Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis

Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis | 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 Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis Mehrsa Edalat, Nasim Babak Nezhad, Mansoore Hosseini-Koupaei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6512879/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 Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased. MS is a disease that destroys the myelin sheath of nerve cells in the central nervous system. In the present study, microarray technology and bioinformatics tools were used to identify genes and their interaction pathways to investigate common molecular mechanisms. Additionally, on the basis of the results of this analysis, drug predictions for the treatment of MS were made. Microarray data from the NCBI database, specifically from the GEO section related to GSE41890, containing information on gene expression in 68 samples, were extracted. The two groups, normal and treatment, were subsequently compared. The R programming language was used to analyze the differentially expressed genes (DEGs), and the desired molecular network was constructed. The protein‒protein interaction (PPI) network was created via STRING, and PPI network module analysis was performed via Cytoscape. To investigate protein‒drug interactions, NetworkAnalyst was used. Finally, docking operations were performed via PyRx software. A total of 1190 DEGs, which were involved mainly in cell immunity, the cell cycle, cell proliferation, and signal transduction, were identified. The PPI network contained 67 nodes and 629 interactions. Three protein targets and fifty-one drug candidates were identified; specifically, approximately 11 drugs were linked to KIF11 , 33 drugs were linked to CCNA2 , and 7 drugs were linked to CDK1 . A total of 99443535, 5005498, and 4566 compounds were generally connected to KIF11 , CDK1 , and CCNA2 , respectively. multiple sclerosis microarray gene protein‒protein interaction docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide since 1990. The symptoms caused by MS can lead to significant disability and pain for patients [ 1 ]. This chronic disease has a sudden and unpredictable onset, causing various stresses across different dimensions of patients' lives. Although the main cause of this disease is not fully understood, its mechanism involves damage to the body's immune system or disruption of the cells that produce the myelin sheath. Several factors, including a history of infectious diseases, immune system defects, stress, and environmental influences, can contribute to the onset of the disease [ 2 ]. MS is believed to arise from a combination of environmental factors, such as diet, pollution, and genetics [ 3 ]. MS is classified into four forms on the basis of the pattern of occurrence and progression of the disease: Relapsing-remitting MS (RRMS): RRMS is the most common subtype, accounting for approximately 87% of cases. It is characterized by unpredictable acute attacks followed by periods of remission [ 4 ]. During RRMS, inflammatory attacks occur on myelin and nerve fibers, leading to symptoms such as visual disturbances, tingling and numbness, periodic fatigue, and disorders of the intestinal and urinary systems, as well as spasticity and impaired learning and memory. The results indicate that the diseased condition of RRMS significantly influences the small nucleolar RNAs – miRNAs network in peripheral blood leukocytes [ 5 ]. Primary progressive MS (PPMS): Patients with PPMS typically have fewer brain lesions. Symptoms may include difficulty walking, weakness, stiffness, and balance issues. Secondary progressive MS (SPMS): Approximately 65% of patients with RRMS subsequently develop SPMS, which is considered the second phase of the disease. Many patients experience increased weakness, bowel and urinary disorders, fatigue, stiffness, and mental health issues. Progressive relapsing MS (PRMS): PRMS is the rarest form, occurring in approximately 5% of patients. It is associated with symptoms such as eye pain and diplopia, along with dysfunctions in the sexual, bowel, and urinary systems, as well as dizziness and depression. MS is generally diagnosed between the ages of 20 and 40, although less than 1% of cases can occur in childhood, and approximately 2–10% can arise after the age of 50 years [ 6 ]. Inflammation of white and gray matter tissues in the central nervous system due to the infiltration of immune cells and their cytokines is the primary cause of damage in MS. Studies have shown that T helper cells (CD4 + T cells) play crucial roles in the initiation and progression of this disease [ 7 , 8 ]. B lymphocytes and their cytokines are also significant factors in the pathogenesis of MS, and CD8 + T cells (cytotoxic T cells) have been found in MS lesions [ 9 ]. Environmental factors, including exposure to viral and bacterial agents such as Epstein–Barr virus (EBV), human herpesvirus type 6, and Mycoplasma pneumoniae, along with smoking, vitamin deficiencies, diet, and exposure to ultraviolet radiation, are associated with the incidence of MS [ 3 ]. Epigenetics refers to hereditary changes outside the DNA sequence that affect gene expression. DNA methylation is the most well characterized major epigenetic process, and aberrant methylation in gene regulatory regions may underlie the mechanisms involved in the onset of MS. Several microRNAs (miRNAs) have been found to be upregulated in various tissues of MS patients, potentially altering immune responses and contributing to disease onset [ 10 ]. Studies have identified a group of genes within the human leukocyte antigen ( HLA ) that are associated with an increased risk of MS. The Human Leukocyte Antigen locus includes six classical genes (HLA-A, -B, -C, -DP, -DQ, and -DR) that are crucial in triggering immune responses against pathogens, along with three non-classical genes (HLA-E, -F, and -G) that interact with Natural Killer cells to control virus-infected and cancerous cells. Susceptible genes in this region include HLA-DR2+ , HLA-DQ6 , DQA 0102 , DQB1 0602 , HLA-DRB1 , DR15 , DRB11501 , and DRB11503 . Additionally, the alpha receptors IL-7 and IL-2 are associated with MS susceptibility, whereas HLA-C554 and HLA-DRB1*11 appear to have protective effects [ 11 – 13 ]. Today, bioinformatics is routinely employed in biomedical research to identify pathways and genes for potential prognostic and diagnostic biomarkers via high-throughput microarray and RNA-seq data. The rapid advancements in bioinformatics and computational biology offer significant potential for uncovering new insights into disease mechanisms and identifying possible therapeutic targets [ 14 ]. Importantly, candidate genes can serve as therapeutic targets in the treatment of MS and other complex diseases. Therefore, in the present study, we utilized microarray technology and bioinformatics tools to identify genes and their interaction pathways to uncover common molecular mechanisms. Additionally, on the basis of the results of this analysis, drug predictions for the treatment of MS can also be made. Genome-wide association studies (GWASs) offer an approach in genetics that tests many genetic variants in the genomes of multiple individuals to identify genotype–phenotype relationships, revolutionizing the field of disease genetics. Specifically, GWAS compares allele frequencies at various positions in the genome between healthy and diseased individuals, with significant differences indicating disease associations. GWAS in MS has shown that a substantial portion of the heritability of the disease can be attributed to the genetic region of the major histocompatibility complex (MHC) [ 15 ]. An X chromosome variant, 200 autosomal susceptibility variants outside the MHC, and 32 variants within the developed MHC that are independently involved in the pathogenesis of MS have been identified [ 16 ]. Identifying the risk genes associated with MS remains a key focus of research, as the actual risk genes may be located far from the susceptibility variants. Therefore, it is essential to prioritize the genes and functions associated with the genetic variants highlighted by the MS GWAS [ 17 ]. For this study, microarray data were extracted from the NCBI database, specifically from the GEO section. Two groups, normal (healthy) and treatment (MS), were compared. The R programming language was employed to analyze the data. Using R software, gene expression levels in these collections were analyzed to distinguish genes with distinct expression profiles between diseased and healthy samples in an in silico environment. In this research, the index genes were first determined via the GEOquery package in R. Subsequently, the desired molecular network was constructed, effective drugs were designed, and their effects were evaluated via bioinformatics software [ 18 ]. Materials and methods The study utilized microarray data sourced from the National Center for Biotechnology Information (NCBI) database, specifically from the Gene Expression Omnibus (GEO). The dataset selected for analysis was GSE41890, which comprises gene expression information from 68 samples: 24 samples related to healthy controls and 44 samples from patients with MS (22 samples in recovery and 22 samples in relapse). To carry out this study, the analysis was conducted in two parts: first, comparing the relapsed or unmedicated samples with the recovering or medicated samples (MS.Treat vs. MS.UnTreat), and second, comparing the medicated samples with the control samples (MS.UnTreat vs. Control). This study was conducted in 2013. Initially, the raw data related to the GSE41890 microarray dataset were preprocessed, which included formatting and removing missing values. All analyses were performed via the R programming language, specifically version 4.3.2. Target finding for genes and identification of DEGs To identify common genes, target genes were selected via the website https://bioinformatics.psb.ugent.be/webtools/Venn/ . According to the volcano plot, genes with p-values < 0.05 and − 0.1 < Log2FC < 0.1 were uploaded to the site, resulting in a Venn diagram that revealed 1,190 differentially expressed genes (DEGs) from these analyses. Protein‒protein interaction (PPI) network To elucidate the interactions between the identified proteins, a protein–protein interaction network was constructed via the STRING database. This network was visualized via Cytoscape, a powerful tool for network analysis. The PPI network helps identify key proteins that may serve as hubs in the molecular network, potentially influencing the progression of MS. Through the Cytohubba and MCC plugins, 10 hub genes involved in MS were identified, resulting in a network with 67 nodes and 629 interactions (edges). To check pathways and ontologies, the 1190 common genes were loaded into FunRich software, yielding four diagrams and four tables related to biological pathways, cellular components, molecular functions, and biological processes. Drug interactions and pharmacokinetics The methodology also included an investigation of protein‒drug interactions. The NetworkAnalyst tool was utilized to explore potential drug candidates that could interact with the identified proteins. The pharmacokinetic properties of these compounds were assessed via the SwissADME database, which evaluates the absorption, distribution, metabolism, and excretion (ADME) characteristics of the drugs. Molecular docking The three-dimensional structures of the KIF11 , CCNA2 , and CDK1 proteins, determined by X-ray diffraction, were downloaded from the www.rcsb.org database in PDB format. Docking operations were performed via PyRx software. In PyRx, the dimensions of the grid box were selected on the basis of the amino acids involved in binding, and relevant literature was used to verify the binding of standard drugs with the proteins [ 19 – 21 ]. Results Differentially expressed genes (DEGs) identified via MS The GSE41890 dataset, obtained from the GEO database, included 44 MS samples and 24 control samples. A total of 1190 DEGs were identified from this dataset. The results are presented as volcano plots in Fig. 1 . Additionally, the DEGs were analyzed via a Venn diagram. As shown in Fig. 2 , 1190 DEGs were found in the dataset. DEG enrichment analysis Using FunRich software, we investigated the pathways related to the identified differentially expressed genes (DEGs). Major changes in pathways associated with the cytoskeleton, homeostasis, and receptor activity and a significant alteration in the immune response were observed. A diagram of the biological pathways and gene ontologies is shown in Figure S1. A list of signaling pathways, along with their corresponding P values, is provided in Table 1 . Table 1 Signaling pathways and associated information obtained from FunRich software. Categories No. of genes in the dataset Description P-Value Biological Process 60 Immune response 1/15E-05 276 Signal transduction 0/000426 3 Regulation of enzyme activity 0/00185 122 Energy pathways 0/003117 254 Cell communication 0/003243 4 Inflammatory response 0/007381 Cellular Component 68 Cytoskeleton 1/01E-12 298 Plasma membrane 7/45E-10 193 Exosomes 1/56E-09 439 Cytoplasm 1/55E-08 156 Lysosome 2/45E-08 8 Platelet alpha granule membrane 3/39E-08 Molecular Function 43 Receptor activity 8/25E-06 28 GTPase activity 0/000113 27 Cytoskeletal protein binding 0/000201 35 Receptor signaling complex scaffold activity 0/000338 10 Transmembrane receptor activity 0/000777 18 GTPase activator activity 0/001972 Biological Pathway 65 Hemostasis 1/17E-11 20 Cell surface interactions at the vascular wall 9/99E-08 28 Platelet activation, signaling and aggregation 4/48E-07 10 Platelet Aggregation (Plug Formation) 2/11E-05 132 Integrin family cell surface interactions 0/000153 128 Beta1 integrin cell surface interactions 0/00033 PPI network and module Analysis We identified protein interactions among the overlapping differentially expressed genes (DEGs) via the STRING online database. Cytoscape was used to construct a protein‒protein interaction (PPI) network consisting of 67 nodes and 629 edges (Fig. 3 ). The top ten hub genes were determined on the basis of betweenness centrality, node degree, and the MCC method. These hub genes included KIF11 , CCNA2 , CDK1 , BUB1 , CDCA8 , DLGAP5 , TTK , KIF20A , TPX2 , and SPAG5 . The full names of the proteins and their corresponding PDB codes are provided in Table S1. The interaction network of genes based on the MCC, generated through Cytohubba, is shown in Figure S2. A comparison of hub gene expression between the drug-treated group and the MS group is shown in Table S2. The results of the bioinformatic analysis of MS expression data reveal significant findings that could pave the way for new therapeutic strategies. This study identified three critical protein targets, KIF11 , CCNA2 , and CDK1 , that are involved in various cellular processes relevant to MS pathology. A total of 11 drugs for KIF11 , 33 drugs for CCNA2 , and 7 drugs for CDK1 were extracted from the research database. Pharmacokinetic properties The pharmacokinetic properties of the identified compounds were assessed via the SwissADME database. Among the fifty-one candidates, forty-nine compounds emerged as viable candidates, demonstrating suitable pharmacokinetic profiles. The list of drugs that complied with Lipinski's rule from the SwissADME database is provided in Table S3. This assessment is crucial because it provides insights into the absorption, distribution, metabolism, and excretion (ADME) characteristics of drugs, which are essential for their effectiveness and safety in clinical applications. Docking results of the top drugs against KIF11 The docking results of the top five compounds against the KIF11 protein are shown in Table S4, with filanesib considered the control drug [ 22 ]. As indicated in the table, the interactions of the compounds with the protein primarily occur through hydrophobic interactions, which are observed in most of the compounds. The polar amino acids involved in these interactions include arginine 355, tyrosine 104, and asparagine 298. The acidic amino acids involved in the interactions included glutamic acid 304. Additionally, hydrogen bonds were also observed. Compound 99443535, with a binding energy of -9.5 kJ/mol, demonstrated the most effective binding with KIF11 . The main interactions between this compound and the enzyme included hydrophobic interactions with the amino acids Ala356, Thr300, Ile299, Gly296, Ile332, Ser269, Leu292, and Leu293, along with hydrogen interactions with Tyr104 and Arg355. This combination resulted in more effective interactions than did the currently approved drug, filanesib. The 2D and 3D representations of the superior compound interactions with the protein are shown in Fig. 4 . The remaining 2D and 3D representations of the top five compounds associated with the protein are displayed in Figure S3. Docking results of the top drugs against CCNA2 The docking results of the top five compounds against CCNA2 are presented in Table S5, with Omacetaxine Mepesuccinate considered the control drug [ 23 ]. As indicated in the table, the interactions of the compounds with the protein occur through hydrophobic and hydrogen bonds, which are observed in most compounds. The polar amino acids involved in these interactions include asparagine, arginine, lysine, and serine. The nonpolar amino acids involved in the interactions include alanine, proline, glycine, leucine, and isoleucine, whereas the acidic amino acids involved include aspartic acid and glutamate. In general, compound 4566, with a binding energy of -8.6 kJ/mol, exhibited the most effective binding to the enzyme. The main interactions between this compound and the enzyme involved hydrophobic interactions with amino acids Ile182, Glu317, Tyr180, Ser181, Phe152, Val157, Glu174, Gly153, Asn173, Leu320, and Phe319, along with hydrogen bonding between Thr316 and Tyr197. Compared with the approved drug omacetaxine, compound 4566 exhibited better binding to the enzyme. The 2D and 3D representations of this optimal interaction with the protein are shown in Fig. 5 , while the remaining 2D and 3D views of the top five compounds are displayed in Figure S4. Docking results of the top drugs against CDK1 The docking results of the top five compounds against the CDK1 protein are shown in Table S6, with Flavopiridol considered the control drug [ 24 ]. As indicated in the table, the interactions of the compounds with the protein are primarily through hydrophobic interactions and hydrogen bonding, which were observed in most of the compounds. The polar amino acids involved in these interactions include asparagine and tyrosine, whereas the acidic amino acids present in the interactions include aspartic acid and glutamate. In general, compound 5005498, with a binding energy of -9.00 kJ/mol, exhibited the most effective binding to the enzyme. The main interactions involved hydrophobic interactions with the amino acids Gln192, Gly13, Ala154, Asp146, Val64, Phe80, Ala31, Leu135, Val18, Lys33, and Asn133. The 2D and 3D representations of this optimal interaction with the protein are shown in Fig. 6 , while the remaining 2D and 3D views of the top five compounds are displayed in Figure S5. Discussion In this study, we analyzed the microarray dataset GSE41890 from the GEO database and identified a total of 1190 differentially expressed genes (DEGs) between healthy individuals and those with multiple sclerosis (MS). Among these genes, 195 presented increased expression, whereas 995 presented decreased expression. The gene with the greatest increase was MMP8 , whereas CD177 had the greatest decrease. Notably, changes in biological processes were significantly related to the immune response, signal transduction, regulation of enzyme activity, energy pathways, cellular communication, and the inflammatory response. Most cellular components were enriched in the cytoskeleton, plasma membrane, exosomes, cytoplasm, lysosomes, and platelet alpha granule membranes. The key molecular functions included receptor activity, GTPase activity, cytoskeleton protein binding, receptor signaling complex scaffold activity, transmembrane receptor activity, and GTPase-activating activity. Biological network analysis revealed that the immune response had the most significant association with MS, with a P value of 1.15 × 10 − 5 . Protein‒protein interaction (PPI) network analysis is crucial for understanding the molecular interactions that influence disease progression. We merged the PPI data to identify central proteins and constructed a PPI network of common DEGs via Cytoscape. By applying the CytoHubba and MCC plugins, we identified the top ten hub genes: KIF11 , CCNA2 , CDK1 , BUB1 , CDCA8 , DLGAP5 , TTK , KIF20A , TPX2 , and SPAG5 . Notably, CDK1 did not exhibit changes in expression, whereas the other genes presented decreased expression in the drug treatment group compared with the MS group. Among these, KIF11 , CCNA2 , and CDK1 , with the highest scores and available protein structures, were selected for further pharmacological investigation. KIF11 is a member of the kinesin superfamily, which moves along microtubule tracks in the cell with the aid of nanomotors, and plays essential roles in chromosome positioning, centrosome segregation, and bipolar spindle establishment during cell mitosis. Axonal loss is a primary cause of irreversible disability in MS, and axonal damage, including severe damage, begins early in the disease process and is correlated with inflammatory activity [ 25 , 26 ]. Therapeutic strategies targeting KIF11 may increase axonal survival and delay neurological disability progression in MS patients. CCNA2 is a cyclin family member that regulates cell cycle progression. Previous studies have indicated altered expression of CCNA2 in MS [ 27 – 29 ]. Cyclin/CDK complexes are crucial for different phases of the cell cycle, with cyclin A/CDK2 being particularly important for S phase and DNA replication, as well as G2 phase development. Dysregulation of cyclins can lead to various pathologies, including cancer and neurological disorders [ 30 ]. CDK1 is a key player in cell cycle regulation, specifically during the G2/M phase transition. It is involved in DNA synthesis and the modulation of G2 progression [ 31 ]. Dysregulation of CDK1 can contribute to diseases such as cancer and neurological disorders [ 32 , 33 ]. Through central protein and drug interaction analysis, we identified several candidate drugs and prioritized targets for MS treatment. Molecular docking was employed to assess potential interactions between these drugs and the identified protein targets. Among the five compounds studied, compound 9944353535 demonstrated the most effective binding with KIF11 , with a binding energy of -9.5 kJ/mol. The primary interactions were hydrophobic, involving amino acids such as Ala356 and Thr300, along with hydrogen interactions with Tyr104 and Arg355. This binding is more effective than that of the approved drug filanesib, suggesting that compound 99443535 could serve as a potential KIF11 inhibitor for MS therapy. For CCNA2 , compound 4566 displayed a binding energy of -8.6 kJ/mol, indicating strong interactions with the enzyme. The binding involves key amino acids integral to the cyclin A binding site [ 34 ] and is more favorable than the approved drug omacetaxine is, highlighting the therapeutic potential of inhibiting cyclin A in MS. Finally, compound 5005498 showed a binding energy of -9.00 kJ/mol with CDK1 , with key interactions involving amino acids located in the ATP binding site. This compound exhibited better binding than Flavopiridol did, supporting its potential as a CDK1 inhibitor in MS treatment. Conclusion This study analyzed the microarray data of GSE41890 from patients with MS and healthy individuals to identify genes, proteins, and potential drug targets involved. We highlighted changes in the expression of ten genes and identified three candidate drugs linked to MS. Various drug combinations were evaluated for these proteins, leading to the selection of three promising candidates. However, preclinical studies are necessary to validate these findings in cell or animal models before clinical application. Overall, biological network analysis offers valuable insights into the development of drug combinations that target disrupted signaling pathways in MS. Declarations Author Contribution Dr. Hosseini-Koupaei and Dr.Babak Nezhad served as supervisors and provided oversight throughout the project. Ms. Edalat and Dr. Babak Nezhad were responsible for the study methodology, data analysis, and drawing conclusions. Mr. Mahouzi and Ms. Edalat contributed to the translation of the manuscript. Ms.Edalat wrote the main manuscript text. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. References Hauser, S.L. and J.R. Oksenberg, The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration . Neuron, 2006. 52(1): p. 61–76. https://doi.org/10.1016/j.neuron.2006.09.011 . 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Pavletich, N.P., Mechanisms of cyclin-dependent kinase regulation: structures of Cdks, their cyclin activators, and Cip and INK4 inhibitors . Journal of molecular biology, 1999. 287(5): p. 821–828. https://doi.org/10.1006/jmbi.1999.2640 . Kim, S.S., et al., Identification of novel cyclin A2 binding site and nanomolar inhibitors of cyclin A2-CDK2 complex . Current computer-aided drug design, 2021. 17(1): p. 57–68. https://doi.org/10.2174/1573409916666191231113055 . Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6512879","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449119295,"identity":"fc486ed2-8879-4c5d-954f-ceec880c555b","order_by":0,"name":"Mehrsa Edalat","email":"","orcid":"","institution":"Department of Chemical Engineering, Naghshejahan Higher Education Institute","correspondingAuthor":false,"prefix":"","firstName":"Mehrsa","middleName":"","lastName":"Edalat","suffix":""},{"id":449119296,"identity":"237bc601-077b-4598-8399-91f61d783461","order_by":1,"name":"Nasim Babak Nezhad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACAyBmBiI7NvbmA0C2hAzRWpL5eI4lgLTwEK2FcZ5EDojNQFiLuXTz4c8FNdbMbDxnPr+6UWPBw8B++OgGfFos5xxLMJ5xLJ2Pjb13m3XOMaDDeNLSbuB12I0cg2QetsNAW85uM85hA2qR4DEjoCX/w2Gef4cZ2yRynhnn/CNKSw5jM28bWAvz49w2IrRYzkgzZubtS09m4zlmxpzbJ8HDRsgv5hLJjz/zfLO2k29vfvw551udHD/74WN4tSADNgkwSaxyEGD+QIrqUTAKRsEoGDkAAOYoQ3xhK5ogAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Biology, Faculty of Science, Naghshejahan Higher Education Institute","correspondingAuthor":true,"prefix":"","firstName":"Nasim","middleName":"Babak","lastName":"Nezhad","suffix":""},{"id":449119297,"identity":"a3dcf9b9-8ece-4007-b736-f16ec392594e","order_by":2,"name":"Mansoore Hosseini-Koupaei","email":"","orcid":"","institution":"Department of Biology, Faculty of Science, Naghshejahan Higher Education Institute","correspondingAuthor":false,"prefix":"","firstName":"Mansoore","middleName":"","lastName":"Hosseini-Koupaei","suffix":""},{"id":449119298,"identity":"06877e56-2501-456b-b897-446e12dc6f3b","order_by":3,"name":"Mehran Mahouzi","email":"","orcid":"","institution":"Faculty of Pharmacy, University of Szeged","correspondingAuthor":false,"prefix":"","firstName":"Mehran","middleName":"","lastName":"Mahouzi","suffix":""}],"badges":[],"createdAt":"2025-04-23 13:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6512879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6512879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81955039,"identity":"80c4ab3b-aa31-4d15-9f2b-5381a23f4e93","added_by":"auto","created_at":"2025-05-05 09:47:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41156,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot displaying differentially expressed genes (DEGs) in MS samples compared with control samples in the GSE41890 dataset.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/b50a9cb3ea70fda76b5187d7.png"},{"id":81953618,"identity":"94faaa86-4d66-4153-a7b3-6ddc6d3b0c06","added_by":"auto","created_at":"2025-05-05 09:39:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74382,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram illustrating the distribution of differentially expressed genes (DEGs) identified in the GSE41890 dataset.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/f99a0a043d9b357bc4063484.png"},{"id":81956400,"identity":"3da78c8e-c1a8-45d8-baa3-cf314f5a15bd","added_by":"auto","created_at":"2025-05-05 09:55:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199494,"visible":true,"origin":"","legend":"\u003cp\u003eProtein‒protein interaction network generated by Cytoscape software from the Cytohubba section via the MCC method.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/2a48873d4422cab93bea03fa.png"},{"id":81953619,"identity":"f1fc62e0-81d9-452b-9ddf-b1d12c0c66d5","added_by":"auto","created_at":"2025-05-05 09:39:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":135183,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D views of the interaction between compound 99443535 and KIF11 following docking analysis.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/871a326b15c68e3660cf419a.png"},{"id":81955043,"identity":"6fff32da-18a0-4fe9-bfc7-81ae270d74be","added_by":"auto","created_at":"2025-05-05 09:47:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174580,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D views of the interaction between compound 4566 and CCNA2 following docking analysis.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/08e3b81e2ec14955459da679.png"},{"id":81956401,"identity":"9e8b572d-fb56-4667-b506-28ddb75e097a","added_by":"auto","created_at":"2025-05-05 09:55:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167674,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D views of the interaction between compound 5005498 and CDK1 following docking analysis.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/7b49fa0f854873fc280afd92.png"},{"id":82635790,"identity":"8ff96f80-18bb-472c-8a90-d4544c3f10df","added_by":"auto","created_at":"2025-05-13 14:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/738264c4-03e9-4c4a-a02a-a119be93a674.pdf"},{"id":81953638,"identity":"a99819ca-b293-4d9c-a8a0-542a948c19ac","added_by":"auto","created_at":"2025-05-05 09:39:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6366646,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6512879/v1/59fcdbc1cb2d0d461bf8691d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide since 1990. The symptoms caused by MS can lead to significant disability and pain for patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This chronic disease has a sudden and unpredictable onset, causing various stresses across different dimensions of patients' lives. Although the main cause of this disease is not fully understood, its mechanism involves damage to the body's immune system or disruption of the cells that produce the myelin sheath. Several factors, including a history of infectious diseases, immune system defects, stress, and environmental influences, can contribute to the onset of the disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MS is believed to arise from a combination of environmental factors, such as diet, pollution, and genetics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMS is classified into four forms on the basis of the pattern of occurrence and progression of the disease: Relapsing-remitting MS (RRMS): RRMS is the most common subtype, accounting for approximately 87% of cases. It is characterized by unpredictable acute attacks followed by periods of remission [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During RRMS, inflammatory attacks occur on myelin and nerve fibers, leading to symptoms such as visual disturbances, tingling and numbness, periodic fatigue, and disorders of the intestinal and urinary systems, as well as spasticity and impaired learning and memory. The results indicate that the diseased condition of RRMS significantly influences the small nucleolar RNAs \u0026ndash; miRNAs network in peripheral blood leukocytes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Primary progressive MS (PPMS): Patients with PPMS typically have fewer brain lesions. Symptoms may include difficulty walking, weakness, stiffness, and balance issues. Secondary progressive MS (SPMS): Approximately 65% of patients with RRMS subsequently develop SPMS, which is considered the second phase of the disease. Many patients experience increased weakness, bowel and urinary disorders, fatigue, stiffness, and mental health issues. Progressive relapsing MS (PRMS): PRMS is the rarest form, occurring in approximately 5% of patients. It is associated with symptoms such as eye pain and diplopia, along with dysfunctions in the sexual, bowel, and urinary systems, as well as dizziness and depression. MS is generally diagnosed between the ages of 20 and 40, although less than 1% of cases can occur in childhood, and approximately 2\u0026ndash;10% can arise after the age of 50 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Inflammation of white and gray matter tissues in the central nervous system due to the infiltration of immune cells and their cytokines is the primary cause of damage in MS. Studies have shown that T helper cells (CD4\u0026thinsp;+\u0026thinsp;T cells) play crucial roles in the initiation and progression of this disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. B lymphocytes and their cytokines are also significant factors in the pathogenesis of MS, and CD8\u0026thinsp;+\u0026thinsp;T cells (cytotoxic T cells) have been found in MS lesions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Environmental factors, including exposure to viral and bacterial agents such as Epstein\u0026ndash;Barr virus (EBV), human herpesvirus type 6, and Mycoplasma pneumoniae, along with smoking, vitamin deficiencies, diet, and exposure to ultraviolet radiation, are associated with the incidence of MS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpigenetics refers to hereditary changes outside the DNA sequence that affect gene expression. DNA methylation is the most well characterized major epigenetic process, and aberrant methylation in gene regulatory regions may underlie the mechanisms involved in the onset of MS. Several microRNAs (miRNAs) have been found to be upregulated in various tissues of MS patients, potentially altering immune responses and contributing to disease onset [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies have identified a group of genes within the human leukocyte antigen (\u003cem\u003eHLA\u003c/em\u003e) that are associated with an increased risk of MS. The Human Leukocyte Antigen locus includes six classical genes (HLA-A, -B, -C, -DP, -DQ, and -DR) that are crucial in triggering immune responses against pathogens, along with three non-classical genes (HLA-E, -F, and -G) that interact with Natural Killer cells to control virus-infected and cancerous cells. Susceptible genes in this region include \u003cem\u003eHLA-DR2+\u003c/em\u003e, \u003cem\u003eHLA-DQ6\u003c/em\u003e, \u003cem\u003eDQA 0102\u003c/em\u003e, \u003cem\u003eDQB1 0602\u003c/em\u003e, \u003cem\u003eHLA-DRB1\u003c/em\u003e, \u003cem\u003eDR15\u003c/em\u003e, \u003cem\u003eDRB11501\u003c/em\u003e, and \u003cem\u003eDRB11503\u003c/em\u003e. Additionally, the alpha receptors IL-7 and IL-2 are associated with MS susceptibility, whereas \u003cem\u003eHLA-C554\u003c/em\u003e and \u003cem\u003eHLA-DRB1*11\u003c/em\u003e appear to have protective effects [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eToday, bioinformatics is routinely employed in biomedical research to identify pathways and genes for potential prognostic and diagnostic biomarkers via high-throughput microarray and RNA-seq data. The rapid advancements in bioinformatics and computational biology offer significant potential for uncovering new insights into disease mechanisms and identifying possible therapeutic targets [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Importantly, candidate genes can serve as therapeutic targets in the treatment of MS and other complex diseases. Therefore, in the present study, we utilized microarray technology and bioinformatics tools to identify genes and their interaction pathways to uncover common molecular mechanisms. Additionally, on the basis of the results of this analysis, drug predictions for the treatment of MS can also be made. Genome-wide association studies (GWASs) offer an approach in genetics that tests many genetic variants in the genomes of multiple individuals to identify genotype\u0026ndash;phenotype relationships, revolutionizing the field of disease genetics. Specifically, GWAS compares allele frequencies at various positions in the genome between healthy and diseased individuals, with significant differences indicating disease associations. GWAS in MS has shown that a substantial portion of the heritability of the disease can be attributed to the genetic region of the major histocompatibility complex (MHC) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. An X chromosome variant, 200 autosomal susceptibility variants outside the MHC, and 32 variants within the developed MHC that are independently involved in the pathogenesis of MS have been identified [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Identifying the risk genes associated with MS remains a key focus of research, as the actual risk genes may be located far from the susceptibility variants. Therefore, it is essential to prioritize the genes and functions associated with the genetic variants highlighted by the MS GWAS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this study, microarray data were extracted from the NCBI database, specifically from the GEO section. Two groups, normal (healthy) and treatment (MS), were compared. The R programming language was employed to analyze the data. Using R software, gene expression levels in these collections were analyzed to distinguish genes with distinct expression profiles between diseased and healthy samples in an in silico environment. In this research, the index genes were first determined via the GEOquery package in R. Subsequently, the desired molecular network was constructed, effective drugs were designed, and their effects were evaluated via bioinformatics software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe study utilized microarray data sourced from the National Center for Biotechnology Information (NCBI) database, specifically from the Gene Expression Omnibus (GEO). The dataset selected for analysis was GSE41890, which comprises gene expression information from 68 samples: 24 samples related to healthy controls and 44 samples from patients with MS (22 samples in recovery and 22 samples in relapse). To carry out this study, the analysis was conducted in two parts: first, comparing the relapsed or unmedicated samples with the recovering or medicated samples (MS.Treat vs. MS.UnTreat), and second, comparing the medicated samples with the control samples (MS.UnTreat vs. Control). This study was conducted in 2013. Initially, the raw data related to the GSE41890 microarray dataset were preprocessed, which included formatting and removing missing values. All analyses were performed via the R programming language, specifically version 4.3.2.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTarget finding for genes and identification of DEGs\u003c/h2\u003e \u003cp\u003eTo identify common genes, target genes were selected via the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. According to the volcano plot, genes with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u0026minus;\u0026thinsp;0.1\u0026thinsp;\u0026lt;\u0026thinsp;Log2FC\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were uploaded to the site, resulting in a Venn diagram that revealed 1,190 differentially expressed genes (DEGs) from these analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtein‒protein interaction (PPI) network\u003c/h3\u003e\n\u003cp\u003eTo elucidate the interactions between the identified proteins, a protein\u0026ndash;protein interaction network was constructed via the STRING database. This network was visualized via Cytoscape, a powerful tool for network analysis. The PPI network helps identify key proteins that may serve as hubs in the molecular network, potentially influencing the progression of MS. Through the Cytohubba and MCC plugins, 10 hub genes involved in MS were identified, resulting in a network with 67 nodes and 629 interactions (edges). To check pathways and ontologies, the 1190 common genes were loaded into FunRich software, yielding four diagrams and four tables related to biological pathways, cellular components, molecular functions, and biological processes.\u003c/p\u003e\n\u003ch3\u003eDrug interactions and pharmacokinetics\u003c/h3\u003e\n\u003cp\u003eThe methodology also included an investigation of protein‒drug interactions. The NetworkAnalyst tool was utilized to explore potential drug candidates that could interact with the identified proteins. The pharmacokinetic properties of these compounds were assessed via the SwissADME database, which evaluates the absorption, distribution, metabolism, and excretion (ADME) characteristics of the drugs.\u003c/p\u003e\n\u003ch3\u003eMolecular docking\u003c/h3\u003e\n\u003cp\u003eThe three-dimensional structures of the \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eCDK1\u003c/em\u003e proteins, determined by X-ray diffraction, were downloaded from the www.rcsb.org database in PDB format. Docking operations were performed via PyRx software. In PyRx, the dimensions of the grid box were selected on the basis of the amino acids involved in binding, and relevant literature was used to verify the binding of standard drugs with the proteins [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes (DEGs) identified via MS\u003c/h2\u003e \u003cp\u003eThe GSE41890 dataset, obtained from the GEO database, included 44 MS samples and 24 control samples. A total of 1190 DEGs were identified from this dataset. The results are presented as volcano plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, the DEGs were analyzed via a Venn diagram. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 1190 DEGs were found in the dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDEG enrichment analysis\u003c/h3\u003e\n\u003cp\u003eUsing FunRich software, we investigated the pathways related to the identified differentially expressed genes (DEGs). Major changes in pathways associated with the cytoskeleton, homeostasis, and receptor activity and a significant alteration in the immune response were observed. A diagram of the biological pathways and gene ontologies is shown in Figure S1. A list of signaling pathways, along with their corresponding P values, is provided 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\u003eSignaling pathways and associated information obtained from FunRich software.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of genes in the dataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/15E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegulation of enzyme activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/00185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnergy pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/003117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/003243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflammatory response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/007381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/01E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/45E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/56E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCytoplasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/55E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/45E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatelet alpha granule membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/39E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReceptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/25E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTPase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCytoskeletal protein binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReceptor signaling complex scaffold activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransmembrane receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTPase activator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/001972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemostasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/17E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell surface interactions at the vascular wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/99E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatelet activation, signaling and aggregation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/48E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatelet Aggregation (Plug Formation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/11E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntegrin family cell surface interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/000153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta1 integrin cell surface interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/00033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePPI network and module Analysis\u003c/h3\u003e\n\u003cp\u003eWe identified protein interactions among the overlapping differentially expressed genes (DEGs) via the STRING online database. Cytoscape was used to construct a protein‒protein interaction (PPI) network consisting of 67 nodes and 629 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The top ten hub genes were determined on the basis of betweenness centrality, node degree, and the MCC method. These hub genes included \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eBUB1\u003c/em\u003e, \u003cem\u003eCDCA8\u003c/em\u003e, \u003cem\u003eDLGAP5\u003c/em\u003e, \u003cem\u003eTTK\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eTPX2\u003c/em\u003e, and \u003cem\u003eSPAG5\u003c/em\u003e. The full names of the proteins and their corresponding PDB codes are provided in Table S1. The interaction network of genes based on the MCC, generated through Cytohubba, is shown in Figure S2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA comparison of hub gene expression between the drug-treated group and the MS group is shown in Table S2. The results of the bioinformatic analysis of MS expression data reveal significant findings that could pave the way for new therapeutic strategies. This study identified three critical protein targets, \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eCDK1\u003c/em\u003e, that are involved in various cellular processes relevant to MS pathology. A total of 11 drugs for \u003cem\u003eKIF11\u003c/em\u003e, 33 drugs for \u003cem\u003eCCNA2\u003c/em\u003e, and 7 drugs for \u003cem\u003eCDK1\u003c/em\u003e were extracted from the research database.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetic properties\u003c/h2\u003e \u003cp\u003eThe pharmacokinetic properties of the identified compounds were assessed via the SwissADME database. Among the fifty-one candidates, forty-nine compounds emerged as viable candidates, demonstrating suitable pharmacokinetic profiles. The list of drugs that complied with Lipinski's rule from the SwissADME database is provided in Table S3. This assessment is crucial because it provides insights into the absorption, distribution, metabolism, and excretion (ADME) characteristics of drugs, which are essential for their effectiveness and safety in clinical applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDocking results of the top drugs against KIF11\u003c/h2\u003e \u003cp\u003eThe docking results of the top five compounds against the \u003cem\u003eKIF11\u003c/em\u003e protein are shown in Table S4, with \u003cem\u003efilanesib\u003c/em\u003e considered the control drug [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As indicated in the table, the interactions of the compounds with the protein primarily occur through hydrophobic interactions, which are observed in most of the compounds. The polar amino acids involved in these interactions include arginine 355, tyrosine 104, and asparagine 298. The acidic amino acids involved in the interactions included glutamic acid 304. Additionally, hydrogen bonds were also observed. Compound 99443535, with a binding energy of -9.5 kJ/mol, demonstrated the most effective binding with \u003cem\u003eKIF11\u003c/em\u003e. The main interactions between this compound and the enzyme included hydrophobic interactions with the amino acids Ala356, Thr300, Ile299, Gly296, Ile332, Ser269, Leu292, and Leu293, along with hydrogen interactions with Tyr104 and Arg355. This combination resulted in more effective interactions than did the currently approved drug, filanesib. The 2D and 3D representations of the superior compound interactions with the protein are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The remaining 2D and 3D representations of the top five compounds associated with the protein are displayed in Figure S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDocking results of the top drugs against CCNA2\u003c/h2\u003e \u003cp\u003eThe docking results of the top five compounds against \u003cem\u003eCCNA2\u003c/em\u003e are presented in Table S5, with \u003cem\u003eOmacetaxine Mepesuccinate\u003c/em\u003e considered the control drug [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As indicated in the table, the interactions of the compounds with the protein occur through hydrophobic and hydrogen bonds, which are observed in most compounds. The polar amino acids involved in these interactions include asparagine, arginine, lysine, and serine. The nonpolar amino acids involved in the interactions include alanine, proline, glycine, leucine, and isoleucine, whereas the acidic amino acids involved include aspartic acid and glutamate. In general, compound 4566, with a binding energy of -8.6 kJ/mol, exhibited the most effective binding to the enzyme. The main interactions between this compound and the enzyme involved hydrophobic interactions with amino acids Ile182, Glu317, Tyr180, Ser181, Phe152, Val157, Glu174, Gly153, Asn173, Leu320, and Phe319, along with hydrogen bonding between Thr316 and Tyr197. Compared with the approved drug omacetaxine, compound 4566 exhibited better binding to the enzyme. The 2D and 3D representations of this optimal interaction with the protein are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, while the remaining 2D and 3D views of the top five compounds are displayed in Figure S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDocking results of the top drugs against CDK1\u003c/h2\u003e \u003cp\u003eThe docking results of the top five compounds against the \u003cem\u003eCDK1\u003c/em\u003e protein are shown in Table S6, with \u003cem\u003eFlavopiridol\u003c/em\u003e considered the control drug [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As indicated in the table, the interactions of the compounds with the protein are primarily through hydrophobic interactions and hydrogen bonding, which were observed in most of the compounds. The polar amino acids involved in these interactions include asparagine and tyrosine, whereas the acidic amino acids present in the interactions include aspartic acid and glutamate. In general, compound 5005498, with a binding energy of -9.00 kJ/mol, exhibited the most effective binding to the enzyme. The main interactions involved hydrophobic interactions with the amino acids Gln192, Gly13, Ala154, Asp146, Val64, Phe80, Ala31, Leu135, Val18, Lys33, and Asn133. The 2D and 3D representations of this optimal interaction with the protein are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, while the remaining 2D and 3D views of the top five compounds are displayed in Figure S5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed the microarray dataset GSE41890 from the GEO database and identified a total of 1190 differentially expressed genes (DEGs) between healthy individuals and those with multiple sclerosis (MS). Among these genes, 195 presented increased expression, whereas 995 presented decreased expression. The gene with the greatest increase was \u003cem\u003eMMP8\u003c/em\u003e, whereas \u003cem\u003eCD177\u003c/em\u003e had the greatest decrease. Notably, changes in biological processes were significantly related to the immune response, signal transduction, regulation of enzyme activity, energy pathways, cellular communication, and the inflammatory response. Most cellular components were enriched in the cytoskeleton, plasma membrane, exosomes, cytoplasm, lysosomes, and platelet alpha granule membranes. The key molecular functions included receptor activity, GTPase activity, cytoskeleton protein binding, receptor signaling complex scaffold activity, transmembrane receptor activity, and GTPase-activating activity. Biological network analysis revealed that the immune response had the most significant association with MS, with a P value of 1.15 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. Protein‒protein interaction (PPI) network analysis is crucial for understanding the molecular interactions that influence disease progression. We merged the PPI data to identify central proteins and constructed a PPI network of common DEGs via Cytoscape. By applying the CytoHubba and MCC plugins, we identified the top ten hub genes: \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eBUB1\u003c/em\u003e, \u003cem\u003eCDCA8\u003c/em\u003e, \u003cem\u003eDLGAP5\u003c/em\u003e, \u003cem\u003eTTK\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eTPX2\u003c/em\u003e, and \u003cem\u003eSPAG5\u003c/em\u003e. Notably, \u003cem\u003eCDK1\u003c/em\u003e did not exhibit changes in expression, whereas the other genes presented decreased expression in the drug treatment group compared with the MS group. Among these, \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eCDK1\u003c/em\u003e, with the highest scores and available protein structures, were selected for further pharmacological investigation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eKIF11\u003c/em\u003e is a member of the kinesin superfamily, which moves along microtubule tracks in the cell with the aid of nanomotors, and plays essential roles in chromosome positioning, centrosome segregation, and bipolar spindle establishment during cell mitosis. Axonal loss is a primary cause of irreversible disability in MS, and axonal damage, including severe damage, begins early in the disease process and is correlated with inflammatory activity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therapeutic strategies targeting \u003cem\u003eKIF11\u003c/em\u003e may increase axonal survival and delay neurological disability progression in MS patients. \u003cem\u003eCCNA2\u003c/em\u003e is a cyclin family member that regulates cell cycle progression. Previous studies have indicated altered expression of \u003cem\u003eCCNA2\u003c/em\u003e in MS [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Cyclin/CDK complexes are crucial for different phases of the cell cycle, with cyclin A/CDK2 being particularly important for S phase and DNA replication, as well as G2 phase development. Dysregulation of cyclins can lead to various pathologies, including cancer and neurological disorders [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eCDK1\u003c/em\u003e is a key player in cell cycle regulation, specifically during the G2/M phase transition. It is involved in DNA synthesis and the modulation of G2 progression [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Dysregulation of CDK1 can contribute to diseases such as cancer and neurological disorders [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough central protein and drug interaction analysis, we identified several candidate drugs and prioritized targets for MS treatment. Molecular docking was employed to assess potential interactions between these drugs and the identified protein targets. Among the five compounds studied, compound 9944353535 demonstrated the most effective binding with \u003cem\u003eKIF11\u003c/em\u003e, with a binding energy of -9.5 kJ/mol. The primary interactions were hydrophobic, involving amino acids such as Ala356 and Thr300, along with hydrogen interactions with Tyr104 and Arg355. This binding is more effective than that of the approved drug filanesib, suggesting that compound 99443535 could serve as a potential \u003cem\u003eKIF11\u003c/em\u003e inhibitor for MS therapy. For \u003cem\u003eCCNA2\u003c/em\u003e, compound 4566 displayed a binding energy of -8.6 kJ/mol, indicating strong interactions with the enzyme. The binding involves key amino acids integral to the cyclin A binding site [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and is more favorable than the approved drug omacetaxine is, highlighting the therapeutic potential of inhibiting cyclin A in MS. Finally, compound 5005498 showed a binding energy of -9.00 kJ/mol with \u003cem\u003eCDK1\u003c/em\u003e, with key interactions involving amino acids located in the ATP binding site. This compound exhibited better binding than Flavopiridol did, supporting its potential as a \u003cem\u003eCDK1\u003c/em\u003e inhibitor in MS treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analyzed the microarray data of GSE41890 from patients with MS and healthy individuals to identify genes, proteins, and potential drug targets involved. We highlighted changes in the expression of ten genes and identified three candidate drugs linked to MS. Various drug combinations were evaluated for these proteins, leading to the selection of three promising candidates. However, preclinical studies are necessary to validate these findings in cell or animal models before clinical application. Overall, biological network analysis offers valuable insights into the development of drug combinations that target disrupted signaling pathways in MS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Hosseini-Koupaei and Dr.Babak Nezhad served as supervisors and provided oversight throughout the project. Ms. Edalat and Dr. Babak Nezhad were responsible for the study methodology, data analysis, and drawing conclusions. Mr. Mahouzi and Ms. Edalat contributed to the translation of the manuscript. Ms.Edalat wrote the main manuscript text. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHauser, S.L. and J.R. Oksenberg, \u003cem\u003eThe neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration\u003c/em\u003e. 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Current computer-aided drug design, 2021. 17(1): p. 57\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1573409916666191231113055\u003c/span\u003e\u003cspan address=\"10.2174/1573409916666191231113055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multiple sclerosis, microarray, gene, protein‒protein interaction, docking","lastPublishedDoi":"10.21203/rs.3.rs-6512879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6512879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased. MS is a disease that destroys the myelin sheath of nerve cells in the central nervous system. In the present study, microarray technology and bioinformatics tools were used to identify genes and their interaction pathways to investigate common molecular mechanisms. Additionally, on the basis of the results of this analysis, drug predictions for the treatment of MS were made. Microarray data from the NCBI database, specifically from the GEO section related to GSE41890, containing information on gene expression in 68 samples, were extracted. The two groups, normal and treatment, were subsequently compared. The R programming language was used to analyze the differentially expressed genes (DEGs), and the desired molecular network was constructed. The protein‒protein interaction (PPI) network was created via STRING, and PPI network module analysis was performed via Cytoscape. To investigate protein‒drug interactions, NetworkAnalyst was used. Finally, docking operations were performed via PyRx software. A total of 1190 DEGs, which were involved mainly in cell immunity, the cell cycle, cell proliferation, and signal transduction, were identified. The PPI network contained 67 nodes and 629 interactions. Three protein targets and fifty-one drug candidates were identified; specifically, approximately 11 drugs were linked to \u003cem\u003eKIF11\u003c/em\u003e, 33 drugs were linked to \u003cem\u003eCCNA2\u003c/em\u003e, and 7 drugs were linked to \u003cem\u003eCDK1\u003c/em\u003e. A total of 99443535, 5005498, and 4566 compounds were generally connected to \u003cem\u003eKIF11\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, and \u003cem\u003eCCNA2\u003c/em\u003e, respectively.\u003c/p\u003e","manuscriptTitle":"Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 09:39:36","doi":"10.21203/rs.3.rs-6512879/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":"c4c027e0-510b-4c15-845a-1ef6e9c35996","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-13T14:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 09:39:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6512879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6512879","identity":"rs-6512879","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}