray analysis of GSE43591 was conducted to identify new dysregulated mRNAs in MS patients. DEGs were determined using the limma package, selecting genes with |logFC| \u0026gt; 1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. PCA indicated that the sample labeled \"patient 7\" was of suboptimal quality and was subsequently excluded (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A correlation heatmap demonstrated distinct clustering of control and patient samples (as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings collectively affirm the high quality of the microarray samples in GSE43591.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. RGS2 has the highest expression in the high-throughput dataset\u003c/h2\u003e \u003cp\u003eThrough 35214 mRNAs analyzed by R Studio, 32 mRNAs were up-regulated, and 62 mRNAs were low expressed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among all DEGs, RGS2 has the most increase in the expression level in MS samples (logFC: 1.7667, adj.P. Value: 0.0079). The volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows the distribution of up and down-regulated mRNAs in MS samples. Also, the top 20 up and down-regulated mRNAs are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 up and down-regulated mRNAs in GSE43591.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadj.P.Val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene. Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup/down\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e202388_at\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.766650062\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007898361\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRGS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e221731_x_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51011654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0142012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e201739_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.509796385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003032329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSGK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e217739_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.490552959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018842419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAMPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e216834_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.429901587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019132056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRGS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e223502_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38741612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018512132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNFSF13B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e238890_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.275610591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008817609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRWD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e209795_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.238893519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004333034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e202643_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.227120683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002205417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1557257_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.215805974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01159354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e232686_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.366588245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017672348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIGLEC17P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37145_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.393112628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047448739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e230464_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.465047902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024049569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS1PR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e205495_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.508453998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042055898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e215894_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.511393342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001527875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTGDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e212070_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.664695451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035022517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPR56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e235811_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.732083026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002659438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1564139_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.796156809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018842419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA2M-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e205898_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.883483227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049875794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCX3CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e210387_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.92938721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047160596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIST1H2BG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown-regulated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Bioinformatics Analyses\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. lncRNAs NCK1-DT and ASH1L-AS1 regulate RGS2, directly and indirectly\u003c/h2\u003e \u003cp\u003eThe analysis of lncRNA interactions suggested that two newly identified lncRNAs, NCK1-DT and ASH1L-AS1, could potentially influence RGS2 expression through direct interactions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, the examination of miRNA interactions identified key miRNAs that regulate RGS2 and two associated lncRNAs. According to the analysis conducted with miRWalk, miR-4638-3p appears to significantly suppress the expression of RGS2 mRNA by directly interacting with its 3\u0026rsquo;UTR region (energy: -26.8 kcal/mol). Furthermore, based on miRNA interaction analysis of the lncBase database, miR-4525 regulates RGS2 and lncRNA ASH1L-AS1, and this interaction axis shows a competitive endogenous RNA (ceRNA) relation between RGS2 and ASH1L-AS1. Also, miR-365a-3p and miR-365b-3p have direct interaction with lncRNAs NCK1-DT and ASH1L-AS1. So, the two lncRNAs also have ceRNA interaction and could regulate the expression level of each one indirectly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. RGS2 protein interacts with three clusters of DEGs in \u0026ldquo;NF-Kappa B Signaling\u0026rdquo;\u003c/h2\u003e \u003cp\u003eFor evaluation of possible interactions between the DEGs in this study, protein interaction analysis was performed using STRING (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The K-mean clustering method was performed for the clustering of DEGs, based on interaction patterns. Interacted proteins were divided into three clusters, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Using the Enrichr database, related pathways of each cluster were evaluated. Based on KEGG pathway analysis using Enrichr, the first cluster of proteins (in which RGS2 is located) regulates the \u0026ldquo;NF-kappa B signaling pathway\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Also, the second cluster modulates the \u0026ldquo;Sphingolipid signaling pathway,\u0026rdquo; and the third cluster (H2BC5 protein) regulates the \u0026ldquo;Systemic lupus erythematosus signaling pathway.\u0026rdquo; Also, an RGS2-specific interaction analysis was performed to find proteins related to RGS2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProtein interaction of DEGs. The proteins in this interaction network are divided into three clusters (using the K-mean clustering method), and each cluster regulates a specific signaling pathway.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecluster color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003egene count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePathway (KEGG)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eNF-kappa B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRGS1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRGS2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSGK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNFSF13B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADGRG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSphingolipid signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCX3CR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNLY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS1PR5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH2BC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. qRT-PCR Experiment\u003c/h2\u003e \u003cp\u003eTo confirm the findings from the microarray data analysis and assess potential alterations in the expression levels of the two chosen lncRNAs, qRT-PCR experiments were conducted. Results from these experiments indicated that RGS2 (logFC: 4.547, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), NCK1-DT (logFC: 2.155, p-value: 0.0132), and ASH1L-AS1 (logFC: 3.345, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) exhibited notable upregulation in MS samples relative to the controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). ROC analysis demonstrated that RGS2 (AUC: 0.9975, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and ASH1L-AS1 (AUC: 0.9225, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) could serve as excellent diagnostic markers for MS, while NCK1-DT presents as an acceptable marker (AUC: 0.7275, p-value: 0.0138, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). A Pearson correlation analysis was also carried out to explore potential correlations between the \u0026ndash; ΔΔCt values obtained from the qRT-PCR experiments. However, no significant correlation was observed between the expression levels of RGS2 and the two lncRNAs under investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrior research has shed light on RGS2's involvement in neurodegenerative diseases and key signaling pathways. Dusonchet and colleagues demonstrated that RGS2 affects both GTPase and kinase activities of LRRK2, significantly influencing the length of neuronal processes. This role of RGS2 is vital for protecting against damage from the prevalent LRRK2 mutation, G2019S, suggesting a novel action mechanism for GAP proteins, distinct from their known GTPase activity modulation. Such findings position RGS2 as a promising candidate for therapeutic intervention in Parkinson's disease (PD), particularly for conditions related to LRRK2 mutations (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). As a member of the RGS protein family, RGS2 interacts with Gα components of heterotrimeric G proteins, specifically targeting Gαq and Gαi subunits to slow their GTP to GDP hydrolysis, thereby reducing signal transduction from G-protein-coupled receptors (GPCRs) and playing a crucial role in synaptic plasticity (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Moreover, RGS2 directly interacts with adenylyl cyclases, decreasing cyclic AMP (cAMP) production, and influencing GPCR-mediated Akt signaling pathways, highlighting its regulatory impact (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Increased RGS2 expression intensifies neurodegeneration caused by mutant Huntingtin (mHtt), with preliminary data suggesting a link to the Erk/MAP kinase signaling pathway. However, the exact mechanisms remain to be fully elucidated (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This study further supports the hypothesis that RGS2 upregulation may elevate the risk of neurodegenerative diseases, reinforcing its potential as a therapeutic target (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our research, we found that RGS2 and its interacting network play a significant role in modulating the NF-κB signaling pathway. The NF-κB family of transcription factors is crucial for regulating genes involved in vital cellular functions such as survival, cell death, inflammation, growth, and differentiation (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Within the nervous system, NF-κB is uniquely active at high levels in neurons, highlighting its essential role in overseeing specific functions in the central nervous system (CNS). It is a critical regulator of neuronal architecture and profoundly influences neuronal plasticity, which in turn impacts learning, memory, and behavior. Additionally, NF-κB provides neurons with protection against damage from excitotoxicity, oxidative stress, and ischemia. It also contributes to the inflammatory response and cell death in cases of brain injury and stroke. In contrast, glial cells show no inherent NF-κB activity under unstressed conditions, suggesting that NF-κB activation in these cells is mainly triggered by stress or pathological conditions (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur research findings demonstrate that RGS2 interacts with the proteins BCL10, TNFAIP3, and TNFSF13B, which are key regulators of the NF-κB signaling pathway. The activation of NF-κB, a crucial process following T-cell receptor (TCR) stimulation, depends on assembling the CARMA1\u0026ndash;Bcl-10\u0026ndash;MALT1 (CBM) complex at the cell membrane, a step facilitated by protein kinase Cθ (PKCθ) (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This assembly is essential for NF-κB to be subsequently activated by the IKK complex. The critical nature of the CBM complex in this pathway is highlighted by studies on mice lacking CARMA1, Bcl-10, or MALT1, which show reduced NF-κB activation, lower Th1 cytokine production, and decreased T-cell proliferation following TCR activation (\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Additionally, genome-wide association studies (GWASs) have identified new genetic loci linked to MS susceptibility in the TNFRSF1A and MALT1 genes, encoding TNF-R1 and MALT1, respectively. These studies have also confirmed TNFAIP3 as a risk gene for MS, underscoring its significance in MS pathogenesis.\u003c/p\u003e \u003cp\u003eBased on previous studies, the NCK1-DT is an important regulatory factor for various cancer types, including prostate cancer, lung cancer, and gastric cancer (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Also, Xie et al. revealed that ASH1L-AS1 has a significant regulatory role in gastric cancer development via modulation of the \u0026ldquo;RAS signaling pathway\u0026rdquo; (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). However, there was no previous study about the possible roles of NCK1-DT and ASH1L-AS1 in MS development. In this study, for the first time, we evaluated the expression level of two mentioned lncRNAs in MS patients and revealed the up-regulation of NCK1-DT and ASH1L-AS1 in MS samples. Furthermore, for the first time, we evaluated the interaction of NCK1-DT and ASH1L-AS1 with mRNA RGS2. Based on our experimental evaluations, NCK1-DT and ASH1L-AS1 could be considered two potential novel diagnostic biomarkers of MS. RGS2, NCK1-DT, and ASH1L-AS1 as three MS biomarkers have multiple direct and indirect interactions.\u003c/p\u003e \u003cp\u003eOne of the most important parts of our investigation is the miRNA interaction analysis. Based on our bioinformatics analyses, miR-4638-3p might have a significant suppressor role in the expression level of mRNA RGS2 via direct interaction with the 3\u0026rsquo;UTR region of this mRNA. Low expression of miR-4638-3p could be one reason for up-regulation of RGS2 in MS patients. Also, there was no previous study about the possible role of miR-4538-3p in MS progression. However, there were two studies about the potential roles of miR-4638-3p in the development of breast cancer. Akshaya et al. 2022 revealed that miR-4638-3p influences the activation of transcription factor-3 induced by transforming growth factor-β1, affecting cell growth, invasion, and programmed cell death in human breast cancer cells (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Another study of that research group revealed that miR-4638-3p has a significant role in the bone metastasis of breast cancer cells (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Based on our analyses, miR-4525 is one of the potential regulatory non-coding RNAs for the changes in the expression level of RGS2 and ASH1L-AS1. Based on ceRNA theory, when two RNA molecules possess identical microRNA response elements (MREs), they may vie for a common set of miRNAs. Consequently, if the expression of a ceRNA increases, it sequesters more miRNAs (a process known as miRNA sponging), reducing the number of miRNAs available to bind to the mRNA sharing the same MRE. As a result, this leads to the derepression of the specific mRNA (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In this case, RGS2 and lncRNA ASH1L-AS1 share the same MRE for miR-4525, and up-regulation of lncRNA ASH1L-AS1 leads to the high expression of RGS2. So, ASH1L-AS1 might affect the expression level of RGS2 directly and indirectly. Also, ASH1L-AS1 and NCK1-DT have the same indirect interaction via binding affinity to miR-365a-3p and miR-365b-3p with the same mechanism. There were no previous studies about the potential roles of mentioned lncRNAs and miRNAs in MS. However, due to our limitations, we could not validate the direct interactions by experimental approach. So, it is highly recommended that the interaction of NCK1-DT, ASH1L-AS1, miR-4638-3p, and miR-4525 with RGS2 be evaluated using experimental methods (e.g. Luciferase assay or RIP).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003elncRNAs NCK1-DT and ASH1L-AS1 may play a crucial role in modulating the expression of RGS2 mRNA in patients with MS. Observations indicate that RGS2, NCK1-DT, and ASH1L-AS1 are upregulated in MS samples, suggesting their potential utility as diagnostic biomarkers for MS. Furthermore, miR-4525 is involved in regulating the expression levels of RGS2 and ASH1L-AS1 within a ceRNA network. Additionally, miR-365a-3p and miR-365-5p facilitate a ceRNA interaction between NCK1-DT and ASH1L-AS1. This intricate regulatory network influences the \u0026ldquo;NF-Kappa B signaling pathway\u0026rdquo;, and any perturbations within this network could disrupt the pathway's normal functioning and elevate the risk of MS progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent for publication:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated or analyzed during the current study are available in the GEO repository,\u0026nbsp;GSE43591.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support and sponsorship:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParisa Forouzanfar, Mohammad Hashemian, Mojdeh Mahmoudian, and Melika Khorsandi\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Software, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization; \u003cstrong\u003eMohammad Rezaei:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Supervision; \u003cstrong\u003eMansoureh Azadeh:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Parisa Forouzanfar, Mohammad Hashemian, Mojdeh Mahmoudian, and Melika Khorsandi contributed to this study as the first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e None.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eReich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med [Internet]. 2018 Jan 11 [cited 2023 Dec 3];378(2):169\u0026ndash;80. Available from: https://pubmed.ncbi.nlm.nih.gov/29320652/\u003c/li\u003e\n \u003cli\u003eGenc B, Bozan HR, Genc S, Genc K. Stem Cell Therapy for Multiple Sclerosis. Adv Exp Med Biol [Internet]. 2019 [cited 2023 Dec 3];1084:145\u0026ndash;74. Available from: https://pubmed.ncbi.nlm.nih.gov/30039439/\u003c/li\u003e\n \u003cli\u003eCotsapas C, Mitrovic M, Hafler D. Multiple sclerosis. Handb Clin Neurol [Internet]. 2018 Jan 1 [cited 2023 Dec 3];148:723\u0026ndash;30. 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Available from: https://pubmed.ncbi.nlm.nih.gov/21802130/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zist Fanavari Novin Biotechnology Institute, Isfahan, Iran","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"long non-coding RNA, Multiple Sclerosis, Bioinformatics, Microarray Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4184298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4184298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMultiple Sclerosis (MS) is recognized as the most prevalent chronic inflammatory condition that targets the brain and spinal cord. According to the third edition of the Atlas of MS, around 2.8 million individuals worldwide are affected by Multiple Sclerosis, equating to a prevalence of 35.9 cases per 100,000 people. In this study, we evaluated the expression levels of potential biomarkers in a high-throughput MS dataset to find novel highly dysregulated RNAs in MS patients. Furthermore, a novel RNA regulatory network has been visualized to find new non-coding interaction patterns in the MS-related signaling pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing R Studio, high-throughput gene expression MS datasets were analyzed to find highly dysregulated mRNAs in MS patients. miRNA, lncRNA, and protein interaction analyses were conducted by miRWalk and lncRRIsearch databases. Using visualized interaction networks, pathway enrichment analysis was performed using Enrichr and KEGG. qRT-PCR experiment was performed for the validation of gene expression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eRGS2 was found to be significantly upregulated in both microarray (logFC: 1.7667, adj.P. Value: 0.0079) and qRT-PCR analyses (logFC: 4.547, p-value \u0026lt; 0.0001). Similarly, the lncRNAs NCK1-DT (logFC: 2.155, p-value: 0.0132) and ASH1L-AS1 (logFC: 3.345, p-value \u0026lt; 0.0001) exhibited elevated expression in MS samples, suggesting a regulatory impact on RGS2 expression levels. The marked changes in the expression of RGS2, NCK1-DT, and ASH1L-AS1 in MS patients compared to normal samples position them as promising diagnostic biomarkers. Additionally, RGS2 and its associated proteins have been implicated in modulating the NF-Kappa B signaling pathway. MiR-4638-3p was identified to directly downregulate RGS2 expression, while miR-4525 influences the expression of RGS2 and ASH1L-AS1 within a competing endogenous RNA (ceRNA) network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eNCK1-DT and ASH1L-AS1 are the two novel diagnostic biomarkers of MS. Mentioned lncRNAs might affect the normal regulatory mechanisms of “NF-Kappa B signaling pathway” through direct and indirect interaction with mRNA RGS2.\u003c/p\u003e","manuscriptTitle":"RGS2-related non-coding interaction network modulates the NF-Kappa B signaling pathway in MS patients: a systems biology investigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 20:53:25","doi":"10.21203/rs.3.rs-4184298/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":"f6f54b9f-9278-4bf6-9873-29af9e229dae","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30020794,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2024-04-01T20:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 20:53:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4184298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4184298","identity":"rs-4184298","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}