Integrative Gene Target Mapping, RNA Sequencing, In Silico Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Study of Marine Derived Molecules for Type 1 Diabetes Mellitus

Integrative Gene Target Mapping, RNA Sequencing, In Silico Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Study of Marine Derived Molecules for Type 1 Diabetes Mellitus | 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 Integrative Gene Target Mapping, RNA Sequencing, In Silico Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Study of Marine Derived Molecules for Type 1 Diabetes Mellitus Basavaraj Mallikarjunayya Vastrad, Shivaling Pattanashetti, Veeresh Sadashivanavar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7640932/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 Type 1 diabetes mellitus (T1DM) is a metabolic disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in T1DM through adopting integrated bioinformatics tools. The gene expression Omnibus (GEO) database was used to obtain next generation sequencing data of T1DM and normal control samples. Furthermore, differentially expressed genes (DEGs) were screened using the Limma package in R bioconductor package. Gene Ontology (GO) and pathway enrichment analyses were performed by g:Profiler. The protein-protein interaction (PPI) network was plotted with IID PPI database and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC. Then, microRNAs (miRNAs) and transcription factors (TFs) in T1DM were screened out from the miRNet and NetworkAnalyst database. Then, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Moreover, a drug-hub gene interaction network of the hub genes was constructed and predicted the drug molecule against hub genes. The receiver operating characteristic (ROC) curves were generated to predict diagnostic value of hub genes. Finally we performed molecular docking, ADMET profiling and molecular dynamics simulation studies of marine derived chemical constituents using Schrodinger Suite 2025-1. A total of 958 DEGs were screened: 479 up regulated genes and 479 down regulated genes. DEG were mainly enriched in the terms of developmental process, membrane, cation binding, response to stimulus, cell periphery, ion binding, neuronal system and metabolism. Based on the data of protein-protein interaction (PPI), the top 10 hub genes (5 up regulated and 5 down regulated) were ranked, including FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6. The miRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-657, hsa-miR-1266-5p, NOTCH1 and GTF3C2 might play an important role in the pathogenesis of T1DM. The drug-hub gene interaction network showed that Clenbuterol, Diethylstilbestrol, Selegiline and Isoflurophate predicted therapeutic drugs for the T1DM. Molecular docking and molecular dynamics simulation study revealed that CMNPD5805 and CMNPD30286 as potential inhibitors of FN1 (pdb id : 3M7P) a key biomarker in pathogenesis of T1DM. These findings promote the understanding of the molecular mechanism and clinically related molecular targets for T1DM. Bioinformatics Drug Discovery, Design, & Development Endocrinology & Metabolism Bioinformatics analysis Differentially expressed genes Type 1 diabetes mellitus Biomarkers Diagnosis Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Introduction Type 1 diabetes mellitus (T1DM) is a prevalent chronic autoimmune disorder [Gillespie, 2006 ], which affects around 5–10% of the world’s children and adolescents population [Maahs and West, 2010]. T1DM is primarily characterized by autoimmune destruction of insulin-producing β cells in the pancreas by CD4 + and CD8 + T cells and macrophages infiltrating the islets [Zhang et al. 2022 ]. This condition affects absolute insulin production [Desai and Deshmukh, 2016]. It is well known that inflammatory cascades [Blagov et al. 2023 ] and oxidative stress [Novoselova et al. 2021 ] of patients with T1DM can be initiated or exacerbated by genetic and certain environmental factors. Therefore, T1DM shares a tight relationship with a number of illnesses, such as type 2 diabetes mellitus [Dabelea et al. 2017 ], depression [Wang et al. 2019 ], neurodegenerative disorders [Satuli-Autere et al. 2024 ], obesity [Vilarrasa et al. 2021 ], hypertension [Ponirakis et al. 2019 ], cardiovascular disorders [Colom et al. 2021 ], kidney disorders [Chowdhury et al. 2021 ], lung disorders [Mameli et al. 2021 ], liver diseases [Memaj and Jornayvaz, 2022 ], eye disorders [Dereci et al. 2022 ], autoimmune disorders [Popoviciu et al. 2023 ], osteoporosis [Khan and Fraser, 2015 ] and stroke [Hägg-Holmberg et al. 2017 ]. Despite imminent confirmation providing considerable mechanistic insight into this condition, the exact molecular mechanism of insulin-producing β cells in the pancreas destruction is still being debated. As T1DM has ambiguous molecular pathogenesis and an unacceptable response to treatment, it is necessary to search the molecular mechanism of T1DM to establish effective target treatments. At present, the key treatment strategies for T1DM 1) hormone therapies insulin [Mathieu et al. 2017 ] 2) immune-focused therapies include cell-directed interventions [Hagopian et al. 2013 ], cytokine-directed interventions [Dwyer et al. 2016 ] and antigen vaccination [Roep et al. 2019 ] 3) non-immunomodulatory adjunctives include amylin [Martin, 2006 ], sodium-glucose cotransporter inhibitors [Ferrannini, 2017 ], GLP-1 receptor agonists [Aroda, 2018 ] and verapamil [Xu et al. 2012 ]. However, these treatment methods may cause some adverse effects. However, T1DM might also be able to be caused by many unknown causes, which cannot be well solved by current drug treatment and T1DM is still a complicated incurable endocrine autoimmune disease. Thus, it is necessary for us to utilize bioinformatics and next generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of T1DM. With the advancement of NGS technology, integrated bioinformatics provides an effective tool for discovering valuable new biomarker targets and signaling pathways for T1DM [Pujar et al. 2022 ; Prashanth et al. 2021 ]. Using bioinformatics strategy, CTLA4 [Kavvoura and Ioannidis, 2005 ], PD1 [Ni et al. 2007 ], KIAA0350 [Hakonarson et al. 2007 ], CYP27B1 [Bailey et al. 2007 ], and HLA-B and HLA-A [Nejentsev et al. 2007 ] are significantly associated with new biomarkers of T1DM. Signaling pathways include PI3K/Akt signaling pathway [Camaya et al. 2022 ], AMPK and Akt signaling pathways [Kang et al. 2010 ], CaMKII/NF-κB/TGF-β1 and PPAR-γ signaling pathway [Gbr et al. 2021 ], NF-κB and Wnt/β-catenin/GSK3β signaling pathways [Liu et al. 2020 ] and AMPK-SREBP signaling pathway [Soetikno et al. 2013 ] might play a vital role in advancement of T1DM. However, there is still a large amount of NGS data related to T1DM to be explored. Herein, we used GSE270484 [Liu et al. 2024 ] from the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ) [Clough and Barrett, 2016 ] database to create a new NGS dataset to search for differentially expressed genes (DEGs) in T1DM. To uncover the possible functions and enriched pathways, the DEGs in T1DM related DEGs were subjected to gene ontology (GO) and REACTOME pathway enrichment analysis, and protein-protein interaction (PPI) network and module analyses. Subsequently, we constructed a miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene intraction network, which will be helpful in investigation on the regulatory mechanisms of these hub genes. The predictive capability of the hub genes were analyzed by receiver operating characteristic (ROC) curve. Molecular docking, Molecular dynamics simulation and ADMET studies were performed. The investigation probably revealed the molecular pathogenic mechanism and potential therapeutic target of T1DM. Materials and Methods Next generation sequencing data source In the investigation, NGS dataset GSE270484 [Liu et al. 2024 ] was obtained from GEO (Home–GEO–NCBI (nih.gov)) database. GSE270484 was based on the GPL24676 Illumina NovaSeq 6000 (Homo sapiens) platform including 454 T1DM samples (isolated human beta islets) and 504 normal control samples (isolated human beta islets). Identification of DEGs “Limma” R bioconductor package [Ritchie et al. 2014] was utilized to identify the DEGs between the isolated human beta islets of T1DM patients and normal controls. A adjusted P value 0.5691 for up regulated genes and |log FC (fold change)| < -0.9219 for down regulated genes were considered statistically significant. The “gplot” R software package was used to construct a heatmap of the DEGs, and “ggplot2” R software package was used to establish a volcano plot of the DEGs. The up regulated and down regulated gene lists were sorted by logFC in NGS dataset. GO and pathway enrichment analyses of DEGs g:Profiler ( http://biit.cs.ut.ee/gprofiler/ ) [Reimand et al. 2007 ] was utilized to distinguish and enrich the biological attributes, such as biological processes (BP), cellular components (CC), molecular functions (MF) and signaling pathways of important DEGs. Moreover, Gene Ontology (GO) ( http://www.geneontology.org ) [Thomas, 2017 ] and REACTOME ( https://reactome.org/ ) [Fabregat et al. 2018 ] pathway enrichment analyses were used to identify the significant pathways. Adjust P-value < .05 was regarded as the cut-off criteria.. Construction of the PPI network and module analysis PPI network among all DEGs was established based on an online tool Integrated Interactions Database (IID) ( https://iid.ophid.utoronto.ca/ ) [Kotlyar et al. 2022 ] interactome and then software Cytoscape software (v3.10.3) ( http://www.cytoscape.org/ ) [Shannon et al. 2003] was employed to adjust and visualize PPI networks. Subsequently, we utilized a Network Analyzer plug-in of Cytoscape to determine hub genes according to the node degree [Luo et al. 2017 ], betweenness [Li et al. 2017 ], stress [Gilbert et al. 2021 ] and closeness [Li et al. 2020 ] algorithms. Subsequently, the Cytoscape plug-in the PEWCC [Zaki et al. 2013 ] was used to identify the significant modules from PPI network. Construction of the miRNA-hub gene regulatory network For searching potential interactions between hub genes and micro RNAs (miRNAs), miRNet database ( https://www.mirnet.ca/ ) [Fan et al. 2018] used for the identification of hub genes-targeted miRNAs. In this study, miRNet was used to establish a miRNA -hub gene regulatory network. The miRNA-hub gene crosstalk pairs were gained from databases such as TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. Then the miRNA-hub gene regulatory network was visualized by Cytoscape. Construction of the TF-hub gene regulatory network For searching potential interactions between hub genes and transcription factors (TFs), NetworkAnalyst ( https://www.networkanalyst.ca/ ) [Zhou et al. 2019 ] used for the identification of hub genes-targeted TFs. In this study, NetworkAnalyst was used to establish a TF-hub gene regulatory network. The TF-hub gene crosstalk pairs were gained from database ChEa. Then the TF-hub gene regulatory network was visualized by Cytoscape. Construction of the drug-hub gene interaction network For searching potential interactions between hub genes and drug molecules, NetworkAnalyst ( https://www.networkanalyst.ca/ ) [Zhou et al. 2019 ] used for the identification of hub genes-targeted drugs. In this study, NetworkAnalyst was used to establish a drug-hub gene interaction network. The drug-hub gene crosstalk pairs were gained from database DrugBank. Then the drug-hub gene interaction network was visualized by Cytoscape. Receiver operating characteristic curve (ROC) analysis The multivariate modeling with combined hub genes were used to identify biomarkers with high sensitivity and specificity for T1DM diagnosis. Used one data as training and other data as validation sample iteratively. The ROC curve analysis and area under curve (AUC) of hub genes conducted by the package “pROC” [Robin et al. 2011 ] was employed to analyze the specificity and diagnostic value of hub genes to T1DM. The AUC was quantified, with AUCs > 0.8 having statistical significance. In silico molecular docking studies Receptor selection based on gene information The selection of target receptor structures for molecular docking was initiated by identifying genes of interest relevant to the disease condition under study. Genes showing differential expression or known functional involvement in disease pathophysiology were prioritized based on genomic and transcriptomic data [Barabási et al. 2011 ; Subramanian et al. 2005 ]. The corresponding protein products of these genes were then retrieved using UniProt ( https://www.uniprot.org/ ) and NCBI Gene ( https://www.ncbi.nlm.nih.gov/gene/ ) databases. These databases provide curated information on gene-protein relationships, functional domains, isoforms, and organism-specific variants [UniProt 2023]. Once the protein names and UniProt accessions were determined, three-dimensional structures of these proteins were searched in the Protein Data Bank (PDB) ( https://www.rcsb.org/ ). Priority was given to experimentally determined structures (X-ray crystallography or cryo-EM) derived from Homo sapiens , with resolutions ≤ 2.5 Å and co-crystallized ligands when available. Structures were evaluated for completeness, presence of functional domains, and biologically relevant binding conformations. In cases where multiple structures were available, the one with the most complete and biologically relevant ligand-protein interaction site was selected [Berman et al. 2000 ; Plewczynski et al. 2011 ]. If co-crystallized ligands were present in the selected PDB entry, these were examined to confirm their biological relevance (e.g. substrate, inhibitor, agonist). The functional nature of the ligand was cross-validated using ligand bioactivity databases such as ChEMBL ( https://www.ebi.ac.uk/chembl/ ) and BindingDB ( https://www.bindingdb.org/ ), where half-maximal inhibitory concentration (IC₅₀), binding affinity (K i ), or other pharmacological data are reported [Gaulton et al. 2017 ; Liu et al. 2007 ]. To confirm the presence and accessibility of druggable binding pockets, cavity detection tools such as CASTp, Fpocket, or DoGSiteScorer were used [Tian et al. 2018 ; Le et al. 2009]. This ensured that the selected structure was suitable for molecular docking, with a validated and accessible ligand-binding domain. Overall, starting from gene selection to receptor structure identification, ensured biological relevance, structural accuracy, and docking compatibility of the chosen targets. The downloaded protein was prepared through a number of steps, including import and refining, review and modification, and minimization to fill in missing residues and chain sides. The protein's essential binding pocket remained unchanged. The OPLS3e (Optimized potential for liquid simulation) force field was used to reduce protein energy, resulting in a low-energy state protein. Materials and techniques The Maestro Schrödinger suite 2025-1(developed by Schrödinger, LLC, New York) was used for computational research. The study used tools such as Protein Preparation Wizard, Ligprep, GLIDE, Desmond, Prime, and WaterMap. Protein preparation and grid generation There were numerous PDB structures for FN1, but we choose and obtained the 3D structure of the protein with PDB id 3M7P for FN1. Then pre-processed and minimized the protein using the Maestro, Schrodinger suite's protein preparation wizard. Heavy atoms and water molecules were eliminated, missing side chains and amino acids were filled, and restricted minimization was used to create the least energy protein structure at neutral pH. During protein processing, all the essential amino acids are kept in the protein structure. The site map was used to create the grid for the FN1 based on active druggable site (Residue numbers: Chain A 323,330,345,347,362,363,369,371,372,373,374,375,376,377,384,401,402,403, 404,405,406,407,408,409,411,422,433,461,462,463,464,465,467,501,502,503,505,524,531,532,535,536,537,539,596) [Tian et al. 2018 ]. Ligand preparation Marine-derived chemical compounds (47,451 in total) were retrieved from the CMNPD database. These ligands were prepared using the LigPrep module of the Schrödinger Maestro suite. During preparation, the lowest-energy 3D structures were generated with appropriate assignment of chirality, tautomeric states, ring conformations, stereochemistry, and ionization states. The optimization was performed under the OPLS4 force field at neutral pH conditions to ensure physiologically relevant protonation states [Chen et al. 2010; Mili et al. 2024 ]. Physicochemical Screening After ligand preparation, the marine-derived compounds were carefully screened for their physicochemical properties using the Schrödinger suite. To begin with, Lipinski’s rule of five was applied to evaluate their drug-likeness and oral bioavailability. The QikProp tool was then used to predict key ADME characteristics, such as molecular weight, lipophilicity (logP), hydrogen bond donors and acceptors, along with other pharmacokinetic parameters. To further refine the dataset, filters for reactive functional groups were employed to exclude unstable or potentially toxic structures. Through this stepwise screening strategy, we retained a set of chemically stable and pharmacologically promising molecules suitable for downstream computational analyses. After extensive physicochemical filtering, a refined subset of 10,497 molecules was obtained from the initial library. These compounds met the required drug-likeness, stability, and ADME-related criteria, and were therefore considered suitable for subsequent computational investigations.The evaluation also took into account human oral absorption. Using the pkCMS webtool ( https://biosig.lab.uq.edu.au/pkcsm ) we predicted the toxicity profile of all chemicals [Klimoszek et al. 2024] Molecular docking From the refined library of 10,497 ligands, molecular docking was performed using the Glide module in the Schrödinger suite 2025-1. A stepwise docking workflow was then followed to combine speed with precision. In the first stage, all compounds were screened using High-Throughput Virtual Screening (HTVS) mode, which enabled rapid evaluation of the large dataset. The top 10% of hits from HTVS were advanced to Standard Precision (SP) docking, where binding poses and affinities were assessed in greater detail. Finally, the best 10% of SP results were subjected to Extra Precision (XP) docking. The XP mode applied stricter scoring functions and more refined sampling, helping to eliminate false positives and identify the most promising candidates. This tiered strategy progressively narrowed the library to a smaller set of ligands with strong predicted binding affinity and reliable interaction profiles, suitable for further computational validation. MM-GBSA analysis Further Prime-Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) [Guimarães et al. 2008; Wang et al. 2019 ] was used to determine the relative binding energy of the XP docked protein-ligand complexes for both proteins. Under the OPLS4 force field, Prime MM-GBSA employs the VSGB solvation model, which relies on the variable-dielectric generalized Born model and solvent as water. The strength with which the ligand binds to the chosen protein can also be predicted using binding energy. The following formula is used to determine the binding free energy of each chosen protein-ligand complex: ∆G bind = G complex- (G protein + G ligand). Where, G = MME (molecular mechanics energy) + GSGB (SGB salvation model for polar solvation) + GNP (nonpolar solvation). Induced fit docking Leads (Best 10 molecules) were chosen for IFD [Sherman et al. 2006 ; Sherman et al. 2006 ] based on the XP docking score, interaction created, MM-GBSA, and ADMET. During docking, IFD considers the ligand's and receptor's flexibility. It describes how a ligand's binding to a receptor causes dynamic changes in its structure. By enabling the receptor to modify its binding position to more closely match the ligand, it improves the precision of predicted binding interactions. This method increases the success rate of structure-based drug design by considering the dynamic nature of protein-ligand interactions. MD Simulation One crucial computational technique for examining the dynamic behaviour of ligand-protein complexes is the use of molecular dynamic (MD) simulations. The limitation of protein rigidity in XP-docking is one of the many benefits that MD simulations provide over conventional XP-docking. The protein-ligand complex is more dynamic and flexible in MD simulations, which enables ligands to change their conformation inside the protein's active site. The stability of the protein-ligand interaction is evaluated in a simulated aqueous environment, which is similar to real biological systems. Docking scores, binding energies and non-bonding interactions with significant amino acid residueswere used to select the top two potential candidates for MD simulations with both proteins. During MD simulation protein and ligands were suspended in water, molecular docking and IFD experiments don't replicate the biological milieu of the body; MDS is utilized to overcome this issue. Two hits for each protein were chosen for MD simulation using Schrodinger’s Desmond module [Saadabadi et al. 2024 ] in the current investigation based on the findings of MMGBSA, binding interactions, XP and induced fit docking results. Three steps were included in the MD simulation process: system builder, minimization, and MD simulation. The entire system is submerged in a single point charge (SPC) solvent model as part of the MDS procedure. The boundary condition was maintained in its orthorhombic form throughout the system development process with dimensions fixed at a, b, and c at 10 A 0 and angles α, β, and γ at 90°. The buffer box size is determined by using a technique used during the construction process. The system builder tool makes use of the OPLS4 force field, and for system minimizing, the minimization tool was employed. After that, MDS was run for 100 ns, producing one frame from the trajectory for every 100 picoseconds, for a total of 1000 frames produced during the MDS process. Results Identification of DEGs The DEGs were screened by “limma” package (adjusted P value 0.5691 for up regulated genes and |log FC (fold change)| < -0.9219 for down regulated genes). The GSE270484 NGS dataset contained 958 DEGs, including 479 up regulated genes and 479 down regulated genes (Table 1). The DEGs of the NGS dataset are shown in volcano plot Fig. 1, and the heatmap of the DEGs is shown in Fig. 2. GO and pathway enrichment analyses of DEGs Then, GO and REACTOME pathway enrichment analyses were implemented with the “g:Profiler” online tool to explore the potential biofunction of DEGs. The findings revealed that the most enriched GO keywords were associated with developmental process, biological regulation, response to stimulus and multicellular organismal process (BP); membrane, cytoplasm, cell periphery and endomembrane system (CC); cation binding, small molecule binding, ion binding and catalytic activity (MF) (Table 2, and Fig. 3 to Fig. 4). According to the REACTOME pathway enrichment study, DEGs have a significant role in neuronal system, GPCR downstream signalling, metabolism and transport of small molecules (Table 3, and Fig. 3 to Fig. 4 ). Construction of the PPI network and module analysis To reveal the interaction of each protein, the PPI network of the DEGs were built according to the IID database. There were 8412 edges and 13220 nodes in Fig. 5, followed by analysis using Cytoscape software. The cytoHubba plugin of Cytoscape was used to score each node gene by four selected algorithms, including node degree, betweenness, stress and closeness. The results shown that FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 were the most hub gene with the highest node degree, betweenness, stress and closeness (Table 4). Furthermore, the two significant modules were extracted from the PPI network. Module 1 contained 21 gene nodes, including DEUP1, GEM and CCHCR1 with 46 edges (Fig. 6). The hub genes in module 1 involved in developmental process, biological regulation and cytoplasm (Fig. 7). Module 2 contained 8 gene nodes, including SIAE, OS9, NAAA and TCTN1 with 14 edges (Fig. 8). The hub genes in module 2 involved in catalytic activity, transport of small molecules and response to stimulus (Fig. 9). Construction of the miRNA-hub gene regulatory network The miRNet database was used to anticipate and visualize the miRNA- hub gene interaction of hub genes. The miRNA-hub gene regulatory network consisted of 2494 (miRNA: 2193; Hub Gene: 301) nodes and 34835 edges (Fig. 10). FN1 was interacted with 439 miRNAs (ex; hsa-mir-657); FLNA was interacted with 424 miRNAs (ex; hsa-miR-200a-5p); PRKCA was interacted with 274 miRNAs (ex; hsa-miR-142-5p); SRGAP2 was interacted with 258 miRNAs (ex; hsa-mir-6834-5p); MAPT was interacted with 216 miRNAs (ex; hsa-miR-501-3p); CTNNB1 was interacted with 500 miRNAs (ex; hsa-miR-1266-5p); CD44 was interacted with 412 miRNAs (ex; hsa-mir-373); ASPH was interacted with 358 miRNAs (ex; hsa-miR-101-3p); LGALS3BP was interacted with 260 miRNAs (ex; hsa-mir-1252-5p); BCL6 was interacted with 206 miRNAs (ex; hsa-miR-33b-3p) (Table 5). Construction of the TF-hub gene regulatory network The Network Analyst database was used to anticipate and visualize the TF- hub gene interaction of hub genes. The TF-hub gene regulatory network consisted of 573 (TF: 271; HUB Gene: 302) nodes and 11306 edges (Fig. 11). PRKCA was interacted with 61 TFs (ex; NOTCH1); CDK8 was interacted with 61 TFs (ex; CEBPB); GSN was interacted with 60 TFs (ex; RUNX1); FN1 was interacted with 57 TFs (ex; HAND2); SRGAP2 was interacted with 53 TFs (ex; ASXL1); BCL6 was interacted with 96 TFs (ex; GTF3C2); CTNNB1 was interacted with 74 TFs (ex; AF4); CD44 was interacted with 63 TFs (ex; PPARG); ASPH was interacted with 56 TFs (ex; RELA); P4HA2 was interacted with 50 TFs (ex; SUZ12) (Table 5). Construction of the drug-hub gene interaction network The Network Analyst database was used to anticipate and visualize the drug- hub gene interaction of hub genes. For up regulated genrs include ADRB2 hub gene was interacted with 65 drug molecules were (ex; Clenbuterol), ESR2 hub gene was interacted with 34 drugs (ex; Diethylstilbestrol), ALOX5 hub gene was interacted with 15 drugs (ex; Minocycline), GLP1R hub gene was interacted with 15 drugs (ex; Exenatide) and PRKCA hub gene was interacted with 5 drugs (ex; Phosphatidyl) (Fig. 12 and Table 6), while down regulated genrs include MAOB hub gene was interacted with 32 drugs (ex; Selegiline), BCHE hub gene was interacted with 28 drugs (ex; Isoflurophate), CTSB hub gene was interacted with 14 drugs (ex; 2-Pyridinethiol), FBP1 hub gene was interacted with 10 drugs (ex; MB07803) and ALDH2 hub gene was interacted with 5 drugs (ex; Crotonaldehyde) (Fig. 13 and Table 6). Receiver operating characteristic curve (ROC) analysis Besides, ROC curves were performed and the corresponding AUC was calculated to validate the diagnostic value of hub genes. The diagnostic value of hub genes in T1DM samples and normal control samples are as follow: FN1 (AUC:0.924), GSN (AUC:0.916), ADRB2 (AUC:0.929), CEP128 (AUC:0.898), FLNA (AUC:0.907), CD74 (AUC:0.920), EFEMP2 (AUC:0.938), POU6F2 (AUC:0.884), P4HA2 (AUC:0.933) and BCL6 (AUC:0.893) (Fig. 14). Therefore, we hypothesise that FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 might be biomarkers For T1DM. Molecular docking and binding affinity study Molecular docking of marinederived ligands against FN1 (PDB ID: 3M7P) was performed using the Schrödinger suite's Glide module in a three-tier workflow: (i) High-Throughput Virtual Screening (HTVS) for rapid filtering, (ii) Standard Precision (SP) docking for refined ranking, and (iii) Extra Precision (XP) docking for high-accuracy binding pose prediction. This sequential strategy is widely viewed as vital in decreasing false positives and assuring dependability in large-scale screening [Fusani et al. 2020 ]. In XP mode, the top-ranked ligands' docking scores varied from − 9.898 to -8.253 kcal/mol (Supplementary Table S1). Among these, CMNPD30283 demonstrated the greatest binding (-9.898 kcal/mol), forming hydrogen bonds with LEU407, GLN330, and ARG503. ARG503 was regularly found as an anchoring residue in many of the top-scoring ligands.CMNPD21894 (-9.014 kcal/mol) displayed robust binding, sustained by interactions with GLY502, ARG503, and SER373, whereas CMNPD15938 (-8.795 kcal/mol) engaged TYR372, PRO363, and GLN409, highlighting contributions from π-π stacking and hydrogen bonding. Hydrophobic residues (LEU407, ALA465, and PHE531) were observed to offer shape complementarity, whereas polar residues (GLN409, GLU505, and GLN330) mediated electrostatic stabilization and ARG503, TYR372, SER373, GLN330, and GLN409 across various complexes suggests that these residues represent a conserved binding hotspot in FN1. The 2D interaction graphs (Fig. 15) demonstrated the preponderance of polar and electrostatic interactions, whereas 3D binding poses (Fig. 16) indicated profound ligand embedding within the FN1 cleft. Induced fit docking (IFD) While Glide XP makes good predictions, it expects a solid receptor structure. To capture protein adaptability, induced fit docking (IFD) was used. IFD takes into consideration side-chain rearrangements and backbone flexibility, providing a more accurate picture of ligand accommodation [Sherman et al. 2006 ].The IFD results verified CMNPD30283 as the leading candidate, having the best IFD dock score (-11.918 kcal/mol). This demonstrates enhanced complementarity between the ligand and FN1 following induced modifications. Other top performers were CMNPD21894 (-10.723 kcal/mol), CMNPD28752 (-10.693 kcal/mol), and CMNPD15938 (-10.246 kcal/mol), which stabilized their poses via adaptive interactions with ARG503, GLU505, and SER373, CMNPD5805 (-10.246). The latter generated adaptive hydrogen bonds with ARG503, SER373, and GLY502, demonstrating its ability to remain stable within a dynamically modified pocket. These findings highlight CMNPD5805 as a promising contender in addition to the top-ranked ligands. In contrast, CMNPD23529 (-8.819 kcal/mol) and CMNPD30363 (-8.973 kcal/mol) had lower IFD scores, indicating less flexibilitydriven accommodation.Overall, IFD demonstrated that FN1 gene active site physically changes to accommodate specific ligands, increasing the dependability of docking predictions (Supplementary Table S2). Prime MM-GBSA analysis on free energy and energy contributions To improve docking predictions, Prime MM-GBSA simulations were used to determine binding free energies (ΔG_bind) and break them down into Coulombic, van der Waals (vdW), lipophilic, and solvation contributions (Supplementary Table S3). Prime MM-GBSA analysis refined estimates of binding stability by breaking down ΔG_bind into major energy components. The binding free energy of the screened ligands ranged from − 53.46 to -38.20 kcal/mol, with three ligands forming the most stable complexes. CMNPD30283 had the highest favorable binding free energy (-53.46 kcal/mol), which was mostly due to strong electrostatic forces (Coulombic) and substantial van der Waals packing. These stabilizing contributions overcame the positive solvation penalty, resulting in an overall very favourable binding profile. Similarly, CMNPD30363 (-49.75 kcal/mol) and CMNPD30456 (-48.90 kcal/mol) were the next most stable binders, both stabilized by a synergistic mix of Coulombic contacts and van der Waals forces. The binding analysis reveals a synergistic mix of Coulombic contacts and van der Waals forces as the primary stabilizing influences within the FN1-ligand complexes. This energetic trend was consistently observed, with ligands exhibiting strong electrostatic anchoring to key residues such as ARG503 and GLN330, providing essential hydrogen bonds and salt bridge interactions This polar anchoring is complemented by deeper hydrophobic engagement involving residues like LEU407 and PHE531, which contribute through van der Waals interactions and hydrophobic packing to optimize ligand fit and stability. This cooperative balance between electrostatic and hydrophobic interactions achieves the most favorable binding conformations and affinities, reinforcing the notion that both interaction types are critical for effective ligand recognition and binding to FN1. Additionally, solvation effects modulate binding affinity by influencing desolvation energies and solvent-ligand interactions, further fine-tuning the overall thermodynamics of complex formation and achieving the most favorable binding [Wang et al. 2015 ; Li et al. 2025 ; Cozzini et al. 2004 ]. Hydrogen bonding and interaction profiles Hydrogen bonding has emerged as a key factor of ligand specificity and affinity in FN1 binding interactions. Key residues such as ARG503, SER373, TYR372, GLN330, and GLN409 consistently formed the most hydrogen bonds with top-scoring ligands [Huggins et al. 2016], emphasizing their importance as binding hotspots. In addition to these polar connections, salt bridge interactions involving GLU505 and GLU467 increased electrostatic stability [Kumar et al. 1999], enhancing the ligand binding orientation. Furthermore, aromatic π-π stacking interactions between ligands and TYR372 increased specificity and binding strength. The 3D structural analysis revealed that a hydrophobic pocket including residues LEU407, ALA465, and PHE531 serves as a stabilizing scaffold, enhancing ligand accommodation via van der Waals and hydrophobic packing pressures. Meanwhile, polar residues effectively anchor ligands within the FN1 active site, achieving a balance between flexibility and stability at the binding interface. This extensive network of hydrogen bonds, salt bridges, aromatic interactions, and hydrophobic contacts contributes to the strong ligand recognition and binding affinity seen in the investigated complexes [Xia et al. 2022 ]. ADMET The screened FN1 ligands exhibited moderate lipophilicity (QPlogPo/w 0.07–4.26), with CMNPD21894 and CMNPD15938 being more hydrophobic and CMNPD5805 being the least. Most compounds, particularly CMNPD30363, were poorly soluble. Caco-2 permeability was highest for CMNPD15938 and CMNPD25748, indicating a high absorption potential, whereas CMNPD30283 and CMNPD30363 had extremely low values. All drugs had negative QPlogBB, indicating minimal brain penetration. Human oral absorption was generally satisfactory, with CMNPD30363 rating the highest. Most compounds met Lipinski's criteria, with the exception of CMNPD30283 and CMNPD30363 (Supplementary Table S4). Toxicity profiling found no AMES mutagenicity or hERG I inhibition, but CMNPD25748 demonstrated hERG II inhibition. Hepatotoxicity concerns were discovered in CMNPD15938, CMNPD5805, and CMNPD25748. Acute and chronic toxicity values showed modest safety margins, with CMNPD7718 being the most acceptable in acute trials but slightly harmful.The majority of compounds had negligible environmental toxicity, with the exception of CMNPD30283, which was very poisonous to minnows (Supplementary Table S5). Molecular dynamic simulation study Root mean square deviation (RMSD) Root mean square deviation (RMSD), estimates the structural change of a molecular system over time by measuring the average distance between its atoms from a reference structure [Filipe et al. 2022]. Root Mean Square Deviation (RMSD).The RMSD plots gave information on the overall structural stability of the FN1-ligand complexes across the 100 ns trajectory. CMNPD5805's protein backbone RMSD stabilized around 2.0-2.5 Å after initial equilibration, indicating worldwide conformational stability. The ligand RMSD remained below 2.0 Å, showing that CMNPD5805 was securely bound within the FN1 binding cleft with minimal positional drift. The CMNPD20863 complex had larger RMSD variations, occasionally exceeding 3.0 Å in the early trajectories before settling at 2.5-3.0 Å in the later phases. This shows a slower equilibration process and slightly lesser stability than CMNPD5805, [Hollingsworth et al. 2018; Jones et al. 2022 ] (Fig. 17). Root mean square fluctuation (RMSF) Residue-level flexibility was evaluated using RMSF. Binding-site residues ARG503, SER373, and GLY502 showed modest variations ( 2.0 Å), which is expected for solvent-exposed loops. However, these fluctuations did not alter the ligand-binding pocket. In the CMNPD20863 complex, loop residues around the binding cleft exhibited mild variations (~ 2.0-2.5 Å), but critical binding residues TYR372 and GLN409 remained stable (< 1.5 Å). These findings suggest that both ligands stabilize the core pocket, with CMNPD5805 inducing a more rigid local environment. This stabilization indicates a reduction in dynamic chaos around crucial interaction sites, which promotes ligand binding and complex stability [Hollingsworth et al. 2018] (Fig. 17). Protein-ligand contact timelines Thew protein-ligand contact timeline analysis across the 100-nanosecond simulation determined the persistence and occupancy of crucial ligand-residue interactions. CMNPD5805 maintained constant hydrogen bonds with ARG503 and SER373 for more than 70% of the simulation trajectory, which was aided by transient interactions with GLY502. This substantial occupancy of polar contacts is consistent with the ligand's constant RMSD profile, which reflects long-term binding stability. In contrast, CMNPD20863 showed fewer durable ligand interactions, with hydrogen bonds to TYR372, GLN409, and GLU505 occupying just 40–50% of the trajectory. While these interactions helped to stabilize the complex, their lower durability compared to CMNPD5805 corresponded with a larger ligand RMSD, indicating enhanced mobility within the binding site [De et al. 2016; Amaro et al. 2020] (Fig. 18). Hydrophobic interactions Hydrophobic packing was critical in maintaining the stability of the protein-ligand complexes. In the CMNPD5805 system, LEU407 and ALA465 formed persistent van der Waals contacts with the ligand, resulting in excellent shape complementarity within the FN1 binding pocket. This persistent hydrophobic scaffold substantially reduced ligand mobility, promoting steady binding throughout the simulation. In the CMNPD20863 system, however, PHE531 and surrounding nonpolar residues were predominantly responsible for hydrophobic interactions. These interactions helped to preserve ligand orientation, particularly during periods of low hydrogen bond occupancy. Nonetheless, the amount and durability of hydrophobic contacts in CMNPD20863 were significantly lower than those reported in the CMNPD5805 complex, indicating reduced binding stability [Bissantz et al. 2010 ; Parimal et al. 2015 ] (Fig. 18). 2D interaction maps The 2D interaction maps generated from molecular dynamics trajectories provide a concise summary of the nature and frequency of major protein-ligand interactions. These maps revealed that CMNPD5805 has persistent hydrogen connections with ARG503 and SER373, as well as stable hydrophobic interactions with LEU407 and ALA465. Furthermore, infrequent water-mediated interactions were found, which contributed to improved complex stability throughout the simulation. In contrast, the 2D maps for CMNPD20863 revealed fewer stable hydrogen bonds and a greater reliance on hydrophobic interactions, particularly with PHE531, along with rare electrostatic contacts involving GLU505. This interaction profile indicates that CMNPD20863's binding mode is mostly driven by hydrophobic forces and less by polar stabilization, which is consistent with its larger RMSD values and weaker binding stability when compared to CMNPD5805 [Hosen et al. 2023 ; Dos et al. 2024] (Fig. 19). Discussion T1DM is the leading cause of childhood autoimmune metabolic disorder in globally [Redondo and Morgan, 2023 ]. Due to the extremely complex metabolic disorders that occur in patients with T1DM, once T1DM has reached the terminal stage, it is often more difficult to treat than type 2 diabetes mellitus. Although extensive studies have investigated the molecular pathogenesis of T1DM, it has not been clarified completely [Ilonen et al. 2019 ]. Therefore, it is necessary to find key biomarkers for early diagnosis and targeted therapy of T1DM. Bioinformatics analysis of NGS data for identifying target genes and signaling pathways involved in the occurrence and advancement of T1DM. In this investigation, a series of bioinformatics analysis identified DEGs between T1DM and normal control samples based on NGS data obtained from GSE270484 dataset. DLK1 [Wallace et al. 2010 ], IAPP (islet amyloid polypeptide) [Paulsson et al. 2014 ], INS (insulin) [Tsai et al. 2006 ], IGF2 [Vafiadis et al. 1998 ], THBD (thrombomodulin) [Okano et al. 2021 ], IGFBP3 [Huber et al. 2010 ], SST (somatostatin) [Hernández et al. 2020 ], SPINK1 [Hassan et al. 2002 ], PYY (peptide YY) [Lafferty et al. 2018 ] and NOX4 [Tang et al. 2023 ] were very important to the progression of T1DM. DLK1 [Hong et al. 2025 ], IAPP (islet amyloid polypeptide) [Meier et al. 2014 ], SPP1 [Freiholtz et al. 2023 ], IGF2 [Wu et al .2025], THBD (thrombomodulin) [Conway, 2012 ], IGFBP3 [Lohr et al. 2014 ], SST (somatostatin) [Hernández et al. 2020 ], PYY (peptide YY) [Kamiya et al. 2022 ] and NOX4 [Li et al. 2023 ] might be the main driving factors of inflammation. DLK1 [Kameswaran et al. 2018 ], IAPP (islet amyloid polypeptide) [Alrouji et al. 2023 ], INS (insulin) [Rachdaoui, 2020 ], SPP1 [Xiao et al. 2024 ], IGF2 [Mercader et al. 2017 ], P2RY1 [Dance et al. 2024 ], THBD (thrombomodulin) [Okano et al. 2021 ], IGFBP3 [Liu et al. 2024 ], SST (somatostatin) [Kothegala et al. 2023 ], SPINK1 [Schneider et al. 2005 ], PYY (peptide YY) [Chen et al. 2023 ] and NOX4 [Meng et al. 2018 ] might play an important role in regulating the genetic network related to the occurrence and development of type 2 diabetes mellitus. The expression of DLK1 [Harris et al. 2020 ], INS (insulin) [Zou et al. 2020 ], IGF2 [Soubry et al. 2011 ], IGFBP3 [Mahmood et al. 2016 ], SST (somatostatin) [Fee et al. 2017 ]. SERPINA6 [Chan et al. 2024], PYY (peptide YY) [Tyszkiewicz-Nwafor et al. 2021 ] and NOX4 [Arab et al. 2023 ] might be associated with depression progression. The abnormal expression of DLK1 [Palumbo et al. 2022 ], IAPP (islet amyloid polypeptide) [Geisler et al. 2002 ], INS (insulin) [Czech, 2017 ], IGF2 [Szydlowska-Gladysz et al. 2024 ], IGFBP3 [Haldrup et al. 2023 ], SST (somatostatin) [Kumar and Singh, 2020 ], SPINK1 [Abass et al. 2022 ], PYY (peptide YY) [Lafferty et al. 2018 ] and NOX4 [Greatorex et al. 2023 ] might be related to the progression of obesity. DLK1 [Zeng et al. 2023 ], INS (insulin) [Sowers and Frohlich, 2004 ], SPP1 [Freiholtz et al. 2023 ], P2RY1 [Timur et al. 2012 ], THBD (thrombomodulin) [Khosravi et al. 2021], IGFBP3 [Lee et al. 2025 ], PYY (peptide YY) [Zhu et al. 2015 ] and NOX4 [Vendrov et al. 2015 ] play an important role in the cardiovascular disorders. The abnormal expression of DLK1 [Marquez-Exposito et al. 2021 ], INS (insulin) [Mak, 2008 ], SPP1 [Ding et al. 2024 ], IGFBP3 [Büscher et al. 2012 ], SST (somatostatin) [Messchendorp et al. 2020 ], PYY (peptide YY) [Pérez-Fontán et al. 2008 ] and NOX4 [Li et al. 2023 ] contributes to the progression of kidney disorders. DLK1 [Li et al. 2025 ], INS (insulin) [Singh et al. 2013 ], SPP1 [Morse et al. 2019 ], IGF2 [de Carvalho et al. 2024 ], IGFBP3 [Ahasic et al. 2012 ], SST (somatostatin) [Ramírez-Jarquín et al. 2012 ] and NOX4 [Harijith et al. 2022 ] were a novel key genes in lung disorders. DLK1 [Perramón and Jiménez, 2022 ], SPP1 [Wu et al. 2024 ], IGF2 [Giraudi et al. 2021 ], THBD (thrombomodulin) [Kan et al. 2016 ], IGFBP3 [Haldrup et al. 2023 ], SST (somatostatin) [Aziz et al. 2018 ], SPINK1 [Oruc et al. 2009 ] and NOX4 [Greatorex et al. 2023 ] genes might be related to the pathophysiology of liver diseases. DLK1 [Dai et al. 2021 ], SPP1 [Gazal et al. 2015 ], IGFBP3 [Peet et al. 2015 ] and PYY (peptide YY) [Balog et al. 2021 ] might be the biomarkers for the early diagnosis of autoimmune disorders. The DLK1 [Figeac et al. 2018 ], SPP1 [Chen et al. 2014 ], IGF2 [Wang et al. 2024 ], IGFBP3 [Shi et al. 2023 ], PYY (peptide YY) [Chen et al. 2020 ] and NOX4 [Zhang et al. 2023 ] genes have been shown to be expressed considerably altered in the osteoporosis. Elevated levels of IAPP (islet amyloid polypeptide) [Dubey et al. 2017 ], IGF2 [Yang et al. 2014 ], IGFBP3 [Hu et al. 2022 ], SST (somatostatin) [Aziz et al. 2018 ] and NOX4 [Syed et al. 2023 ] have been associated with oxidative stress. IAPP (islet amyloid polypeptide) [Alrouji et al. 2023 ], INS (insulin) [Ramalingam and Kim, 2014 ], SPP1 [De Schepper et al. 2023 ], IGF2 [Alberini, 2023 ], IGFBP3 [Johansson et al. 2013 ], SST (somatostatin) [Hernández et al. 2020 ] and NOX4 [Boonpraman and Yi, 2024 ] expression has been found to be altered in patients with neurodegenerative disorders. Altered levels of INS (insulin) [Sarafidis, 2007], SPP1 [Chen et al. 2023 ], IGFBP3 [Neto et al. 2021 ], SST (somatostatin) [Troisi et al. 2019 ], PYY (peptide YY) [Zhu et al. 2015 ] and NOX4 [Pavlov et al. 2020 ] have been associated with hypertension. The abnormal expression of SPP1 [Rejas-González et al. 2024 ], IGFBP3 [Romaniuk et al. 2013 ], SST (somatostatin) [Hernández et al. 2020 ] and NOX4 [Meng et al. 2018 ] might be involved in the pathogenesis of eye disorders. INS (insulin) [Ntaios et al. 2014 ], IGF2 [Fei et al. 2025], P2RY1 [Janicki et al. 2017 ], THBD (thrombomodulin) [Zhu et al. 2022 ], IGFBP3 [Armbrust et al. 2017 ], SST (somatostatin) [Chiazza et al. 2021 ], NOX4 [Li et al. 2022 ] and are involved in growth and development of stroke. The above results suggest that these significant DEGs might influence the progression of T1DM. GO and REACTOME pathway enrichment analyses were used to explore the molecular mechanisms of the enriched genes involved in the occurrence and advancement of T1DM. Signaling pathways include neuronal system [Dolatshahi et al. 2023 ], signal transduction [Magrini et al. 2002 ], signaling by receptor tyrosine kinases [Galán et al. 2012 ], neurotransmitter receptors and postsynaptic signal transmission [Pan et al. 2022 ], Neurexins and neuroligins [Suckow et al. 2008 ], muscle contraction [Grotle et al. 2020 ], metabolism [Zhang et al. 2022 ], neutrophil degranulation [Nigi et al. 2025 ] and innate immune system [Needell and Zipris, 2017 ] plays an important role in the pathogenesis of T1DM. Previous studies have reported that the enriched genes include CALD1 [Śnit et al. 2017 ], RBP4 [Pullakhandam et al. 2012 ], PDX1 [Ding et al. 2020 ], EGR1 [Ao et al. 2019 ], GSN (gelsolin) [Noren Hooten et al. 2023 ], NPY (neuropeptide Y) [Skärstrand et al. 2015 ], SPHK1 [Liu et al. 2022 ], GREM1 [Abas et al. 2025 ], SOCS2 [Alkharusi et al. 2016 ], B4GALNT1 [Boraska et al. 2009 ], TRIB3 [Lu et al. 2024 ], RNASEL (ribonuclease L) [Zeng et al. 2014 ], GPNMB (glycoprotein nmb) [Huo et al. 2023 ], ADRB2 [Schouwenberg et al. 2008 ], IGF2BP2 [Gu et al. 2012 ], TNFAIP3 [Cao et al. 2023 ], MST1 [Wu et al. 2022 ], SOX9 [Zhang et al. 2016 ], PCSK9 [Bojanin et al. 2019 ], AREG (amphiregulin) [Raugh et al. 2019], FFAR2 [Shi et al. 2014 ], G6PC2 [Sanda et al. 2013 ], GLP1R [Khera et al. 2019 ], RGS16 [Villasenor et al. 2010 ], GCGR (glucagon receptor) [Lee et al. 2011 ], GLRA3 [Sandholm et al. 2018 ], SLC2A2 [Alhaidan et al. 2020 ], SCD5 [Zámbó et al. 2022 ], CD5 [De Filippo et al. 1997 ], CYP2J2 [Li et al. 2015 ], ENTPD1 [Friedman et al. 2009 ], ITPR3 [Qu et al. 2008 ], CD6 [Do et al. 2024 ], HLA-DQB1 [Singh et al. 2020 ], NPTX2 [Çadırcı et al. 2025 ], SOD3 [Mohammedi et al. 2015 ], ESR2 [Sartoretto et al. 2019 ], CREB5 [Liang et al. 2025 ], AGT (angiotensinogen) [Yang et al. 2025 ], TFPI (tissue factor pathway inhibitor) [Leurs et al. 2003 ], CXCL1 [Takahashi et al. 2011 ], CD36 [Terasaki et al. 2020 ], AGTR1 [Kovacevic et al. 2025 ], TGFBI (transforming growth factor beta induced) [Wu et al. 2023 ], CD74 [Mangano et al. 2024 ], SOCS1 [Kakoki et al. 2021 ], SLC6A4 [Xiu et al. 2015 ], TXNIP (thioredoxin interacting protein) [Basnet et al. 2022 ], BCL6 [McNitt et al. 2025 ], GCG (glucagon) [Leung et al. 2023 ], UCP2 [Rudofsky et al. 2006 ], LPAR1 [Abo El-Magd et al. 2021 ], GPR119 [Bilal et al. 2025 ], IL7 [Hoffmann et al. 2022 ], CD44 [Assayag-Asherie et al. 2015 ], DLL1 [Qu et al. 2021 ], IGFBP2 [Bereket et al. 1995 ], HPSE (heparanase) [Zhou et al. 2025 ], VDR (vitamin D receptor) [Tapia et al. 2019 ], GLB1 [MacFarlane et al. 2003 ], DPP4 [Davanso et al. 2019 ], SLC29A3 [Besci et al. 2022 ], LOXL3 [Huang et al. 2022 ], RFX6 [Şimşek et al. 2024 ], MSH2 [Babić et al. 2022 ], B2M [Monteiro et al. 2016 ], GPER1 [Yao et al. 2024 ], BCHE (butyrylcholinesterase) [Ma et al. 2013 ], CD40 [Vaitaitis et al. 2017 ], LEPR (leptin receptor) [Lee et al. 2006 ], IRF1 [Colli et al. 2018 ], SLC40A1 [Hao et al. 2021 ], SLC22A4 [Santiago et al. 2006 ], DPP4 [Blaslov et al. 2015 ], CDKAL1 [Chistiakov et al. 2011 ], IPPK (inositol-pentakisphosphate 2-kinase) [Looker et al. 2018 ], GLRX (glutaredoxin) [Montano et al. 2015 ] and ALDH2 [He et al. 2021 ] are a key regulators of T1DM. Studies had shown that enriched genes include TGFBR3 [Qianru et al. 2021 ], NTN1 [Mentxaka et al. 2022 ], ALOX5 [Cai et al. 2024 ], FFAR4 [Salaga et al. 2021 ], ADAMTS5 [Sharma et al. 2020 ], RBP4 [Pazos-Pérez et al. 2024 ], PDX1 [Wang et al. 2019 ], FAT1 [Weylandt et al. 2008 ], EGR1 [Lehman et al. 2022 ], GSN (gelsolin) [Piktel et al. 2018 ], GLIPR2 [Wu et al. 2022 ], AGT (angiotensinogen) [Carroll et al. 2013 ], DEPTOR (DEP domain containing MTOR interacting protein) [Shi et al. 2025 ], CARD11 [Yamamoto-Furusho et al. 2018 ], PLCG2 [Tsai et al. 2022 ], CXCL1 [De Filippo et al. 2013 ], PRG4 [Menon et al. 2021 ], CD36 [Zhao et al. 2018 ], AGTR1 [Fung et al. 2011 ] and ENPP2 [Chattopadhyay et al. 2023 ] were associated with inflammation. Abnormal regulation of enriched genes include NTN1 [Chaitra et al. 2025 ], ALOX5 [Nejatian et al. 2019 ], FFAR4 [Chen et al. 2025 ], ADCYAP1 [Gu et al. 2002], CABLES1 [Hetty et al. 2023 ], RBP4 [Pullakhandam et al. 2012 ], OLIG1 [Joshi et al. 2021 ], PDX1 [Lian et al. 2023 ], EGR1 [Ke et al. 2023 ], CRABP2 [Zhang et al. 2025 ], FAIM2 [Kang et al. 2020 ], AGT (angiotensinogen) [Qiao et al. 2018 ], TFPI (tissue factor pathway inhibitor) [El-Hagracy et al. 2010 ], CXCL1 [Sajadi et al. 2013 ], VIPR1 [Tavaglione et al. 2016 ], DNER (delta/notch like EGF repeat containing) [Deng et al. 2015 ], CD36 [Moon et al. 2020 ], AGTR1 [Ihsan et al. 2024 ], CD74 [Chen et al. 2022 ] and SOCS1 [Opazo-Ríos et al. 2020 ] are associated with type 2 diabetes mellitus. Studies have found that enriched genes include ADCYAP1 [Matsuzaki and Tohyama, 2008 ], MAPT (microtubule associated protein tau) [Kimura et al. 2013 ], RBP4 [Yao and Li, 2020 ], OLIG1 [Mosebach et al. 2013 ], FAT1 [Gu et al. 2021 ], EGR1 [Lee and Bondy, 1993 ], CHL1 [Yang et al. 2020 ], NPY (neuropeptide Y) [Ozsoy et al. 2016 ], KCNQ2 [Costi et al. 2 021], CASR (calcium sensing receptor) [Guo et al. 2023 ], AGT (angiotensinogen) [López-León et al. 2008 ], NTRK2 [Paolini et al. 2023 ], CXCL1 [Fanelli et al. 2019 ], CD36 [Bai et al. 2021 ], AGTR1 [Taylor et al. 2013 ], SOCS1 [Sun et al. 2022 ], SLC6A4 [Ho et al. 2013 ], TSPAN8 [Schartner et al. 2017 ], ADCY2 [Aghabozorg et al. 2020] and UCP2 [Du et al. 2016 ] are alterwd expressed in depression. Enriched genes include NTN1 [Mentxaka et al. 2022 ], ALOX5 [Szukiewicz et al. 2025], CCND1 [Thun et al. 2013 ], CABLES1 [Hetty et al. 2023 ], RBP4 [Kilicarslan et al. 2020 ], PDX1 [Kouidrat et al. 2024 ], FAT1 [Tang et al. 2024 ], EGR1 [Ruebel et al. 2016 ], SIX3 [Yu et al. 2024 ], NPY (neuropeptide Y) [Wu et al. 2019 ], FAIM2 [Scharf et al. 2023 ], AGT (angiotensinogen) [Repchuk et al. 2021 ], DEPTOR (DEP domain containing MTOR interacting protein) [Caron et al. 2015 ], NTRK2 [Rask-Andersen et al. 2011 ], CARD11 [Lee et al. 2024 ], TFPI (tissue factor pathway inhibitor) [Vambergue et al. 2001 ], CXCL1 [Zhang et al. 2016 ], PRG4 [Nahon et al. 2019 ], CD36 [Wu et al. 2019 ] and AGTR1 [Adiyeva et al. 2023 ] are altered expression in obesity. Enriched genes include TGFBR3 [Tang et al. 2025 ], ALOX5 [Halade et al. 2022 ], ADAMTS5 [Wang et al. 2019 ], RBP4 [Ji et al. 2023 ], FAT1 [Mao et al. 2021 ], EGR1 [Khachigian, 2024 ], SULF1 [Ji et al. 2024 ], NPY (neuropeptide Y) [Tan et al. 2018 ], DKK3 [Xu et al. 2024 ], CASR (calcium sensing receptor) [Chu et al. 2022 ], GLIPR2 [Yuan et al. 2025 ], FAIM2 [Zhong et al. 2024 ], AGT (angiotensinogen) [Daugherty et al. 2024 ], TFPI (tissue factor pathway inhibitor) [Kaiser et al. 2001 ], PLCG2 [Wang et al. 2024 ], CXCL1 [Wu et al. 2021 ], PRG4 [Zhou et al. 2024 ], CD36 [Shu et al. 2022 ], AGTR1 [Wei et al. 2023 ] and ENPP2 [Karshovska et al. 2022 ] plays an indispensable role in cardiovascular disorders. Enriched genes include TGFBR3 [Caza et al. 2021 ], ALOX5 [Chen et al. 2022 ], FFAR4 [Yang et al. 2022 ], SIX2 [Fogelgren et al. 2009 ], CCND1 [Jiang et al. 2020 ], ADAMTS5 [Taylor et al. 2020 ], CALD1 [Śnit et al. 2017 ], RBP4 [Chen et al. 2024 ], PDX1 [Wiggenhause et al. 2022], FAT1 [Gee et al. 2016 ], GLIPR2 [Baxter et al. 2007 ], AGT (angiotensinogen) [Cruz-López et al. 2022 ], DEPTOR (DEP domain containing MTOR interacting protein) [Wang et al. 2018 ], TFPI (tissue factor pathway inhibitor) [Brook et al. 2025 ], CXCL1 [Li et al. 2024 ], LAMB2 [Trutin et al. 2025 ], CD36 [Li et al. 2015 ], AGTR1 [Al-Maraghi et al. 2024 ], TGFBI (transforming growth factor beta induced) [Dou et al. 2023 ] and CD74 [Zhou et al. 2024 ] could be an early detection biomarkers for kidney disorders. Enriched genes include ALOX5 [Mirra et al. 2023 ], CCND1 [Thun et al. 2013 ], CALD1 [Wu et al. 2024 ], MAPT (microtubule associated protein tau) [Di Fonzo et al. 2014 , RBP4 [Jin et al. 2013 ], EGR1 [Zhao et al. 2025 ], SULF1 [Tu et al. 2024 ], GSN (gelsolin) [Oikonomou et al. 2009 ], NPY (neuropeptide Y) [Itano et al. 2022 ], DKK3 [Zhong et al. 2024 ], FAIM2 [Shen et al. 2018 ], AGT (angiotensinogen) [Marushchak et al. 2019 ], DEPTOR (DEP domain containing MTOR interacting protein) [Wang et al. 2024 ], NTRK2 [Kong et al. 2025 ], TFPI (tissue factor pathway inhibitor) [Fujii et al. 2000 ], RCN3 [Ding et al. 2023 ], CXCL1 [Meng et al. 2018 ], PRG4 [Asfari et al. 2023 ], DNER (delta/notch like EGF repeat containing) [Ballester-López et al. 2019 ] and PTP4A3 [Montuschi and Adcock, 2025 ] expression is a potential targets for lung disorders. Enriched genes include FFAR4 [Jiang et al. 2023 ], CCND1 [Chen et al. 2025 ], RBP4 [Huang and Xu, 2022 ], FAT1 [Zhang et al. 2025 ], EGR1 [Wu et al. 2025 ], SULF1 [Graham et al. 2016 ], GSN (gelsolin) [Leifeld et al. 2006 ], NPY (neuropeptide Y) [Ortiz et al. 2023 ], SPHK1 [Ding et al. 2024 ], GREM1 [Horn et al. 2024 ], AGT (angiotensinogen) [Che et al. 2025 ], DEPTOR (DEP domain containing MTOR interacting protein) [Chen et al. 2018 ], PLCG2 [Gardin et al. 2024 ], CXCL1 [Liu et al. 2025 ], PRG4 [Nahon et al. 2019 ], CD36 [Liu and Yin, 2025 ], AGTR1 [Zhu et al. 2019 ], ENPP2 [Luo and Yu, 2024 ], TGFBI (transforming growth factor beta induced) [Krzistetzko et al. 2023 ], CD74 [Cheng et al. 2025 ] and SOCS1 [Mafanda et al. 2019 ] were frequently altered in liver diseases. Studied have proved that enriched genes include ALOX5 [Cai et al. 2024 ], ADCYAP1 [Cunningham et al. 2007 ], ADAMTS5 [Tsuzaka et al. 2010 ], RBP4 [Toyama et al. 2013 ], PDX1 [Amatya et al. 2018 ], EGR1 [Yang et al. 2025 ], GSN (gelsolin) [Lee et al. 2024 ], NPY (neuropeptide Y) [Bedoui et al. 2003 ], CASR (calcium sensing receptor) [Gavalas et al. 2007 ], HDAC9 [Yan et al. 2011 ], FAIM2 [Sawicka et al. 2020 ], PLCG2 [Szymanski et al. 2018], CXCL1 [Zeng et al. 2021 ], VIPR1 [Abad et al. 2016 ], DNER (delta/notch like EGF repeat containing) [Jarius et al. 2015], CD36 [He et al. 2024 ], UNC93B1 [Wolf et al. 2024 ], PTGER4 [Rahman et al. 2018 ], CD74 [Meza-Romero et al. 2014 ] and SOCS1 [Yu et al. 2024 ] play important roles in development of autoimmune disorders. Enriched genes include CCND1 [Wang and Zhao, 2021 ], RBP4 [Mihai et al. 2019 ], EGR1 [Wang et al. 2023 ], NPY (neuropeptide Y) [Chen et al. 2023 ], CASR (calcium sensing receptor) [Di Nisio et al. 2018 ], DNM3 [Kaur et al. 2024 ], ADRB2 [Krasnova et al. 2025 ], LRP4 [Wang et al. 2025 ], SEMA3A [Zhang et al. 2025 ], PHGDH (phosphoglycerate dehydrogenase) [Wang et al. 2019 ], DEPTOR (DEP domain containing MTOR interacting protein) [Chen et al. 2018 ], CXCL1 [Hu et al. 2020 ], TXNIP (thioredoxin interacting protein) [Peng et al. 2025 ], ALDH1A1 [Jia et al. 2019 ], CSF1 [Batoon et al. 2021 ], CD44 [Sikora et al. 2021 ], MITF (melanocyte inducing transcription factor) [Xue et al. 2024 ], IGFBP2 [Sugimoto et al. 1997 ], VDR (vitamin D receptor) [Gasperini et al. 2023 ] and NNMT (nicotinamide N-methyltransferase) [Yu et al. 2021 ] expression had been confirmed in osteoporosis. Recent study reported that enriched genes include TGFBR3 [Qianru et al. 2021 ], ALOX5 [Luo et al. 2024 ], CCND1 [Zhong et al. 2021 ], MAPT (microtubule associated protein tau) [Bradford et al. 2016], RBP4 [Wang et al. 2022 ], PDX1 [Baumel-Alterzon and Scott, 2022 ], FAT1 [Boyle et al. 2020 ], EGR1 [Pang et al. 2025 ], NPY (neuropeptide Y) [Kuncová et al. 2011 ], DKK3 [Muecklich et al. 2023 ], GLIPR2 [Wu et al. 2022 ], AGT (angiotensinogen) [Marushchak et al. 2019 ], CARD11 [Lu et al. 2025 ], TFPI (tissue factor pathway inhibitor) [Pawlak et al. 2007 ], PLCG2 [Hu et al. 2020 ], CXCL1 [Jiang et al. 2020 ], PRG4 [Zhou et al. 2024 ], CD36 [Liu et al. 2018 ], AGTR1 [Fenty-Stewart et al. 2009 ] and ENPP2 [Fang et al. 2003] were observed to be associated with the risk of oxidative stress. Enriched genes include TGFBR3 [Zhou et al. 2024 ], ALOX5 [Song et al. 2023 ], CCND1 [Dietrich et al. 2022 ], ADAMTS5 [Miguel et al. 2005 ], MAPT (microtubule associated protein tau) [Zhang et al. 2016 ], OLFM1 [Wei et al. 2025 ], PDX1 [Guo et al. 2016 ], CCDC88C [Leńska-Mieciek et al. 2019 ], EGR1 [Guo et al. 2023 ], GSN (gelsolin) [Ma et al. 2006], FAIM2 [Komnig et al. 2016 ], NTRK2 [Tomás et al. 2023 ], TFPI (tissue factor pathway inhibitor) [Piazza et al. 2012 ], PLCG2 [Messenger et al. 2025 ], PLEKHB1 [Marques et al. 2022], CXCL1 [Ma et al. 2025 ], CD36 [Šerý et al. 2020], CD74 [Bryan et al. 2008 ], SOCS1 [Lofrumento et al. 2014 ] and SLC6A4 [Calabrò et al. 2020 ] plays a role in the pathogenesis of neurodegenerative disorders. Enriched genes include SIX2 [Fogelgren et al. 2009 ], RBP4 [Jadhao et al. 2024 ], EGR1 [Laggner et al. 2022 ], NPY (neuropeptide Y) [Baltazi et al. 2011 ], DKK3 [Schäfer et al. 2024], CASR (calcium sensing receptor) [Liu et al. 2022 ], SPHK1 [Yang et al. 2019 ], GREM1 [Meng et al. 2020 ], PCSK1 [Gu et al. 2015 ], TRIB3 [He et al. 2020 ], AGT (angiotensinogen) [Satou et al. 2015 ], NTRK2 [Su et al. 2023 ], TFPI (tissue factor pathway inhibitor) [White et al. 2010 ], RCN3 [He et al. 2022 ], CXCL1 [Hilscher et al. 2019 ], CD36 [Khaleel et al. 2022], AGTR1 [Zeng et al. 2023 ], TGFBI (transforming growth factor beta induced) [Roh et al. 2025 ], SLC6A4 [Zhang et al. 2020 ] and TXNIP (thioredoxin interacting protein) [Wang et al. 2020 ] expression has been found to be increased in patients with hypertension. Elevated levels of enriched genes include TGFBR3 [Xie et al. 2023 ], EGR1 [Li et al. 2008 ], DKK3 [Yu et al. 2025 ], SHISA6 [Oishi et al. 2013 ], TRIB3 [Ung et al. 2024 ], RXRG (retinoid X receptor gamma) [Hsieh et al. 2011 ], GPNMB (glycoprotein nmb) [Huo et al. 2023 ], ECM1 [Cai et al. 2024 ], ADRB2 [Chmielarz-Czarnocińska et al. 2025 ], SEMA3A [Tanaka et al. 2015 ], AGT (angiotensinogen) [Qiao et al. 2018 ], CXCL1 [Wang et al. 2022 ], LAMB2 [Alshamrani et al. 2024 ], PRG4 [Menon et al. 2021 ], CD36 [Lavalette et al. 2020 ], AGTR1 [Durska et al. 2024 ], TGFBI (transforming growth factor beta induced) [Kheir et al. 2019 ], SOCS1 [Ahmed et al. 2024 ], NPHP4 [Wiik et al. 2008 ] and TXNIP (thioredoxin interacting protein) [Singh, 2013 ] have been associated with eye disorders. Enriched genes include ALOX5 [Karuppagounder et al. 2018 ], MAPT (microtubule associated protein tau) [Michalski et al. 2016 ], RBP4 [Wang et al. 2025 ], EGR1 [Li et al. 2020 ], GSN (gelsolin) [Endres et al. 1999 ], NPY (neuropeptide Y) [Dong et al. 2025 ], DKK3 [Zhou et al. 2024 ], HDAC9 [Chiou et al. 2021 ], HDAC9 [Markus, 2023 ], BDH2 [Li et al. 2024 ], FAIM2 [Hu and Lin, 2024 ], AGT (angiotensinogen) [Isordia-Salas et al. 2019 ], NTRK2 [Shi et al. 2020 ], TFPI (tissue factor pathway inhibitor) [Rossouw et al. 2012 ], CXCL1 [Barber et al. 2023 ], AGTR1 [Altarescu et al. 2013 ], CD74 [Yang et al. 2017 ], SOCS1 [Ma et al. 2016 ] and SLC6A4 [Kang et al. 2021 ] have been identified as a key biomarkers in stroke. Our results indicate the importance of enriched genes in the occurrence and development of T1DM. We need to pay attention to the roles of these enriched genes in T1DM. Notably, the PPI network and modules related to T1DM was composed of functional proteins that interacted with each other to participate in biological signal transmission, gene expression regulation, energy and metabolism. Genes were identified as hub genes from PPI network and modules of T1DM. FN1 functions as an ECM scaffold, facilitating collagen deposition and myofibroblast activation as well as promotes fibroblast differentiation [Proctor, 1987 ]. FN1 might be considered as a novel biomarker for T1DM. Hub gene GSN (gelsolin) regulates actin dynamics, insulin secretion, and inflammation in T1DM [Noren Hooten et al. 2023 ]. Hub gene ADRB2 regulates insulin secretion, glucose metabolism, and lipolysis in T1DM [Schouwenberg et al. 2008 ]. CEP128 is a centrosomal and ciliary protein that regulates cell division, polarity, and signaling. Its dysfunction leads to neurological disorders mainly via ciliary signaling disruption and centrosome defect [Lin et al. 2024 ]. CEP128 might be considerd as a novel biomarker for T1DM associated neurodevelopmental disorders. FLNA (Filamin A) mediates disease by altering actin filament dynamics, protein interactions, and mechanotransduction. Specific hub gene FLNA mutations disrupt protein folding and function, leading to structural defects and loss of essential cellular signaling in cardiac defects [Haataja et al. 2019 ]. CEP128 might be considered as a novel biomarker for T1DM associated cardiac defects. Hub gene CD74 potentiates the immune visibility of β-cells, escalating lymphocytic infiltration and targeting for autoimmune attack in T1DM [Mangano et al. 2024 ]. Hub gene EFEMP2’s disruption leads to widespread connective tissue pathologies due to combined defects in elastic fiber and collagen synthesis in aortic aneurysm [Sadeghipour et al. 2024 ]. EFEMP2 might be considered as a novel biomarker for T1DM associated aortic aneurysm. The POU6F2 hub gene encodes a transcription factor with key roles in ocular development. Mutations and dysregulation of POU6F2 leads to. glaucoma [Lin et al. 2024 ]. POU6F2 might be considered as a novel biomarker for T1DM associated glaucoma. Dysregulation of hub gene P4HA2 is implicated in glycolysis in cancer [Wu et al. 2025 ]. P4HA2 might be considered as a novel biomarker for T1DM associated cancer. Hub gene BCL6 expression is controlled by cytokine signaling and costimulatory signals, which are important in the immune networks implicated in T1DM pathogenesis [McNitt et al. 2025 ]. Hub gene GEM loss leads to increased brain injury after ischemic stroke [Takahashi et al. 2021 ]. GEM might be considered as a novel biomarker for T1DM associated stroke. Hub gene CCHCR1 regulates cytoskeletal dynamics critical for centriole duplication and proper mitotic spindle formation, abnormalities in which can leads to psoriasis progression [Tervaniemi et al. 2018 ]. CCHCR1 might be considerd as a novel biomarker for T1DM associated psoriasis. Dysregulation of hub gene SIAE (sialic acid acetylesterase) might influence autoimmune pathogenesis by modifing antigen presentation and helper T cell polarization [Sevdali et al. 2017 ]. SIAE might be considered as a novel biomarker for T1DM associated autoimmune diseases. Dysregulation of hub gene OS-9 can alter HIF-1α levels, impacting hypoxia responses critical in cancer progression [Baek et al. 2005 ]. OS-9 might be considered as a novel biomarker for T1DM associated cancer. NAAA regulates the local concentrations of palmitoylethanolamide (PEA) and anandamide (AEA) signaling molecules impacting in inflammation [Xie et al. 2022 ]. NAAA might be considered as a novel biomarker for T1DM associated inflammation. Recent literature shows TCTN1 molecular mechanisms center on modulation of the Hh pathway in cancer progression [Wang et al. 2015 ].TCTN1 might be considered as a novel biomarker for T1DM associated cancer. Our results showed that hub genes might involved in progression of T1DM. Novel biomarkers responsible for T1DM progression listed in Supplementary Table S6. A miRNA-hub gene regulatory network and miRNA-hub gene regulatory network are a complex biological system where miRNAs and TFs regulate the expression of hub genes in T1DM. The abnormal expression of hsa-miR-142-5p [Collares et al. 2013 ], hsa-miR-501-3p [Pinheiro et al. 2013], hsa-miR-101-3p [Santos et al. 2019 ], NOTCH1 [Wang et al. 2021 ], RUNX1 [Zhong et al. 2022 ] and PPARG [Zusi et al. 2023 ] contributes to the progreesion of T1DM. Novel biomarkers include hsa-mir-657, hsa-miR-200a-5p, hsa-mir-6834-5p, hsa-miR-1266-5p, hsa-mir-373, hsa-miR-33b-3p, hsa-mir-183-5p, CEBPB, HAND2, ASXL1, GTF3C2, AF4, RELA and SUZ12 were might be involved in the pathogenesis of T1DM. Our investigation focused on understanding the action of drug on expression of hub genes. Our findings suggest that drugs- Clenbuterol, Diethylstilbestrol, Minocycline, Exenatide, Phosphatidyl, Selegiline, Isoflurophate, 2-Pyridinethiol, MB07803 and Crotonaldehyde concurrently target to hub genes include ADRB2, ESR2, ALOX5, GLP1R, PRKCA, MAOB, BCHE, CTSB, FBP1 and ALDH2, potentially controlling the development of T1DM. The docking analyses consistently revealed CMNPD30283 as the most potent FN1 inhibitor, which is stabilized by electrostatic anchoring to ARG503 and GLN330 and hydrophobic interactions with LEU407 and PHE531. CMNPD30363 and CMNPD30456 both had positive binding free energies, although CMNPD5805 showed adaptability in IFD, indicating its potential as a flexible binder. Across all ligands, residues ARG503, SER373, TYR372, GLN330, and GLN409 revealed as conserved hotspots for hydrogen bonding and electrostatic interactions, which were supplemented by hydrophobic packing from LEU407 and PHE531. Although solvation penalties were positive, they were balanced out by substantial Coulombic and van der Waals contributions, indicating that FN1-ligand binding is mediated by a combination of polar and hydrophobic forces. MD simulations validated these findings, with CMNPD5805 exhibiting low RMSD values, minor residue fluctuations, and sustained hydrogen bonds with ARG503 and SER373, which are maintained by hydrophobic interactions with LEU407 and ALA465. In contrast, CMNPD20863 was somewhat stable, relying on hydrophobic interactions with PHE531 and intermittent polar contacts with GLU505. Recurrent interactions with ARG503, SER373, TYR372, GLN330, GLN409, and PHE531 during docking and MD corroborated their involvement as structural hotspots in FN1-ligand recognition. These findings point to CMNPD30283 as the most promising FN1 inhibitor, with CMNPD5805 as a dynamically stable alternative and CMNPD30363, CMNPD30456, and CMNPD20863 as other candidates of interest. The tiered computational approach which included Glide docking, IFD, MM-GBSA, and MDwas effective not only in evaluating candidate inhibitors but also in understanding the molecular mechanisms underlying FN1 binding, providing a solid foundation for future optimization and experimental validation. In conclusion, the present study identified FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 as key genes in the pathogenesis of T1DM by integrated analysis of NGS dataset. The results of this investigation further provide useful evidence for investigation into molecular mechanisms, selection of biomarkers, and treatment targets exploration of T1DM. However, further in vitro and in vivo analyses experiments are needed to confirm the functional pathways and hub genes linked with T1DM. This work finds CMNPD30283 as the best FN1 inhibitor candidate, validated by docking, IFD, and free-energy studies, whereas CMNPD5805 emerged as a dynamically stable ligand with persistent polar and hydrophobic contacts. Additional compounds, such as CMNPD30363, CMNPD30456, and CMNPD20863, also showed significant binding, lability with different stability. The repeated participation of ARG503, SER373, TYR372, GLN330, GLN409, and PHE531 underscores their importance in ligand recognition. Overall, this integrated computational method establishes a solid platform for choosing marine derived scaffolds while also laying the groundwork for future FN1 inhibitor optimization and experimental validation from in vitro and in vivo findings. Abbreviations T1DM: Type 1 diabetes mellitus DEGs: Differentially expressed genes NGS: Next generation sequencing GEO: Gene expression omnibus GO: Gene ontology PPI: Protein-protein interaction miRNA: Micro ribonuclic acid TF: Transcription factor ROC: Receiver operating characteristic curve FN1: Fibronectin 1 GSN: Gelsolin ADRB2: Adrenoceptor beta 2 CEP128: Centrosomal protein 128 FLNA: Filamin A CD74: CD74 molecule EFEMP2: EGF containing fibulin extracellular matrix protein 2 POU6F2: POU class 6 homeobox 2 P4HA2: Prolyl 4-hydroxylase subunit alpha 2 BCL6: BCL6 transcription repressor Declarations Funding The authors received no financial support for the research Conflict of interest The authors declare that they have no conflict of interest. Ethical approval Not applicable Consent to participate Not applicable Written Consent for publication Not applicable Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE270484) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270484] Code availability Not applicable Author Contributions B. V. - Writing original draft, and review and editing S.P. - Formal analysis and validation V.S. - Resources and investigation K,P. - Investigation and validation C. 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Table 5 MiRNA - hub gene and TF – hub gene topology table Regulation Hub Genes Degree MicroRNA Regulation Hub Genes Degree TF Up FN1 439 hsa-mir-657 Up PRKCA 61 NOTCH1 Up FLNA 424 hsa-miR-200a-5p Up CDK8 61 CEBPB Up PRKCA 274 hsa-miR-142-5p Up GSN 60 RUNX1 Up SRGAP2 258 hsa-mir-6834-5p Up FN1 57 HAND2 Up MAPT 216 hsa-miR-501-3p Up SRGAP2 53 ASXL1 Up GSN 206 hsa-mir-4533 Up VPS37C 48 EOMES Up CREB5 203 hsa-mir-155-3p Up CREB5 48 ISL1 Up VPS37C 191 hsa-mir-6771-3p Up MAPT 44 ESRRB Up CDK8 138 hsa-mir-224-5p Up ADRB2 41 TAL1 Up GEM 125 hsa-mir-3690 Up GEM 39 CRX Up CEP128 111 hsa-mir-103a-3p Up CEP128 30 NFE2L2 Up ADRB2 95 hsa-miR-30d-5p Up FLNA 30 JUN Up FFAR2 48 hsa-miR-431-5p Up CCHCR1 26 SOX9 Up CCHCR1 45 hsa-miR-15b-3p Up FFAR2 24 GATA4 Up CCDC102B 40 hsa-miR-1911-5p Up CCDC102B 15 PHOX2B Down CTNNB1 500 hsa-miR-1266-5p Down BCL6 96 GTF3C2 Down CD44 412 hsa-mir-373 Down CTNNB1 74 AF4 Down ASPH 358 hsa-miR-101-3p Down CD44 63 PPARG Down LGALS3BP 260 hsa-mir-1252-5p Down ASPH 56 RELA Down BCL6 206 hsa-miR-33b-3p Down P4HA2 50 SUZ12 Down PSEN1 188 hsa-mir-183-5p Down UNC93B1 48 STAT1 Down HSPA2 168 hsa-mir-7977 Down PSEN1 44 BRD4 Down UNC93B1 101 hsa-mir-151a-5p Down EFEMP2 39 TFEB Down FANCG 77 hsa-mir-106b-5p Down LGALS3BP 38 HOXC9 Down POU6F2 73 hsa-miR-19b-3p Down HSPA2 37 NR0B1 Down P4HA2 67 hsa-miR-100-3p Down TCTN1 30 GATA3 Down EFEMP2 67 hsa-miR-16-5p Down FANCG 26 MYBL2 Down CD74 67 hsa-miR-7706 Down VTN 26 TFAP2A Down TCTN1 49 hsa-miR-130a-3p Down POU6F2 24 FOXH1 Down VTN 16 hsa-miR-221-3p Down CD74 24 WDR5 Table 6 Drug- hub gene topology table Categeory Gene Degree Drug (one examples) Up ADRB2 65 Clenbuterol Up ESR2 34 Diethylstilbestrol Up ALOX5 15 Minocycline Up GLP1R 6 Exenatide Up PRKCA 5 Phosphatidyl Up SLC15A2 4 Benzylpenicillin Up THBD 2 Ibuprofen Up MAPT 2 Docetaxel Up CTSV 2 3-amino-5-phenylpentane Up TUBB2B 2 CYT997 Up PHGDH 1 NADH Down MAOB 32 Selegiline Down BCHE 28 Isoflurophate Down CTSB 14 2-Pyridinethiol Down FBP1 10 MB07803 Down ALDH2 5 Crotonaldehyde Down HPN 5 Bentiromide Down PPIF 4 Cyclosporine Down QPCT 3 Glutamine t-butyl ester Down B2M 3 Doxycycline Down P4HA2 2 L-Proline Down ASPH 2 L-Aspartic Down PTGER4 2 Misoprostol Down CPE 2 Insulin Human Down PDK3 2 Radicicol Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx SupplementaryTableS4.docx SupplementaryTableS5.docx SupplementaryTableS6.docx Table1To4and7.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7640932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516571840,"identity":"11ffbd62-75e3-48ac-a9c4-05ae96857bd1","order_by":0,"name":"Basavaraj Mallikarjunayya Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Pharmaceutical Chemistry, K.L.E. 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The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/a0f990913ad43c3331653e1f.png"},{"id":91592641,"identity":"b0bd33f9-2278-4066-9d18-10cf8331e2df","added_by":"auto","created_at":"2025-09-18 06:54:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":941349,"visible":true,"origin":"","legend":"\u003cp\u003eGO and REACTOME pathway enrichment analysis for down regulated genes. p \u0026lt; 0.05. Abbreviations: BP, biological process; CC, cell component; MF, molecular function. GO, Gene Ontology; REAC, REACTOME. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/735f727372e0c9e836943ffd.png"},{"id":91592890,"identity":"5d52c332-a31d-403f-894f-546c529f170f","added_by":"auto","created_at":"2025-09-18 07:02:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3004746,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs. Up regulated genes are marked in parrot green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/0627320b595ac622467e05d7.png"},{"id":91592889,"identity":"92e59831-6dce-4ee6-bedc-69238e193a9a","added_by":"auto","created_at":"2025-09-18 07:02:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1196848,"visible":true,"origin":"","legend":"\u003cp\u003eModules 1 was isolated form PPI of up regulated genes. Module 1 has 21 nodes and 46 edges for up regulated genes. Up regulated genes are marked in green\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/fffefd11975b6c283f18bad9.png"},{"id":91592626,"identity":"6b47b161-13b1-464f-9fc2-a2453b6c8e7c","added_by":"auto","created_at":"2025-09-18 06:54:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":598606,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 1. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/4e0405c2a44881b641e91bb0.png"},{"id":91592891,"identity":"4f72b355-81fd-40f5-8d2a-92acd298b0ac","added_by":"auto","created_at":"2025-09-18 07:02:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":697201,"visible":true,"origin":"","legend":"\u003cp\u003eModule 2 was isolated form PPI of up regulated genes. Module 2 has 8 nodes and 14 edges for down regulated genes. Down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/19dd144d1fbb29560272d358.png"},{"id":91592742,"identity":"7ae35166-8a5c-437a-bc9d-9d3a68b7178a","added_by":"auto","created_at":"2025-09-18 06:54:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":588716,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 2. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/d4b0774826ee693b5013cbd0.png"},{"id":91592740,"identity":"30f8710f-0520-4478-876b-d9bd967f8ec1","added_by":"auto","created_at":"2025-09-18 06:54:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3422694,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - miRNA regulatory network. The light purple color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/886b03e8b82f7c719b837cfd.png"},{"id":91592737,"identity":"b8211daa-c612-4971-be97-c0a25af223e5","added_by":"auto","created_at":"2025-09-18 06:54:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":2818867,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - TF regulatory network. The blue color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/fce06edbe7d8a620376cf848.png"},{"id":91592636,"identity":"d4be5e5c-a6ff-4ce1-a36b-57dc63fb0017","added_by":"auto","created_at":"2025-09-18 06:54:18","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":753937,"visible":true,"origin":"","legend":"\u003cp\u003eDrug-hub gene interaction network. The blue color rectangle nodes represent the drug molecule; up regulated genes are marked in green\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/90a99ad960d842feb3586b3f.png"},{"id":91592734,"identity":"703ce20f-ed63-4f06-897e-77a3c92a35f0","added_by":"auto","created_at":"2025-09-18 06:54:19","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1008227,"visible":true,"origin":"","legend":"\u003cp\u003eDrug-hub gene interaction network. The blue color rectangle nodes represent the key drug molecules; down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/835afdac1cf6fe82aa4fd7fc.png"},{"id":91592633,"identity":"561c5f91-af0b-4495-b69b-081927fca5ef","added_by":"auto","created_at":"2025-09-18 06:54:17","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":361246,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) FN1 B) GSN C) ADRB2 D) CEP128 E) FLNA F) CD74 G) EFEMP2 H) POU6F2 I) P4HA2 J) BCL6\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/41d0fc255c8760b964fff437.png"},{"id":91592888,"identity":"398517a2-69ad-402a-bf12-d1bfd9103746","added_by":"auto","created_at":"2025-09-18 07:02:17","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":2378572,"visible":true,"origin":"","legend":"\u003cp\u003e2D docked images of ligands against the FN1 (pdb id: 3M7P) \u0026nbsp;gene\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/8ab2e647bd36ab0281d0a4e8.png"},{"id":91592724,"identity":"7fd31095-7da7-4c63-b136-580d29287e13","added_by":"auto","created_at":"2025-09-18 06:54:19","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":4392285,"visible":true,"origin":"","legend":"\u003cp\u003e3D docked images of ligands against the FN1 (pdb id: 3M7P) \u0026nbsp;gene.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/3c653ced03553a8b3314f7c4.png"},{"id":91592892,"identity":"ec35edf8-f898-4350-9bf6-d322a08baeec","added_by":"auto","created_at":"2025-09-18 07:02:19","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":1744937,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD, RMSF graph of CMNPD5805 (A, C) andCMNPD20863(B, D) against FN1 gene (pdb id: 3M7P) at 100-ns MD simulation.\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/4059e55fde8d1b7a59392052.png"},{"id":91593880,"identity":"043d0c54-0a20-4a64-b9a3-63d793416663","added_by":"auto","created_at":"2025-09-18 07:10:19","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":2479737,"visible":true,"origin":"","legend":"\u003cp\u003eLigand protein contact, ligand protein interaction timeline graph of CMNPD5805 (E, G) andCMNPD20863(F, H) against FN1 gene (pdb id: 3M7P) at 100-ns MD simulation.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/6d516cc9eb7313a9cbae93f5.png"},{"id":91592741,"identity":"cc5b541b-9a55-467a-b4f3-50ed913cad5c","added_by":"auto","created_at":"2025-09-18 06:54:21","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":936227,"visible":true,"origin":"","legend":"\u003cp\u003e2D amino acid interaction protein of CMNPD5805 (I) andCMNPD20863(J) against FN1 gene (pdb id: 3M7P) at 100-ns MD Simulation\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/6c0ef142cbd9fd7af652b077.png"},{"id":91594167,"identity":"46f60749-1603-4dcb-8d77-7c2c77e6071f","added_by":"auto","created_at":"2025-09-18 07:18:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":39676474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/f08c1a80-ef7b-450f-809a-5ac08cef996b.pdf"},{"id":91592894,"identity":"21496b30-e03e-4935-a0ad-300fbef06f61","added_by":"auto","created_at":"2025-09-18 07:02:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13931,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/03fa0dc8fe9bfda5ab1a5d77.docx"},{"id":91592639,"identity":"35b2a6a5-25b6-4ef0-81d2-be1ca1deaf9e","added_by":"auto","created_at":"2025-09-18 06:54:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14040,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/5769ce57b6e51791e3bf0d14.docx"},{"id":91592627,"identity":"e1c237a9-8f84-438f-bed0-e4735fbdcbf8","added_by":"auto","created_at":"2025-09-18 06:54:17","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14045,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/d87c1a213b8737a6ed31d43a.docx"},{"id":91592895,"identity":"f0ddd033-a6f4-4dc7-9cca-9491843ba435","added_by":"auto","created_at":"2025-09-18 07:02:19","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14342,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/f6e706e3d9a737307c7f3532.docx"},{"id":91592732,"identity":"555532fc-ca3c-441a-8f99-0c313149fe5a","added_by":"auto","created_at":"2025-09-18 06:54:19","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14772,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/a18b6f50b39b88113b45ddc3.docx"},{"id":91592635,"identity":"4da55332-a8fe-4cee-8ed5-8d953661cb5f","added_by":"auto","created_at":"2025-09-18 06:54:18","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14311,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS6.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/3311807b6d219f19aebd9397.docx"},{"id":91592897,"identity":"c47812b4-0dc8-437f-aef7-8465cb4257cd","added_by":"auto","created_at":"2025-09-18 07:02:20","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":217166,"visible":true,"origin":"","legend":"","description":"","filename":"Table1To4and7.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640932/v1/c85ea377ffc40efc082fc08e.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrative Gene Target Mapping, RNA Sequencing, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn Silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Study of Marine Derived Molecules for Type 1 Diabetes\u003c/strong\u003e \u003cstrong\u003eMellitus\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 1 diabetes mellitus (T1DM) is a prevalent chronic autoimmune disorder [Gillespie, \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], which affects around 5\u0026ndash;10% of the world\u0026rsquo;s children and adolescents population [Maahs and West, 2010]. T1DM is primarily characterized by autoimmune destruction of insulin-producing β cells in the pancreas by CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells and macrophages infiltrating the islets [Zhang et al. \u003cspan citationid=\"CR583\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. This condition affects absolute insulin production [Desai and Deshmukh, 2016]. It is well known that inflammatory cascades [Blagov et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and oxidative stress [Novoselova et al. \u003cspan citationid=\"CR363\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] of patients with T1DM can be initiated or exacerbated by genetic and certain environmental factors. Therefore, T1DM shares a tight relationship with a number of illnesses, such as type 2 diabetes mellitus [Dabelea et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], depression [Wang et al. \u003cspan citationid=\"CR528\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], neurodegenerative disorders [Satuli-Autere et al. \u003cspan citationid=\"CR431\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], obesity [Vilarrasa et al. \u003cspan citationid=\"CR507\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], hypertension [Ponirakis et al. \u003cspan citationid=\"CR390\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], cardiovascular disorders [Colom et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], kidney disorders [Chowdhury et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], lung disorders [Mameli et al. \u003cspan citationid=\"CR317\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], liver diseases [Memaj and Jornayvaz, \u003cspan citationid=\"CR330\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], eye disorders [Dereci et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], autoimmune disorders [Popoviciu et al. \u003cspan citationid=\"CR391\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], osteoporosis [Khan and Fraser, \u003cspan citationid=\"CR239\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] and stroke [H\u0026auml;gg-Holmberg et al. \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. Despite imminent confirmation providing considerable mechanistic insight into this condition, the exact molecular mechanism of insulin-producing β cells in the pancreas destruction is still being debated. As T1DM has ambiguous molecular pathogenesis and an unacceptable response to treatment, it is necessary to search the molecular mechanism of T1DM to establish effective target treatments.\u003c/p\u003e\u003cp\u003eAt present, the key treatment strategies for T1DM 1) hormone therapies insulin [Mathieu et al. \u003cspan citationid=\"CR326\" class=\"CitationRef\"\u003e2017\u003c/span\u003e] 2) immune-focused therapies include cell-directed interventions [Hagopian et al. \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], cytokine-directed interventions [Dwyer et al. \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] and antigen vaccination [Roep et al. \u003cspan citationid=\"CR414\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] 3) non-immunomodulatory adjunctives include amylin [Martin, \u003cspan citationid=\"CR323\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], sodium-glucose cotransporter inhibitors [Ferrannini, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], GLP-1 receptor agonists [Aroda, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] and verapamil [Xu et al. \u003cspan citationid=\"CR554\" class=\"CitationRef\"\u003e2012\u003c/span\u003e]. However, these treatment methods may cause some adverse effects. However, T1DM might also be able to be caused by many unknown causes, which cannot be well solved by current drug treatment and T1DM is still a complicated incurable endocrine autoimmune disease. Thus, it is necessary for us to utilize bioinformatics and next generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of T1DM.\u003c/p\u003e\u003cp\u003eWith the advancement of NGS technology, integrated bioinformatics provides an effective tool for discovering valuable new biomarker targets and signaling pathways for T1DM [Pujar et al. \u003cspan citationid=\"CR394\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Prashanth et al. \u003cspan citationid=\"CR392\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. Using bioinformatics strategy, CTLA4 [Kavvoura and Ioannidis, \u003cspan citationid=\"CR235\" class=\"CitationRef\"\u003e2005\u003c/span\u003e], PD1 [Ni et al. \u003cspan citationid=\"CR360\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], KIAA0350 [Hakonarson et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], CYP27B1 [Bailey et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], and HLA-B and HLA-A [Nejentsev et al. \u003cspan citationid=\"CR358\" class=\"CitationRef\"\u003e2007\u003c/span\u003e] are significantly associated with new biomarkers of T1DM. Signaling pathways include PI3K/Akt signaling pathway [Camaya et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], AMPK and Akt signaling pathways [Kang et al. \u003cspan citationid=\"CR229\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], CaMKII/NF-κB/TGF-β1 and PPAR-γ signaling pathway [Gbr et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NF-κB and Wnt/β-catenin/GSK3β signaling pathways [Liu et al. \u003cspan citationid=\"CR292\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and AMPK-SREBP signaling pathway [Soetikno et al. \u003cspan citationid=\"CR458\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] might play a vital role in advancement of T1DM. However, there is still a large amount of NGS data related to T1DM to be explored.\u003c/p\u003e\u003cp\u003eHerein, we used GSE270484 [Liu et al. \u003cspan citationid=\"CR296\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] from the Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Clough and Barrett, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] database to create a new NGS dataset to search for differentially expressed genes (DEGs) in T1DM. To uncover the possible functions and enriched pathways, the DEGs in T1DM related DEGs were subjected to gene ontology (GO) and REACTOME pathway enrichment analysis, and protein-protein interaction (PPI) network and module analyses. Subsequently, we constructed a miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene intraction network, which will be helpful in investigation on the regulatory mechanisms of these hub genes. The predictive capability of the hub genes were analyzed by receiver operating characteristic (ROC) curve. Molecular docking, Molecular dynamics simulation and ADMET studies were performed. The investigation probably revealed the molecular pathogenic mechanism and potential therapeutic target of T1DM.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eNext generation sequencing data source\u003c/h2\u003e\u003cp\u003eIn the investigation, NGS dataset GSE270484 [Liu et al. \u003cspan citationid=\"CR296\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] was obtained from GEO (Home\u0026ndash;GEO\u0026ndash;NCBI (nih.gov)) database. GSE270484 was based on the GPL24676 Illumina NovaSeq 6000 (Homo sapiens) platform including 454 T1DM samples (isolated human beta islets) and 504 normal control samples (isolated human beta islets).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of DEGs\u003c/h3\u003e\n\u003cp\u003e\u0026ldquo;Limma\u0026rdquo; R bioconductor package [Ritchie et al. 2014] was utilized to identify the DEGs between the isolated human beta islets of T1DM patients and normal controls. A adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ,|log FC (fold change)| \u0026gt;0.5691 for up regulated genes and |log FC (fold change)| \u0026lt; -0.9219 for down regulated genes were considered statistically significant. The \u0026ldquo;gplot\u0026rdquo; R software package was used to construct a heatmap of the DEGs, and \u0026ldquo;ggplot2\u0026rdquo; R software package was used to establish a volcano plot of the DEGs. The up regulated and down regulated gene lists were sorted by logFC in NGS dataset.\u003c/p\u003e\n\u003ch3\u003eGO and pathway enrichment analyses of DEGs\u003c/h3\u003e\n\u003cp\u003eg:Profiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biit.cs.ut.ee/gprofiler/\u003c/span\u003e\u003cspan address=\"http://biit.cs.ut.ee/gprofiler/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Reimand et al. \u003cspan citationid=\"CR409\" class=\"CitationRef\"\u003e2007\u003c/span\u003e] was utilized to distinguish and enrich the biological attributes, such as biological processes (BP), cellular components (CC), molecular functions (MF) and signaling pathways of important DEGs. Moreover, Gene Ontology (GO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Thomas, \u003cspan citationid=\"CR486\" class=\"CitationRef\"\u003e2017\u003c/span\u003e] and REACTOME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Fabregat et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] pathway enrichment analyses were used to identify the significant pathways. Adjust P-value \u0026lt; .05 was regarded as the cut-off criteria..\u003c/p\u003e\n\u003ch3\u003eConstruction of the PPI network and module analysis\u003c/h3\u003e\n\u003cp\u003ePPI network among all DEGs was established based on an online tool Integrated Interactions Database (IID) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iid.ophid.utoronto.ca/\u003c/span\u003e\u003cspan address=\"https://iid.ophid.utoronto.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Kotlyar et al. \u003cspan citationid=\"CR249\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] interactome and then software Cytoscape software (v3.10.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Shannon et al. 2003] was employed to adjust and visualize PPI networks. Subsequently, we utilized a Network Analyzer plug-in of Cytoscape to determine hub genes according to the node degree [Luo et al. \u003cspan citationid=\"CR306\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], betweenness [Li et al. \u003cspan citationid=\"CR282\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], stress [Gilbert et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and closeness [Li et al. \u003cspan citationid=\"CR273\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] algorithms. Subsequently, the Cytoscape plug-in the PEWCC [Zaki et al. \u003cspan citationid=\"CR574\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] was used to identify the significant modules from PPI network.\u003c/p\u003e\n\u003ch3\u003eConstruction of the miRNA-hub gene regulatory network\u003c/h3\u003e\n\u003cp\u003eFor searching potential interactions between hub genes and micro RNAs (miRNAs), miRNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Fan et al. 2018] used for the identification of hub genes-targeted miRNAs. In this study, miRNet was used to establish a miRNA -hub gene regulatory network. The miRNA-hub gene crosstalk pairs were gained from databases such as TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. Then the miRNA-hub gene regulatory network was visualized by Cytoscape.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the TF-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eFor searching potential interactions between hub genes and transcription factors (TFs), NetworkAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Zhou et al. \u003cspan citationid=\"CR597\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] used for the identification of hub genes-targeted TFs. In this study, NetworkAnalyst was used to establish a TF-hub gene regulatory network. The TF-hub gene crosstalk pairs were gained from database ChEa. Then the TF-hub gene regulatory network was visualized by Cytoscape.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConstruction of the drug-hub gene interaction network\u003c/h3\u003e\n\u003cp\u003eFor searching potential interactions between hub genes and drug molecules, NetworkAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [Zhou et al. \u003cspan citationid=\"CR597\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] used for the identification of hub genes-targeted drugs. In this study, NetworkAnalyst was used to establish a drug-hub gene interaction network. The drug-hub gene crosstalk pairs were gained from database DrugBank. Then the drug-hub gene interaction network was visualized by Cytoscape.\u003c/p\u003e\n\u003ch3\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h3\u003e\n\u003cp\u003eThe multivariate modeling with combined hub genes were used to identify biomarkers with high sensitivity and specificity for T1DM diagnosis. Used one data as training and other data as validation sample iteratively. The ROC curve analysis and area under curve (AUC) of hub genes conducted by the package \u0026ldquo;pROC\u0026rdquo; [Robin et al. \u003cspan citationid=\"CR413\" class=\"CitationRef\"\u003e2011\u003c/span\u003e] was employed to analyze the specificity and diagnostic value of hub genes to T1DM. The AUC was quantified, with AUCs\u0026thinsp;\u0026gt;\u0026thinsp;0.8 having statistical significance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003emolecular docking studies\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eReceptor selection based on gene information\u003c/h2\u003e\u003cp\u003eThe selection of target receptor structures for molecular docking was initiated by identifying genes of interest relevant to the disease condition under study. Genes showing differential expression or known functional involvement in disease pathophysiology were prioritized based on genomic and transcriptomic data [Barab\u0026aacute;si et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Subramanian et al. \u003cspan citationid=\"CR464\" class=\"CitationRef\"\u003e2005\u003c/span\u003e]. The corresponding protein products of these genes were then retrieved using UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and NCBI Gene (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. These databases provide curated information on gene-protein relationships, functional domains, isoforms, and organism-specific variants [UniProt 2023].\u003c/p\u003e\u003cp\u003eOnce the protein names and UniProt accessions were determined, three-dimensional structures of these proteins were searched in the Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Priority was given to experimentally determined structures (X-ray crystallography or cryo-EM) derived from \u003cem\u003eHomo sapiens\u003c/em\u003e, with resolutions\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026Aring; and co-crystallized ligands when available. Structures were evaluated for completeness, presence of functional domains, and biologically relevant binding conformations. In cases where multiple structures were available, the one with the most complete and biologically relevant ligand-protein interaction site was selected [Berman et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Plewczynski et al. \u003cspan citationid=\"CR389\" class=\"CitationRef\"\u003e2011\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIf co-crystallized ligands were present in the selected PDB entry, these were examined to confirm their biological relevance (e.g. substrate, inhibitor, agonist). The functional nature of the ligand was cross-validated using ligand bioactivity databases such as ChEMBL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/chembl/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/chembl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and BindingDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bindingdb.org/\u003c/span\u003e\u003cspan address=\"https://www.bindingdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), where half-maximal inhibitory concentration (IC₅₀), binding affinity (K\u003csub\u003ei\u003c/sub\u003e), or other pharmacological data are reported [Gaulton et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR295\" class=\"CitationRef\"\u003e2007\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo confirm the presence and accessibility of druggable binding pockets, cavity detection tools such as CASTp, Fpocket, or DoGSiteScorer were used [Tian et al. \u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Le et al. 2009]. This ensured that the selected structure was suitable for molecular docking, with a validated and accessible ligand-binding domain. Overall, starting from gene selection to receptor structure identification, ensured biological relevance, structural accuracy, and docking compatibility of the chosen targets. The downloaded protein was prepared through a number of steps, including import and refining, review and modification, and minimization to fill in missing residues and chain sides. The protein's essential binding pocket remained unchanged. The OPLS3e (Optimized potential for liquid simulation) force field was used to reduce protein energy, resulting in a low-energy state protein.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMaterials and techniques\u003c/h2\u003e\u003cp\u003eThe Maestro Schr\u0026ouml;dinger suite 2025-1(developed by Schr\u0026ouml;dinger, LLC, New York) was used for computational research. The study used tools such as Protein Preparation Wizard, Ligprep, GLIDE, Desmond, Prime, and WaterMap.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProtein preparation and grid generation\u003c/h2\u003e\u003cp\u003eThere were numerous PDB structures for FN1, but we choose and obtained the 3D structure of the protein with PDB id 3M7P for FN1. Then pre-processed and minimized the protein using the Maestro, Schrodinger suite's protein preparation wizard. Heavy atoms and water molecules were eliminated, missing side chains and amino acids were filled, and restricted minimization was used to create the least energy protein structure at neutral pH. During protein processing, all the essential amino acids are kept in the protein structure. The site map was used to create the grid for the FN1 based on active druggable site (Residue numbers: Chain A 323,330,345,347,362,363,369,371,372,373,374,375,376,377,384,401,402,403, 404,405,406,407,408,409,411,422,433,461,462,463,464,465,467,501,502,503,505,524,531,532,535,536,537,539,596) [Tian et al. \u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e2018\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLigand preparation\u003c/h2\u003e\u003cp\u003eMarine-derived chemical compounds (47,451 in total) were retrieved from the CMNPD database. These ligands were prepared using the \u003cem\u003eLigPrep\u003c/em\u003e module of the Schr\u0026ouml;dinger Maestro suite. During preparation, the lowest-energy 3D structures were generated with appropriate assignment of chirality, tautomeric states, ring conformations, stereochemistry, and ionization states. The optimization was performed under the OPLS4 force field at neutral pH conditions to ensure physiologically relevant protonation states [Chen et al. 2010; Mili et al. \u003cspan citationid=\"CR280\" class=\"CitationRef\"\u003e2024\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePhysicochemical Screening\u003c/h2\u003e\u003cp\u003eAfter ligand preparation, the marine-derived compounds were carefully screened for their physicochemical properties using the Schr\u0026ouml;dinger suite. To begin with, Lipinski\u0026rsquo;s rule of five was applied to evaluate their drug-likeness and oral bioavailability. The \u003cem\u003eQikProp\u003c/em\u003e tool was then used to predict key ADME characteristics, such as molecular weight, lipophilicity (logP), hydrogen bond donors and acceptors, along with other pharmacokinetic parameters. To further refine the dataset, filters for reactive functional groups were employed to exclude unstable or potentially toxic structures. Through this stepwise screening strategy, we retained a set of chemically stable and pharmacologically promising molecules suitable for downstream computational analyses. After extensive physicochemical filtering, a refined subset of 10,497 molecules was obtained from the initial library. These compounds met the required drug-likeness, stability, and ADME-related criteria, and were therefore considered suitable for subsequent computational investigations.The evaluation also took into account human oral absorption. Using the pkCMS webtool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/pkcsm\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/pkcsm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) we predicted the toxicity profile of all chemicals [Klimoszek et al. 2024]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking\u003c/h2\u003e\u003cp\u003eFrom the refined library of 10,497 ligands, molecular docking was performed using the \u003cem\u003eGlide\u003c/em\u003e module in the Schr\u0026ouml;dinger suite 2025-1. A stepwise docking workflow was then followed to combine speed with precision. In the first stage, all compounds were screened using High-Throughput Virtual Screening (HTVS) mode, which enabled rapid evaluation of the large dataset. The top 10% of hits from HTVS were advanced to Standard Precision (SP) docking, where binding poses and affinities were assessed in greater detail. Finally, the best 10% of SP results were subjected to Extra Precision (XP) docking. The XP mode applied stricter scoring functions and more refined sampling, helping to eliminate false positives and identify the most promising candidates. This tiered strategy progressively narrowed the library to a smaller set of ligands with strong predicted binding affinity and reliable interaction profiles, suitable for further computational validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMM-GBSA analysis\u003c/h2\u003e\u003cp\u003eFurther Prime-Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) [Guimar\u0026atilde;es et al. 2008; Wang et al. \u003cspan citationid=\"CR528\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] was used to determine the relative binding energy of the XP docked protein-ligand complexes for both proteins. Under the OPLS4 force field, Prime MM-GBSA employs the VSGB solvation model, which relies on the variable-dielectric generalized Born model and solvent as water. The strength with which the ligand binds to the chosen protein can also be predicted using binding energy.\u003c/p\u003e\u003cp\u003eThe following formula is used to determine the binding free energy of each chosen protein-ligand complex:\u003c/p\u003e\u003cp\u003e∆G bind\u0026thinsp;=\u0026thinsp;G complex- (G protein\u0026thinsp;+\u0026thinsp;G ligand).\u003c/p\u003e\u003cp\u003eWhere, G\u0026thinsp;=\u0026thinsp;MME (molecular mechanics energy)\u0026thinsp;+\u0026thinsp;GSGB (SGB salvation model for polar solvation)\u0026thinsp;+\u0026thinsp;GNP (nonpolar solvation).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInduced fit docking\u003c/h2\u003e\u003cp\u003eLeads (Best 10 molecules) were chosen for IFD [Sherman et al. \u003cspan citationid=\"CR443\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sherman et al. \u003cspan citationid=\"CR443\" class=\"CitationRef\"\u003e2006\u003c/span\u003e] based on the XP docking score, interaction created, MM-GBSA, and ADMET. During docking, IFD considers the ligand's and receptor's flexibility. It describes how a ligand's binding to a receptor causes dynamic changes in its structure. By enabling the receptor to modify its binding position to more closely match the ligand, it improves the precision of predicted binding interactions. This method increases the success rate of structure-based drug design by considering the dynamic nature of protein-ligand interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMD Simulation\u003c/h2\u003e\u003cp\u003eOne crucial computational technique for examining the dynamic behaviour of ligand-protein complexes is the use of molecular dynamic (MD) simulations. The limitation of protein rigidity in XP-docking is one of the many benefits that MD simulations provide over conventional XP-docking. The protein-ligand complex is more dynamic and flexible in MD simulations, which enables ligands to change their conformation inside the protein's active site. The stability of the protein-ligand interaction is evaluated in a simulated aqueous environment, which is similar to real biological systems. Docking scores, binding energies and non-bonding interactions with significant amino acid residueswere used to select the top two potential candidates for MD simulations with both proteins. During MD simulation protein and ligands were suspended in water, molecular docking and IFD experiments don't replicate the biological milieu of the body; MDS is utilized to overcome this issue. Two hits for each protein were chosen for MD simulation using Schrodinger\u0026rsquo;s Desmond module [Saadabadi et al. \u003cspan citationid=\"CR420\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] in the current investigation based on the findings of MMGBSA, binding interactions, XP and induced fit docking results. Three steps were included in the MD simulation process: system builder, minimization, and MD simulation. The entire system is submerged in a single point charge (SPC) solvent model as part of the MDS procedure. The boundary condition was maintained in its orthorhombic form throughout the system development process with dimensions fixed at a, b, and c at 10 A\u003csup\u003e0\u003c/sup\u003e and angles α, β, and γ at 90\u0026deg;. The buffer box size is determined by using a technique used during the construction process. The system builder tool makes use of the OPLS4 force field, and for system minimizing, the minimization tool was employed. After that, MDS was run for 100 ns, producing one frame from the trajectory for every 100 picoseconds, for a total of 1000 frames produced during the MDS process.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of DEGs\u003c/h2\u003e\u003cp\u003eThe DEGs were screened by \u0026ldquo;limma\u0026rdquo; package (adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ,|log FC (fold change)| \u0026gt;0.5691 for up regulated genes and |log FC (fold change)| \u0026lt; -0.9219 for down regulated genes). The GSE270484 NGS dataset contained 958 DEGs, including 479 up regulated genes and 479 down regulated genes (Table\u0026nbsp;1). The DEGs of the NGS dataset are shown in volcano plot Fig.\u0026nbsp;1, and the heatmap of the DEGs is shown in Fig.\u0026nbsp;2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eGO and pathway enrichment analyses of DEGs\u003c/h2\u003e\u003cp\u003eThen, GO and REACTOME pathway enrichment analyses were implemented with the \u0026ldquo;g:Profiler\u0026rdquo; online tool to explore the potential biofunction of DEGs. The findings revealed that the most enriched GO keywords were associated with developmental process, biological regulation, response to stimulus and multicellular organismal process (BP); membrane, cytoplasm, cell periphery and endomembrane system (CC); cation binding, small molecule binding, ion binding and catalytic activity (MF) (Table\u0026nbsp;2, and Fig.\u0026nbsp;3 to Fig.\u0026nbsp;4). According to the REACTOME pathway enrichment study, DEGs have a significant role in neuronal system, GPCR downstream signalling, metabolism and transport of small molecules (Table\u0026nbsp;3, and Fig.\u0026nbsp;3 to Fig.\u0026nbsp;4 ).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eConstruction of the PPI network and module analysis\u003c/h2\u003e\u003cp\u003eTo reveal the interaction of each protein, the PPI network of the DEGs were built according to the IID database. There were 8412 edges and 13220 nodes in Fig.\u0026nbsp;5, followed by analysis using Cytoscape software. The cytoHubba plugin of Cytoscape was used to score each node gene by four selected algorithms, including node degree, betweenness, stress and closeness. The results shown that FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 were the most hub gene with the highest node degree, betweenness, stress and closeness (Table\u0026nbsp;4). Furthermore, the two significant modules were extracted from the PPI network. Module 1 contained 21 gene nodes, including DEUP1, GEM and CCHCR1 with 46 edges (Fig.\u0026nbsp;6). The hub genes in module 1 involved in developmental process, biological regulation and cytoplasm (Fig.\u0026nbsp;7). Module 2 contained 8 gene nodes, including SIAE, OS9, NAAA and TCTN1 with 14 edges (Fig.\u0026nbsp;8). The hub genes in module 2 involved in catalytic activity, transport of small molecules and response to stimulus (Fig.\u0026nbsp;9).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the miRNA-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eThe miRNet database was used to anticipate and visualize the miRNA- hub gene interaction of hub genes. The miRNA-hub gene regulatory network consisted of 2494 (miRNA: 2193; Hub Gene: 301) nodes and 34835 edges (Fig.\u0026nbsp;10). FN1 was interacted with 439 miRNAs (ex; hsa-mir-657); FLNA was interacted with 424 miRNAs (ex; hsa-miR-200a-5p); PRKCA was interacted with 274 miRNAs (ex; hsa-miR-142-5p); SRGAP2 was interacted with 258 miRNAs (ex; hsa-mir-6834-5p); MAPT was interacted with 216 miRNAs (ex; hsa-miR-501-3p); CTNNB1 was interacted with 500 miRNAs (ex; hsa-miR-1266-5p); CD44 was interacted with 412 miRNAs (ex; hsa-mir-373); ASPH was interacted with 358 miRNAs (ex; hsa-miR-101-3p); LGALS3BP was interacted with 260 miRNAs (ex; hsa-mir-1252-5p); BCL6 was interacted with 206 miRNAs (ex; hsa-miR-33b-3p) (Table\u0026nbsp;5).\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eConstruction of the TF-hub gene regulatory network\u003c/h2\u003e\u003cp\u003eThe Network Analyst database was used to anticipate and visualize the TF- hub gene interaction of hub genes. The TF-hub gene regulatory network consisted of 573 (TF: 271; HUB Gene: 302) nodes and 11306 edges (Fig.\u0026nbsp;11). PRKCA was interacted with 61 TFs (ex; NOTCH1); CDK8 was interacted with 61 TFs (ex; CEBPB); GSN was interacted with 60 TFs (ex; RUNX1); FN1 was interacted with 57 TFs (ex; HAND2); SRGAP2 was interacted with 53 TFs (ex; ASXL1); BCL6 was interacted with 96 TFs (ex; GTF3C2); CTNNB1 was interacted with 74 TFs (ex; AF4); CD44 was interacted with 63 TFs (ex; PPARG); ASPH was interacted with 56 TFs (ex; RELA); P4HA2 was interacted with 50 TFs (ex; SUZ12) (Table\u0026nbsp;5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eConstruction of the drug-hub gene interaction network\u003c/h2\u003e\u003cp\u003eThe Network Analyst database was used to anticipate and visualize the drug- hub gene interaction of hub genes. For up regulated genrs include ADRB2 hub gene was interacted with 65 drug molecules were (ex; Clenbuterol), ESR2 hub gene was interacted with 34 drugs (ex; Diethylstilbestrol), ALOX5 hub gene was interacted with 15 drugs (ex; Minocycline), GLP1R hub gene was interacted with 15 drugs (ex; Exenatide) and PRKCA hub gene was interacted with 5 drugs (ex; Phosphatidyl) (Fig.\u0026nbsp;12 and Table\u0026nbsp;6), while down regulated genrs include MAOB hub gene was interacted with 32 drugs (ex; Selegiline), BCHE hub gene was interacted with 28 drugs (ex; Isoflurophate), CTSB hub gene was interacted with 14 drugs (ex; 2-Pyridinethiol), FBP1 hub gene was interacted with 10 drugs (ex; MB07803) and ALDH2 hub gene was interacted with 5 drugs (ex; Crotonaldehyde) (Fig.\u0026nbsp;13 and Table\u0026nbsp;6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h2\u003e\u003cp\u003eBesides, ROC curves were performed and the corresponding AUC was calculated to validate the diagnostic value of hub genes. The diagnostic value of hub genes in T1DM samples and normal control samples are as follow: FN1 (AUC:0.924), GSN (AUC:0.916), ADRB2 (AUC:0.929), CEP128 (AUC:0.898), FLNA (AUC:0.907), CD74 (AUC:0.920), EFEMP2 (AUC:0.938), POU6F2 (AUC:0.884), P4HA2 (AUC:0.933) and BCL6 (AUC:0.893) (Fig.\u0026nbsp;14). Therefore, we hypothesise that FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 might be biomarkers For T1DM.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking and binding affinity study\u003c/h2\u003e\u003cp\u003eMolecular docking of marinederived ligands against FN1 (PDB ID: 3M7P) was performed using the Schr\u0026ouml;dinger suite's Glide module in a three-tier workflow: (i) High-Throughput Virtual Screening (HTVS) for rapid filtering, (ii) Standard Precision (SP) docking for refined ranking, and (iii) Extra Precision (XP) docking for high-accuracy binding pose prediction. This sequential strategy is widely viewed as vital in decreasing false positives and assuring dependability in large-scale screening [Fusani et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2020\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn XP mode, the top-ranked ligands' docking scores varied from \u0026minus;\u0026thinsp;9.898 to -8.253 kcal/mol (Supplementary Table S1). Among these, CMNPD30283 demonstrated the greatest binding (-9.898 kcal/mol), forming hydrogen bonds with LEU407, GLN330, and ARG503. ARG503 was regularly found as an anchoring residue in many of the top-scoring ligands.CMNPD21894 (-9.014 kcal/mol) displayed robust binding, sustained by interactions with GLY502, ARG503, and SER373, whereas CMNPD15938 (-8.795 kcal/mol) engaged TYR372, PRO363, and GLN409, highlighting contributions from π-π stacking and hydrogen bonding.\u003c/p\u003e\u003cp\u003eHydrophobic residues (LEU407, ALA465, and PHE531) were observed to offer shape complementarity, whereas polar residues (GLN409, GLU505, and GLN330) mediated electrostatic stabilization and ARG503, TYR372, SER373, GLN330, and GLN409 across various complexes suggests that these residues represent a conserved binding hotspot in FN1. The 2D interaction graphs (Fig.\u0026nbsp;15) demonstrated the preponderance of polar and electrostatic interactions, whereas 3D binding poses (Fig.\u0026nbsp;16) indicated profound ligand embedding within the FN1 cleft.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eInduced fit docking (IFD)\u003c/h2\u003e\u003cp\u003eWhile Glide XP makes good predictions, it expects a solid receptor structure. To capture protein adaptability, induced fit docking (IFD) was used. IFD takes into consideration side-chain rearrangements and backbone flexibility, providing a more accurate picture of ligand accommodation [Sherman et al. \u003cspan citationid=\"CR443\" class=\"CitationRef\"\u003e2006\u003c/span\u003e].The IFD results verified CMNPD30283 as the leading candidate, having the best IFD dock score (-11.918 kcal/mol). This demonstrates enhanced complementarity between the ligand and FN1 following induced modifications. Other top performers were CMNPD21894 (-10.723 kcal/mol), CMNPD28752 (-10.693 kcal/mol), and CMNPD15938 (-10.246 kcal/mol), which stabilized their poses via adaptive interactions with ARG503, GLU505, and SER373, CMNPD5805 (-10.246). The latter generated adaptive hydrogen bonds with ARG503, SER373, and GLY502, demonstrating its ability to remain stable within a dynamically modified pocket. These findings highlight CMNPD5805 as a promising contender in addition to the top-ranked ligands. In contrast, CMNPD23529 (-8.819 kcal/mol) and CMNPD30363 (-8.973 kcal/mol) had lower IFD scores, indicating less flexibilitydriven accommodation.Overall, IFD demonstrated that FN1 gene active site physically changes to accommodate specific ligands, increasing the dependability of docking predictions (Supplementary Table S2).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrime MM-GBSA analysis on free energy and energy contributions\u003c/h3\u003e\n\u003cp\u003eTo improve docking predictions, Prime MM-GBSA simulations were used to determine binding free energies (ΔG_bind) and break them down into Coulombic, van der Waals (vdW), lipophilic, and solvation contributions (Supplementary Table S3). Prime MM-GBSA analysis refined estimates of binding stability by breaking down ΔG_bind into major energy components. The binding free energy of the screened ligands ranged from \u0026minus;\u0026thinsp;53.46 to -38.20 kcal/mol, with three ligands forming the most stable complexes. CMNPD30283 had the highest favorable binding free energy (-53.46 kcal/mol), which was mostly due to strong electrostatic forces (Coulombic) and substantial van der Waals packing. These stabilizing contributions overcame the positive solvation penalty, resulting in an overall very favourable binding profile. Similarly, CMNPD30363 (-49.75 kcal/mol) and CMNPD30456 (-48.90 kcal/mol) were the next most stable binders, both stabilized by a synergistic mix of Coulombic contacts and van der Waals forces. The binding analysis reveals a synergistic mix of Coulombic contacts and van der Waals forces as the primary stabilizing influences within the FN1-ligand complexes. This energetic trend was consistently observed, with ligands exhibiting strong electrostatic anchoring to key residues such as ARG503 and GLN330, providing essential hydrogen bonds and salt bridge interactions This polar anchoring is complemented by deeper hydrophobic engagement involving residues like LEU407 and PHE531, which contribute through van der Waals interactions and hydrophobic packing to optimize ligand fit and stability.\u003c/p\u003e\u003cp\u003eThis cooperative balance between electrostatic and hydrophobic interactions achieves the most favorable binding conformations and affinities, reinforcing the notion that both interaction types are critical for effective ligand recognition and binding to FN1. Additionally, solvation effects modulate binding affinity by influencing desolvation energies and solvent-ligand interactions, further fine-tuning the overall thermodynamics of complex formation and achieving the most favorable binding [Wang et al. \u003cspan citationid=\"CR519\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR272\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cozzini et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2004\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eHydrogen bonding and interaction profiles\u003c/h2\u003e\u003cp\u003eHydrogen bonding has emerged as a key factor of ligand specificity and affinity in FN1 binding interactions. Key residues such as ARG503, SER373, TYR372, GLN330, and GLN409 consistently formed the most hydrogen bonds with top-scoring ligands [Huggins et al. 2016], emphasizing their importance as binding hotspots. In addition to these polar connections, salt bridge interactions involving GLU505 and GLU467 increased electrostatic stability [Kumar et al. 1999], enhancing the ligand binding orientation. Furthermore, aromatic π-π stacking interactions between ligands and TYR372 increased specificity and binding strength.\u003c/p\u003e\u003cp\u003eThe 3D structural analysis revealed that a hydrophobic pocket including residues LEU407, ALA465, and PHE531 serves as a stabilizing scaffold, enhancing ligand accommodation via van der Waals and hydrophobic packing pressures. Meanwhile, polar residues effectively anchor ligands within the FN1 active site, achieving a balance between flexibility and stability at the binding interface. This extensive network of hydrogen bonds, salt bridges, aromatic interactions, and hydrophobic contacts contributes to the strong ligand recognition and binding affinity seen in the investigated complexes [Xia et al. \u003cspan citationid=\"CR548\" class=\"CitationRef\"\u003e2022\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eADMET\u003c/h2\u003e\u003cp\u003eThe screened FN1 ligands exhibited moderate lipophilicity (QPlogPo/w 0.07\u0026ndash;4.26), with CMNPD21894 and CMNPD15938 being more hydrophobic and CMNPD5805 being the least. Most compounds, particularly CMNPD30363, were poorly soluble. Caco-2 permeability was highest for CMNPD15938 and CMNPD25748, indicating a high absorption potential, whereas CMNPD30283 and CMNPD30363 had extremely low values. All drugs had negative QPlogBB, indicating minimal brain penetration. Human oral absorption was generally satisfactory, with CMNPD30363 rating the highest. Most compounds met Lipinski's criteria, with the exception of CMNPD30283 and CMNPD30363 (Supplementary Table S4).\u003c/p\u003e\u003cp\u003eToxicity profiling found no AMES mutagenicity or hERG I inhibition, but CMNPD25748 demonstrated hERG II inhibition. Hepatotoxicity concerns were discovered in CMNPD15938, CMNPD5805, and CMNPD25748. Acute and chronic toxicity values showed modest safety margins, with CMNPD7718 being the most acceptable in acute trials but slightly harmful.The majority of compounds had negligible environmental toxicity, with the exception of CMNPD30283, which was very poisonous to minnows (Supplementary Table S5).\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eMolecular dynamic simulation study\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eRoot mean square deviation (RMSD)\u003c/h2\u003e\u003cp\u003eRoot mean square deviation (RMSD), estimates the structural change of a molecular system over time by measuring the average distance between its atoms from a reference structure [Filipe et al. 2022]. Root Mean Square Deviation (RMSD).The RMSD plots gave information on the overall structural stability of the FN1-ligand complexes across the 100 ns trajectory. CMNPD5805's protein backbone RMSD stabilized around 2.0-2.5 \u0026Aring; after initial equilibration, indicating worldwide conformational stability. The ligand RMSD remained below 2.0 \u0026Aring;, showing that CMNPD5805 was securely bound within the FN1 binding cleft with minimal positional drift. The CMNPD20863 complex had larger RMSD variations, occasionally exceeding 3.0 \u0026Aring; in the early trajectories before settling at 2.5-3.0 \u0026Aring; in the later phases. This shows a slower equilibration process and slightly lesser stability than CMNPD5805, [Hollingsworth et al. 2018; Jones et al. \u003cspan citationid=\"CR222\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] (Fig.\u0026nbsp;17).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eRoot mean square fluctuation (RMSF)\u003c/h3\u003e\n\u003cp\u003eResidue-level flexibility was evaluated using RMSF. Binding-site residues ARG503, SER373, and GLY502 showed modest variations (\u0026lt;\u0026thinsp;1.5 \u0026Aring;) in the CMNPD5805 complex, indicating their stability throughout the trajectory. Peripheral loop sections showed slightly increased flexibility (\u0026gt;\u0026thinsp;2.0 \u0026Aring;), which is expected for solvent-exposed loops. However, these fluctuations did not alter the ligand-binding pocket. In the CMNPD20863 complex, loop residues around the binding cleft exhibited mild variations (~\u0026thinsp;2.0-2.5 \u0026Aring;), but critical binding residues TYR372 and GLN409 remained stable (\u0026lt;\u0026thinsp;1.5 \u0026Aring;). These findings suggest that both ligands stabilize the core pocket, with CMNPD5805 inducing a more rigid local environment. This stabilization indicates a reduction in dynamic chaos around crucial interaction sites, which promotes ligand binding and complex stability [Hollingsworth et al. 2018] (Fig.\u0026nbsp;17).\u003c/p\u003e\n\u003ch3\u003eProtein-ligand contact timelines\u003c/h3\u003e\n\u003cp\u003eThew protein-ligand contact timeline analysis across the 100-nanosecond simulation determined the persistence and occupancy of crucial ligand-residue interactions. CMNPD5805 maintained constant hydrogen bonds with ARG503 and SER373 for more than 70% of the simulation trajectory, which was aided by transient interactions with GLY502. This substantial occupancy of polar contacts is consistent with the ligand's constant RMSD profile, which reflects long-term binding stability. In contrast, CMNPD20863 showed fewer durable ligand interactions, with hydrogen bonds to TYR372, GLN409, and GLU505 occupying just 40\u0026ndash;50% of the trajectory. While these interactions helped to stabilize the complex, their lower durability compared to CMNPD5805 corresponded with a larger ligand RMSD, indicating enhanced mobility within the binding site [De et al. 2016; Amaro et al. 2020] (Fig.\u0026nbsp;18).\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eHydrophobic interactions\u003c/h2\u003e\u003cp\u003eHydrophobic packing was critical in maintaining the stability of the protein-ligand complexes. In the CMNPD5805 system, LEU407 and ALA465 formed persistent van der Waals contacts with the ligand, resulting in excellent shape complementarity within the FN1 binding pocket. This persistent hydrophobic scaffold substantially reduced ligand mobility, promoting steady binding throughout the simulation. In the CMNPD20863 system, however, PHE531 and surrounding nonpolar residues were predominantly responsible for hydrophobic interactions. These interactions helped to preserve ligand orientation, particularly during periods of low hydrogen bond occupancy. Nonetheless, the amount and durability of hydrophobic contacts in CMNPD20863 were significantly lower than those reported in the CMNPD5805 complex, indicating reduced binding stability [Bissantz et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Parimal et al. \u003cspan citationid=\"CR377\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] (Fig.\u0026nbsp;18).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2D interaction maps\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 2D interaction maps generated from molecular dynamics trajectories provide a concise summary of the nature and frequency of major protein-ligand interactions. These maps revealed that CMNPD5805 has persistent hydrogen connections with ARG503 and SER373, as well as stable hydrophobic interactions with LEU407 and ALA465. Furthermore, infrequent water-mediated interactions were found, which contributed to improved complex stability throughout the simulation. In contrast, the 2D maps for CMNPD20863 revealed fewer stable hydrogen bonds and a greater reliance on hydrophobic interactions, particularly with PHE531, along with rare electrostatic contacts involving GLU505. This interaction profile indicates that CMNPD20863's binding mode is mostly driven by hydrophobic forces and less by polar stabilization, which is consistent with its larger RMSD values and weaker binding stability when compared to CMNPD5805 [Hosen et al. \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dos et al. 2024] (Fig.\u0026nbsp;19).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eT1DM is the leading cause of childhood autoimmune metabolic disorder in globally [Redondo and Morgan, \u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. Due to the extremely complex metabolic disorders that occur in patients with T1DM, once T1DM has reached the terminal stage, it is often more difficult to treat than type 2 diabetes mellitus. Although extensive studies have investigated the molecular pathogenesis of T1DM, it has not been clarified completely [Ilonen et al. \u003cspan citationid=\"CR208\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. Therefore, it is necessary to find key biomarkers for early diagnosis and targeted therapy of T1DM. Bioinformatics analysis of NGS data for identifying target genes and signaling pathways involved in the occurrence and advancement of T1DM. In this investigation, a series of bioinformatics analysis identified DEGs between T1DM and normal control samples based on NGS data obtained from GSE270484 dataset. DLK1 [Wallace et al. \u003cspan citationid=\"CR509\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], IAPP (islet amyloid polypeptide) [Paulsson et al. \u003cspan citationid=\"CR378\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], INS (insulin) [Tsai et al. \u003cspan citationid=\"CR496\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], IGF2 [Vafiadis et al. \u003cspan citationid=\"CR503\" class=\"CitationRef\"\u003e1998\u003c/span\u003e], THBD (thrombomodulin) [Okano et al. \u003cspan citationid=\"CR368\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], IGFBP3 [Huber et al. \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], SST (somatostatin) [Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SPINK1 [Hassan et al. \u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], PYY (peptide YY) [Lafferty et al. \u003cspan citationid=\"CR257\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] and NOX4 [Tang et al. \u003cspan citationid=\"CR478\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] were very important to the progression of T1DM. DLK1 [Hong et al. \u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], IAPP (islet amyloid polypeptide) [Meier et al. \u003cspan citationid=\"CR329\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], SPP1 [Freiholtz et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], IGF2 [Wu et al .2025], THBD (thrombomodulin) [Conway, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], IGFBP3 [Lohr et al. \u003cspan citationid=\"CR299\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], SST (somatostatin) [Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PYY (peptide YY) [Kamiya et al. \u003cspan citationid=\"CR227\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and NOX4 [Li et al. \u003cspan citationid=\"CR275\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] might be the main driving factors of inflammation. DLK1 [Kameswaran et al. \u003cspan citationid=\"CR226\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], IAPP (islet amyloid polypeptide) [Alrouji et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], INS (insulin) [Rachdaoui, \u003cspan citationid=\"CR402\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SPP1 [Xiao et al. \u003cspan citationid=\"CR549\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGF2 [Mercader et al. \u003cspan citationid=\"CR337\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], P2RY1 [Dance et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], THBD (thrombomodulin) [Okano et al. \u003cspan citationid=\"CR368\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], IGFBP3 [Liu et al. \u003cspan citationid=\"CR296\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], SST (somatostatin) [Kothegala et al. \u003cspan citationid=\"CR248\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], SPINK1 [Schneider et al. \u003cspan citationid=\"CR436\" class=\"CitationRef\"\u003e2005\u003c/span\u003e], PYY (peptide YY) [Chen et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and NOX4 [Meng et al. \u003cspan citationid=\"CR333\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] might play an important role in regulating the genetic network related to the occurrence and development of type 2 diabetes mellitus. The expression of DLK1 [Harris et al. \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], INS (insulin) [Zou et al. \u003cspan citationid=\"CR606\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], IGF2 [Soubry et al. \u003cspan citationid=\"CR460\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], IGFBP3 [Mahmood et al. \u003cspan citationid=\"CR315\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], SST (somatostatin) [Fee et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. SERPINA6 [Chan et al. 2024], PYY (peptide YY) [Tyszkiewicz-Nwafor et al. \u003cspan citationid=\"CR499\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and NOX4 [Arab et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] might be associated with depression progression. The abnormal expression of DLK1 [Palumbo et al. \u003cspan citationid=\"CR373\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], IAPP (islet amyloid polypeptide) [Geisler et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], INS (insulin) [Czech, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], IGF2 [Szydlowska-Gladysz et al. \u003cspan citationid=\"CR470\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP3 [Haldrup et al. \u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], SST (somatostatin) [Kumar and Singh, \u003cspan citationid=\"CR255\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SPINK1 [Abass et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PYY (peptide YY) [Lafferty et al. \u003cspan citationid=\"CR257\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] and NOX4 [Greatorex et al. \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] might be related to the progression of obesity. DLK1 [Zeng et al. \u003cspan citationid=\"CR577\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], INS (insulin) [Sowers and Frohlich, \u003cspan citationid=\"CR461\" class=\"CitationRef\"\u003e2004\u003c/span\u003e], SPP1 [Freiholtz et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], P2RY1 [Timur et al. \u003cspan citationid=\"CR490\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], THBD (thrombomodulin) [Khosravi et al. 2021], IGFBP3 [Lee et al. \u003cspan citationid=\"CR264\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PYY (peptide YY) [Zhu et al. \u003cspan citationid=\"CR604\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] and NOX4 [Vendrov et al. \u003cspan citationid=\"CR506\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] play an important role in the cardiovascular disorders. The abnormal expression of DLK1 [Marquez-Exposito et al. \u003cspan citationid=\"CR322\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], INS (insulin) [Mak, \u003cspan citationid=\"CR316\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], SPP1 [Ding et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP3 [B\u0026uuml;scher et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], SST (somatostatin) [Messchendorp et al. \u003cspan citationid=\"CR338\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PYY (peptide YY) [P\u0026eacute;rez-Font\u0026aacute;n et al. \u003cspan citationid=\"CR384\" class=\"CitationRef\"\u003e2008\u003c/span\u003e] and NOX4 [Li et al. \u003cspan citationid=\"CR275\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] contributes to the progression of kidney disorders. DLK1 [Li et al. \u003cspan citationid=\"CR272\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], INS (insulin) [Singh et al. \u003cspan citationid=\"CR454\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], SPP1 [Morse et al. \u003cspan citationid=\"CR351\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], IGF2 [de Carvalho et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP3 [Ahasic et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], SST (somatostatin) [Ram\u0026iacute;rez-Jarqu\u0026iacute;n et al. \u003cspan citationid=\"CR405\" class=\"CitationRef\"\u003e2012\u003c/span\u003e] and NOX4 [Harijith et al. \u003cspan citationid=\"CR178\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] were a novel key genes in lung disorders. DLK1 [Perram\u0026oacute;n and Jim\u0026eacute;nez, \u003cspan citationid=\"CR385\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SPP1 [Wu et al. \u003cspan citationid=\"CR541\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGF2 [Giraudi et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], THBD (thrombomodulin) [Kan et al. \u003cspan citationid=\"CR228\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], IGFBP3 [Haldrup et al. \u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], SST (somatostatin) [Aziz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], SPINK1 [Oruc et al. \u003cspan citationid=\"CR371\" class=\"CitationRef\"\u003e2009\u003c/span\u003e] and NOX4 [Greatorex et al. \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] genes might be related to the pathophysiology of liver diseases. DLK1 [Dai et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SPP1 [Gazal et al. \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], IGFBP3 [Peet et al. \u003cspan citationid=\"CR382\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] and PYY (peptide YY) [Balog et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] might be the biomarkers for the early diagnosis of autoimmune disorders. The DLK1 [Figeac et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], SPP1 [Chen et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], IGF2 [Wang et al. \u003cspan citationid=\"CR511\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP3 [Shi et al. \u003cspan citationid=\"CR448\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], PYY (peptide YY) [Chen et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and NOX4 [Zhang et al. \u003cspan citationid=\"CR582\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] genes have been shown to be expressed considerably altered in the osteoporosis. Elevated levels of IAPP (islet amyloid polypeptide) [Dubey et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], IGF2 [Yang et al. \u003cspan citationid=\"CR559\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], IGFBP3 [Hu et al. \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SST (somatostatin) [Aziz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] and NOX4 [Syed et al. \u003cspan citationid=\"CR468\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] have been associated with oxidative stress. IAPP (islet amyloid polypeptide) [Alrouji et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], INS (insulin) [Ramalingam and Kim, \u003cspan citationid=\"CR404\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], SPP1 [De Schepper et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], IGF2 [Alberini, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], IGFBP3 [Johansson et al. \u003cspan citationid=\"CR221\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], SST (somatostatin) [Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and NOX4 [Boonpraman and Yi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] expression has been found to be altered in patients with neurodegenerative disorders. Altered levels of INS (insulin) [Sarafidis, 2007], SPP1 [Chen et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], IGFBP3 [Neto et al. \u003cspan citationid=\"CR359\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SST (somatostatin) [Troisi et al. \u003cspan citationid=\"CR493\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], PYY (peptide YY) [Zhu et al. \u003cspan citationid=\"CR604\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] and NOX4 [Pavlov et al. \u003cspan citationid=\"CR379\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] have been associated with hypertension. The abnormal expression of SPP1 [Rejas-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR410\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP3 [Romaniuk et al. \u003cspan citationid=\"CR416\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], SST (somatostatin) [Hern\u0026aacute;ndez et al. \u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and NOX4 [Meng et al. \u003cspan citationid=\"CR333\" class=\"CitationRef\"\u003e2018\u003c/span\u003e] might be involved in the pathogenesis of eye disorders. INS (insulin) [Ntaios et al. \u003cspan citationid=\"CR364\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], IGF2 [Fei et al. 2025], P2RY1 [Janicki et al. \u003cspan citationid=\"CR212\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], THBD (thrombomodulin) [Zhu et al. \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], IGFBP3 [Armbrust et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], SST (somatostatin) [Chiazza et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NOX4 [Li et al. \u003cspan citationid=\"CR274\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and are involved in growth and development of stroke. The above results suggest that these significant DEGs might influence the progression of T1DM.\u003c/p\u003e\u003cp\u003eGO and REACTOME pathway enrichment analyses were used to explore the molecular mechanisms of the enriched genes involved in the occurrence and advancement of T1DM. Signaling pathways include neuronal system [Dolatshahi et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], signal transduction [Magrini et al. \u003cspan citationid=\"CR314\" class=\"CitationRef\"\u003e2002\u003c/span\u003e], signaling by receptor tyrosine kinases [Gal\u0026aacute;n et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], neurotransmitter receptors and postsynaptic signal transmission [Pan et al. \u003cspan citationid=\"CR374\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], Neurexins and neuroligins [Suckow et al. \u003cspan citationid=\"CR465\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], muscle contraction [Grotle et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], metabolism [Zhang et al. \u003cspan citationid=\"CR583\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], neutrophil degranulation [Nigi et al. \u003cspan citationid=\"CR361\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and innate immune system [Needell and Zipris, \u003cspan citationid=\"CR356\" class=\"CitationRef\"\u003e2017\u003c/span\u003e] plays an important role in the pathogenesis of T1DM. Previous studies have reported that the enriched genes include CALD1 [Śnit et al. \u003cspan citationid=\"CR456\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], RBP4 [Pullakhandam et al. \u003cspan citationid=\"CR395\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], PDX1 [Ding et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], EGR1 [Ao et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], GSN (gelsolin) [Noren Hooten et al. \u003cspan citationid=\"CR362\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], NPY (neuropeptide Y) [Sk\u0026auml;rstrand et al. \u003cspan citationid=\"CR455\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], SPHK1 [Liu et al. \u003cspan citationid=\"CR290\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], GREM1 [Abas et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SOCS2 [Alkharusi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], B4GALNT1 [Boraska et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], TRIB3 [Lu et al. \u003cspan citationid=\"CR302\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], RNASEL (ribonuclease L) [Zeng et al. \u003cspan citationid=\"CR576\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], GPNMB (glycoprotein nmb) [Huo et al. \u003cspan citationid=\"CR204\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ADRB2 [Schouwenberg et al. \u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], IGF2BP2 [Gu et al. \u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], TNFAIP3 [Cao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], MST1 [Wu et al. \u003cspan citationid=\"CR547\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SOX9 [Zhang et al. \u003cspan citationid=\"CR587\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], PCSK9 [Bojanin et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], AREG (amphiregulin) [Raugh et al. 2019], FFAR2 [Shi et al. \u003cspan citationid=\"CR445\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], G6PC2 [Sanda et al. \u003cspan citationid=\"CR424\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], GLP1R [Khera et al. \u003cspan citationid=\"CR241\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], RGS16 [Villasenor et al. \u003cspan citationid=\"CR508\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], GCGR (glucagon receptor) [Lee et al. \u003cspan citationid=\"CR266\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], GLRA3 [Sandholm et al. \u003cspan citationid=\"CR425\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], SLC2A2 [Alhaidan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SCD5 [Z\u0026aacute;mb\u0026oacute; et al. \u003cspan citationid=\"CR575\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CD5 [De Filippo et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1997\u003c/span\u003e], CYP2J2 [Li et al. \u003cspan citationid=\"CR276\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], ENTPD1 [Friedman et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], ITPR3 [Qu et al. \u003cspan citationid=\"CR400\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], CD6 [Do et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], HLA-DQB1 [Singh et al. \u003cspan citationid=\"CR452\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NPTX2 [\u0026Ccedil;adırcı et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SOD3 [Mohammedi et al. \u003cspan citationid=\"CR346\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], ESR2 [Sartoretto et al. \u003cspan citationid=\"CR429\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CREB5 [Liang et al. \u003cspan citationid=\"CR286\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], AGT (angiotensinogen) [Yang et al. \u003cspan citationid=\"CR565\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Leurs et al. \u003cspan citationid=\"CR271\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], CXCL1 [Takahashi et al. \u003cspan citationid=\"CR473\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], CD36 [Terasaki et al. \u003cspan citationid=\"CR484\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], AGTR1 [Kovacevic et al. \u003cspan citationid=\"CR251\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TGFBI (transforming growth factor beta induced) [Wu et al. \u003cspan citationid=\"CR536\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CD74 [Mangano et al. \u003cspan citationid=\"CR318\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], SOCS1 [Kakoki et al. \u003cspan citationid=\"CR225\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SLC6A4 [Xiu et al. \u003cspan citationid=\"CR553\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], TXNIP (thioredoxin interacting protein) [Basnet et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], BCL6 [McNitt et al. \u003cspan citationid=\"CR328\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], GCG (glucagon) [Leung et al. \u003cspan citationid=\"CR270\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], UCP2 [Rudofsky et al. \u003cspan citationid=\"CR418\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], LPAR1 [Abo El-Magd et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], GPR119 [Bilal et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], IL7 [Hoffmann et al. \u003cspan citationid=\"CR189\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CD44 [Assayag-Asherie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], DLL1 [Qu et al. \u003cspan citationid=\"CR401\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], IGFBP2 [Bereket et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e], HPSE (heparanase) [Zhou et al. \u003cspan citationid=\"CR600\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], VDR (vitamin D receptor) [Tapia et al. \u003cspan citationid=\"CR480\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], GLB1 [MacFarlane et al. \u003cspan citationid=\"CR312\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], DPP4 [Davanso et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], SLC29A3 [Besci et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], LOXL3 [Huang et al. \u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], RFX6 [Şimşek et al. \u003cspan citationid=\"CR451\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MSH2 [Babić et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], B2M [Monteiro et al. \u003cspan citationid=\"CR348\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], GPER1 [Yao et al. \u003cspan citationid=\"CR567\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], BCHE (butyrylcholinesterase) [Ma et al. \u003cspan citationid=\"CR309\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], CD40 [Vaitaitis et al. \u003cspan citationid=\"CR504\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], LEPR (leptin receptor) [Lee et al. \u003cspan citationid=\"CR261\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], IRF1 [Colli et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], SLC40A1 [Hao et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SLC22A4 [Santiago et al. \u003cspan citationid=\"CR426\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], DPP4 [Blaslov et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], CDKAL1 [Chistiakov et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], IPPK (inositol-pentakisphosphate 2-kinase) [Looker et al. \u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], GLRX (glutaredoxin) [Montano et al. \u003cspan citationid=\"CR347\" class=\"CitationRef\"\u003e2015\u003c/span\u003e] and ALDH2 [He et al. \u003cspan citationid=\"CR184\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] are a key regulators of T1DM. Studies had shown that enriched genes include TGFBR3 [Qianru et al. \u003cspan citationid=\"CR397\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], NTN1 [Mentxaka et al. \u003cspan citationid=\"CR336\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ALOX5 [Cai et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FFAR4 [Salaga et al. \u003cspan citationid=\"CR423\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], ADAMTS5 [Sharma et al. \u003cspan citationid=\"CR440\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], RBP4 [Pazos-P\u0026eacute;rez et al. \u003cspan citationid=\"CR381\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PDX1 [Wang et al. \u003cspan citationid=\"CR528\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], FAT1 [Weylandt et al. \u003cspan citationid=\"CR531\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], EGR1 [Lehman et al. \u003cspan citationid=\"CR267\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], GSN (gelsolin) [Piktel et al. \u003cspan citationid=\"CR387\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], GLIPR2 [Wu et al. \u003cspan citationid=\"CR547\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], AGT (angiotensinogen) [Carroll et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Shi et al. \u003cspan citationid=\"CR447\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CARD11 [Yamamoto-Furusho et al. \u003cspan citationid=\"CR557\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PLCG2 [Tsai et al. \u003cspan citationid=\"CR495\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CXCL1 [De Filippo et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], PRG4 [Menon et al. \u003cspan citationid=\"CR334\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CD36 [Zhao et al. \u003cspan citationid=\"CR590\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], AGTR1 [Fung et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2011\u003c/span\u003e] and ENPP2 [Chattopadhyay et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] were associated with inflammation. Abnormal regulation of enriched genes include NTN1 [Chaitra et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ALOX5 [Nejatian et al. \u003cspan citationid=\"CR357\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], FFAR4 [Chen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ADCYAP1 [Gu et al. 2002], CABLES1 [Hetty et al. \u003cspan citationid=\"CR186\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], RBP4 [Pullakhandam et al. \u003cspan citationid=\"CR395\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], OLIG1 [Joshi et al. \u003cspan citationid=\"CR223\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], PDX1 [Lian et al. \u003cspan citationid=\"CR285\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EGR1 [Ke et al. \u003cspan citationid=\"CR236\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CRABP2 [Zhang et al. \u003cspan citationid=\"CR588\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], FAIM2 [Kang et al. \u003cspan citationid=\"CR231\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], AGT (angiotensinogen) [Qiao et al. \u003cspan citationid=\"CR398\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [El-Hagracy et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], CXCL1 [Sajadi et al. \u003cspan citationid=\"CR422\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], VIPR1 [Tavaglione et al. \u003cspan citationid=\"CR481\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], DNER (delta/notch like EGF repeat containing) [Deng et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], CD36 [Moon et al. \u003cspan citationid=\"CR350\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], AGTR1 [Ihsan et al. \u003cspan citationid=\"CR207\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CD74 [Chen et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and SOCS1 [Opazo-R\u0026iacute;os et al. \u003cspan citationid=\"CR369\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] are associated with type 2 diabetes mellitus. Studies have found that enriched genes include ADCYAP1 [Matsuzaki and Tohyama, \u003cspan citationid=\"CR327\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], MAPT (microtubule associated protein tau) [Kimura et al. \u003cspan citationid=\"CR245\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], RBP4 [Yao and Li, \u003cspan citationid=\"CR566\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], OLIG1 [Mosebach et al. \u003cspan citationid=\"CR352\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], FAT1 [Gu et al. \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], EGR1 [Lee and Bondy, \u003cspan citationid=\"CR265\" class=\"CitationRef\"\u003e1993\u003c/span\u003e], CHL1 [Yang et al. \u003cspan citationid=\"CR560\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], NPY (neuropeptide Y) [Ozsoy et al. \u003cspan citationid=\"CR372\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], KCNQ2 [Costi et al. 2 021], CASR (calcium sensing receptor) [Guo et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], AGT (angiotensinogen) [L\u0026oacute;pez-Le\u0026oacute;n et al. \u003cspan citationid=\"CR301\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], NTRK2 [Paolini et al. \u003cspan citationid=\"CR376\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CXCL1 [Fanelli et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CD36 [Bai et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], AGTR1 [Taylor et al. \u003cspan citationid=\"CR483\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], SOCS1 [Sun et al. \u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SLC6A4 [Ho et al. \u003cspan citationid=\"CR188\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], TSPAN8 [Schartner et al. \u003cspan citationid=\"CR435\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], ADCY2 [Aghabozorg et al. 2020] and UCP2 [Du et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] are alterwd expressed in depression. Enriched genes include NTN1 [Mentxaka et al. \u003cspan citationid=\"CR336\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ALOX5 [Szukiewicz et al. 2025], CCND1 [Thun et al. \u003cspan citationid=\"CR487\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], CABLES1 [Hetty et al. \u003cspan citationid=\"CR186\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], RBP4 [Kilicarslan et al. \u003cspan citationid=\"CR243\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PDX1 [Kouidrat et al. \u003cspan citationid=\"CR250\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FAT1 [Tang et al. \u003cspan citationid=\"CR477\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EGR1 [Ruebel et al. \u003cspan citationid=\"CR419\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], SIX3 [Yu et al. \u003cspan citationid=\"CR569\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NPY (neuropeptide Y) [Wu et al. \u003cspan citationid=\"CR540\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], FAIM2 [Scharf et al. \u003cspan citationid=\"CR434\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], AGT (angiotensinogen) [Repchuk et al. \u003cspan citationid=\"CR411\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Caron et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], NTRK2 [Rask-Andersen et al. \u003cspan citationid=\"CR406\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], CARD11 [Lee et al. \u003cspan citationid=\"CR263\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Vambergue et al. \u003cspan citationid=\"CR505\" class=\"CitationRef\"\u003e2001\u003c/span\u003e], CXCL1 [Zhang et al. \u003cspan citationid=\"CR587\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], PRG4 [Nahon et al. \u003cspan citationid=\"CR354\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CD36 [Wu et al. \u003cspan citationid=\"CR540\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and AGTR1 [Adiyeva et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] are altered expression in obesity. Enriched genes include TGFBR3 [Tang et al. \u003cspan citationid=\"CR479\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ALOX5 [Halade et al. \u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ADAMTS5 [Wang et al. \u003cspan citationid=\"CR528\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], RBP4 [Ji et al. \u003cspan citationid=\"CR215\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], FAT1 [Mao et al. \u003cspan citationid=\"CR319\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], EGR1 [Khachigian, \u003cspan citationid=\"CR237\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], SULF1 [Ji et al. \u003cspan citationid=\"CR214\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NPY (neuropeptide Y) [Tan et al. \u003cspan citationid=\"CR474\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], DKK3 [Xu et al. \u003cspan citationid=\"CR555\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CASR (calcium sensing receptor) [Chu et al. \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], GLIPR2 [Yuan et al. \u003cspan citationid=\"CR573\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], FAIM2 [Zhong et al. \u003cspan citationid=\"CR593\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], AGT (angiotensinogen) [Daugherty et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Kaiser et al. \u003cspan citationid=\"CR224\" class=\"CitationRef\"\u003e2001\u003c/span\u003e], PLCG2 [Wang et al. \u003cspan citationid=\"CR511\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CXCL1 [Wu et al. \u003cspan citationid=\"CR537\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], PRG4 [Zhou et al. \u003cspan citationid=\"CR599\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CD36 [Shu et al. \u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], AGTR1 [Wei et al. \u003cspan citationid=\"CR530\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and ENPP2 [Karshovska et al. \u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] plays an indispensable role in cardiovascular disorders. Enriched genes include TGFBR3 [Caza et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], ALOX5 [Chen et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FFAR4 [Yang et al. \u003cspan citationid=\"CR564\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SIX2 [Fogelgren et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], CCND1 [Jiang et al. \u003cspan citationid=\"CR218\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], ADAMTS5 [Taylor et al. \u003cspan citationid=\"CR482\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CALD1 [Śnit et al. \u003cspan citationid=\"CR456\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], RBP4 [Chen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PDX1 [Wiggenhause et al. 2022], FAT1 [Gee et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], GLIPR2 [Baxter et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], AGT (angiotensinogen) [Cruz-L\u0026oacute;pez et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Wang et al. \u003cspan citationid=\"CR510\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Brook et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CXCL1 [Li et al. \u003cspan citationid=\"CR280\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], LAMB2 [Trutin et al. \u003cspan citationid=\"CR494\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CD36 [Li et al. \u003cspan citationid=\"CR276\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], AGTR1 [Al-Maraghi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TGFBI (transforming growth factor beta induced) [Dou et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and CD74 [Zhou et al. \u003cspan citationid=\"CR599\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] could be an early detection biomarkers for kidney disorders. Enriched genes include ALOX5 [Mirra et al. \u003cspan citationid=\"CR345\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CCND1 [Thun et al. \u003cspan citationid=\"CR487\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], CALD1 [Wu et al. \u003cspan citationid=\"CR541\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], MAPT (microtubule associated protein tau) [Di Fonzo et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, RBP4 [Jin et al. \u003cspan citationid=\"CR220\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], EGR1 [Zhao et al. \u003cspan citationid=\"CR591\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SULF1 [Tu et al. \u003cspan citationid=\"CR498\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], GSN (gelsolin) [Oikonomou et al. \u003cspan citationid=\"CR365\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], NPY (neuropeptide Y) [Itano et al. \u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], DKK3 [Zhong et al. \u003cspan citationid=\"CR593\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FAIM2 [Shen et al. \u003cspan citationid=\"CR441\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], AGT (angiotensinogen) [Marushchak et al. \u003cspan citationid=\"CR324\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Wang et al. \u003cspan citationid=\"CR511\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NTRK2 [Kong et al. \u003cspan citationid=\"CR247\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Fujii et al. \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2000\u003c/span\u003e], RCN3 [Ding et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CXCL1 [Meng et al. \u003cspan citationid=\"CR333\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PRG4 [Asfari et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], DNER (delta/notch like EGF repeat containing) [Ballester-L\u0026oacute;pez et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and PTP4A3 [Montuschi and Adcock, \u003cspan citationid=\"CR349\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] expression is a potential targets for lung disorders. Enriched genes include FFAR4 [Jiang et al. \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CCND1 [Chen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], RBP4 [Huang and Xu, \u003cspan citationid=\"CR200\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FAT1 [Zhang et al. \u003cspan citationid=\"CR588\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EGR1 [Wu et al. \u003cspan citationid=\"CR538\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SULF1 [Graham et al. \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], GSN (gelsolin) [Leifeld et al. \u003cspan citationid=\"CR268\" class=\"CitationRef\"\u003e2006\u003c/span\u003e], NPY (neuropeptide Y) [Ortiz et al. \u003cspan citationid=\"CR370\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], SPHK1 [Ding et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], GREM1 [Horn et al. \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], AGT (angiotensinogen) [Che et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Chen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], PLCG2 [Gardin et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CXCL1 [Liu et al. \u003cspan citationid=\"CR294\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PRG4 [Nahon et al. \u003cspan citationid=\"CR354\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CD36 [Liu and Yin, \u003cspan citationid=\"CR297\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], AGTR1 [Zhu et al. \u003cspan citationid=\"CR603\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], ENPP2 [Luo and Yu, \u003cspan citationid=\"CR305\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TGFBI (transforming growth factor beta induced) [Krzistetzko et al. \u003cspan citationid=\"CR253\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CD74 [Cheng et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] and SOCS1 [Mafanda et al. \u003cspan citationid=\"CR313\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] were frequently altered in liver diseases. Studied have proved that enriched genes include ALOX5 [Cai et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ADCYAP1 [Cunningham et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], ADAMTS5 [Tsuzaka et al. \u003cspan citationid=\"CR497\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], RBP4 [Toyama et al. \u003cspan citationid=\"CR492\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], PDX1 [Amatya et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], EGR1 [Yang et al. \u003cspan citationid=\"CR565\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], GSN (gelsolin) [Lee et al. \u003cspan citationid=\"CR263\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NPY (neuropeptide Y) [Bedoui et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e], CASR (calcium sensing receptor) [Gavalas et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], HDAC9 [Yan et al. \u003cspan citationid=\"CR558\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], FAIM2 [Sawicka et al. \u003cspan citationid=\"CR432\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PLCG2 [Szymanski et al. 2018], CXCL1 [Zeng et al. \u003cspan citationid=\"CR579\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], VIPR1 [Abad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], DNER (delta/notch like EGF repeat containing) [Jarius et al. 2015], CD36 [He et al. \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], UNC93B1 [Wolf et al. \u003cspan citationid=\"CR535\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PTGER4 [Rahman et al. \u003cspan citationid=\"CR403\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], CD74 [Meza-Romero et al. \u003cspan citationid=\"CR340\" class=\"CitationRef\"\u003e2014\u003c/span\u003e] and SOCS1 [Yu et al. \u003cspan citationid=\"CR569\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] play important roles in development of autoimmune disorders. Enriched genes include CCND1 [Wang and Zhao, \u003cspan citationid=\"CR516\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], RBP4 [Mihai et al. \u003cspan citationid=\"CR343\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], EGR1 [Wang et al. \u003cspan citationid=\"CR512\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], NPY (neuropeptide Y) [Chen et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CASR (calcium sensing receptor) [Di Nisio et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], DNM3 [Kaur et al. \u003cspan citationid=\"CR234\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ADRB2 [Krasnova et al. \u003cspan citationid=\"CR252\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], LRP4 [Wang et al. \u003cspan citationid=\"CR514\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SEMA3A [Zhang et al. \u003cspan citationid=\"CR588\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PHGDH (phosphoglycerate dehydrogenase) [Wang et al. \u003cspan citationid=\"CR528\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], DEPTOR (DEP domain containing MTOR interacting protein) [Chen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], CXCL1 [Hu et al. \u003cspan citationid=\"CR198\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], TXNIP (thioredoxin interacting protein) [Peng et al. \u003cspan citationid=\"CR383\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], ALDH1A1 [Jia et al. \u003cspan citationid=\"CR216\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CSF1 [Batoon et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CD44 [Sikora et al. \u003cspan citationid=\"CR450\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MITF (melanocyte inducing transcription factor) [Xue et al. \u003cspan citationid=\"CR556\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], IGFBP2 [Sugimoto et al. \u003cspan citationid=\"CR466\" class=\"CitationRef\"\u003e1997\u003c/span\u003e], VDR (vitamin D receptor) [Gasperini et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and NNMT (nicotinamide N-methyltransferase) [Yu et al. \u003cspan citationid=\"CR570\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] expression had been confirmed in osteoporosis. Recent study reported that enriched genes include TGFBR3 [Qianru et al. \u003cspan citationid=\"CR397\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], ALOX5 [Luo et al. \u003cspan citationid=\"CR304\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CCND1 [Zhong et al. \u003cspan citationid=\"CR595\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], MAPT (microtubule associated protein tau) [Bradford et al. 2016], RBP4 [Wang et al. \u003cspan citationid=\"CR522\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PDX1 [Baumel-Alterzon and Scott, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], FAT1 [Boyle et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], EGR1 [Pang et al. \u003cspan citationid=\"CR375\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], NPY (neuropeptide Y) [Kuncov\u0026aacute; et al. \u003cspan citationid=\"CR256\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], DKK3 [Muecklich et al. \u003cspan citationid=\"CR353\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], GLIPR2 [Wu et al. \u003cspan citationid=\"CR547\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], AGT (angiotensinogen) [Marushchak et al. \u003cspan citationid=\"CR324\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CARD11 [Lu et al. \u003cspan citationid=\"CR303\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Pawlak et al. \u003cspan citationid=\"CR380\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], PLCG2 [Hu et al. \u003cspan citationid=\"CR198\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], CXCL1 [Jiang et al. \u003cspan citationid=\"CR218\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PRG4 [Zhou et al. \u003cspan citationid=\"CR599\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], CD36 [Liu et al. \u003cspan citationid=\"CR291\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], AGTR1 [Fenty-Stewart et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2009\u003c/span\u003e] and ENPP2 [Fang et al. 2003] were observed to be associated with the risk of oxidative stress. Enriched genes include TGFBR3 [Zhou et al. \u003cspan citationid=\"CR599\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ALOX5 [Song et al. \u003cspan citationid=\"CR459\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CCND1 [Dietrich et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], ADAMTS5 [Miguel et al. \u003cspan citationid=\"CR342\" class=\"CitationRef\"\u003e2005\u003c/span\u003e], MAPT (microtubule associated protein tau) [Zhang et al. \u003cspan citationid=\"CR587\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], OLFM1 [Wei et al. \u003cspan citationid=\"CR529\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PDX1 [Guo et al. \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], CCDC88C [Leńska-Mieciek et al. \u003cspan citationid=\"CR269\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], EGR1 [Guo et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], GSN (gelsolin) [Ma et al. 2006], FAIM2 [Komnig et al. \u003cspan citationid=\"CR246\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], NTRK2 [Tom\u0026aacute;s et al. \u003cspan citationid=\"CR491\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Piazza et al. \u003cspan citationid=\"CR386\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], PLCG2 [Messenger et al. \u003cspan citationid=\"CR339\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], PLEKHB1 [Marques et al. 2022], CXCL1 [Ma et al. \u003cspan citationid=\"CR307\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], CD36 [Šer\u0026yacute; et al. 2020], CD74 [Bryan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], SOCS1 [Lofrumento et al. \u003cspan citationid=\"CR298\" class=\"CitationRef\"\u003e2014\u003c/span\u003e] and SLC6A4 [Calabr\u0026ograve; et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] plays a role in the pathogenesis of neurodegenerative disorders. Enriched genes include SIX2 [Fogelgren et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], RBP4 [Jadhao et al. \u003cspan citationid=\"CR211\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], EGR1 [Laggner et al. \u003cspan citationid=\"CR258\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], NPY (neuropeptide Y) [Baltazi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], DKK3 [Sch\u0026auml;fer et al. 2024], CASR (calcium sensing receptor) [Liu et al. \u003cspan citationid=\"CR290\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SPHK1 [Yang et al. \u003cspan citationid=\"CR561\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], GREM1 [Meng et al. \u003cspan citationid=\"CR332\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], PCSK1 [Gu et al. \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], TRIB3 [He et al. \u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], AGT (angiotensinogen) [Satou et al. \u003cspan citationid=\"CR430\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], NTRK2 [Su et al. \u003cspan citationid=\"CR463\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [White et al. \u003cspan citationid=\"CR532\" class=\"CitationRef\"\u003e2010\u003c/span\u003e], RCN3 [He et al. \u003cspan citationid=\"CR183\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CXCL1 [Hilscher et al. \u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], CD36 [Khaleel et al. 2022], AGTR1 [Zeng et al. \u003cspan citationid=\"CR577\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], TGFBI (transforming growth factor beta induced) [Roh et al. \u003cspan citationid=\"CR415\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SLC6A4 [Zhang et al. \u003cspan citationid=\"CR581\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and TXNIP (thioredoxin interacting protein) [Wang et al. \u003cspan citationid=\"CR521\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] expression has been found to be increased in patients with hypertension. Elevated levels of enriched genes include TGFBR3 [Xie et al. \u003cspan citationid=\"CR550\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], EGR1 [Li et al. \u003cspan citationid=\"CR277\" class=\"CitationRef\"\u003e2008\u003c/span\u003e], DKK3 [Yu et al. \u003cspan citationid=\"CR571\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SHISA6 [Oishi et al. \u003cspan citationid=\"CR366\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], TRIB3 [Ung et al. \u003cspan citationid=\"CR500\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], RXRG (retinoid X receptor gamma) [Hsieh et al. \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], GPNMB (glycoprotein nmb) [Huo et al. \u003cspan citationid=\"CR204\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], ECM1 [Cai et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], ADRB2 [Chmielarz-Czarnocińska et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SEMA3A [Tanaka et al. \u003cspan citationid=\"CR475\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], AGT (angiotensinogen) [Qiao et al. \u003cspan citationid=\"CR398\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], CXCL1 [Wang et al. \u003cspan citationid=\"CR522\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], LAMB2 [Alshamrani et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PRG4 [Menon et al. \u003cspan citationid=\"CR334\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], CD36 [Lavalette et al. \u003cspan citationid=\"CR259\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], AGTR1 [Durska et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], TGFBI (transforming growth factor beta induced) [Kheir et al. \u003cspan citationid=\"CR240\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], SOCS1 [Ahmed et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NPHP4 [Wiik et al. \u003cspan citationid=\"CR534\" class=\"CitationRef\"\u003e2008\u003c/span\u003e] and TXNIP (thioredoxin interacting protein) [Singh, \u003cspan citationid=\"CR453\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] have been associated with eye disorders. Enriched genes include ALOX5 [Karuppagounder et al. \u003cspan citationid=\"CR233\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], MAPT (microtubule associated protein tau) [Michalski et al. \u003cspan citationid=\"CR341\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], RBP4 [Wang et al. \u003cspan citationid=\"CR514\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], EGR1 [Li et al. \u003cspan citationid=\"CR273\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], GSN (gelsolin) [Endres et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e1999\u003c/span\u003e], NPY (neuropeptide Y) [Dong et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], DKK3 [Zhou et al. \u003cspan citationid=\"CR599\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], HDAC9 [Chiou et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], HDAC9 [Markus, \u003cspan citationid=\"CR320\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], BDH2 [Li et al. \u003cspan citationid=\"CR280\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], FAIM2 [Hu and Lin, \u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], AGT (angiotensinogen) [Isordia-Salas et al. \u003cspan citationid=\"CR209\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], NTRK2 [Shi et al. \u003cspan citationid=\"CR446\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], TFPI (tissue factor pathway inhibitor) [Rossouw et al. \u003cspan citationid=\"CR417\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], CXCL1 [Barber et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], AGTR1 [Altarescu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], CD74 [Yang et al. \u003cspan citationid=\"CR563\" class=\"CitationRef\"\u003e2017\u003c/span\u003e], SOCS1 [Ma et al. \u003cspan citationid=\"CR308\" class=\"CitationRef\"\u003e2016\u003c/span\u003e] and SLC6A4 [Kang et al. \u003cspan citationid=\"CR230\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] have been identified as a key biomarkers in stroke. Our results indicate the importance of enriched genes in the occurrence and development of T1DM. We need to pay attention to the roles of these enriched genes in T1DM.\u003c/p\u003e\u003cp\u003eNotably, the PPI network and modules related to T1DM was composed of functional proteins that interacted with each other to participate in biological signal transmission, gene expression regulation, energy and metabolism. Genes were identified as hub genes from PPI network and modules of T1DM. FN1 functions as an ECM scaffold, facilitating collagen deposition and myofibroblast activation as well as promotes fibroblast differentiation [Proctor, \u003cspan citationid=\"CR393\" class=\"CitationRef\"\u003e1987\u003c/span\u003e]. FN1 might be considered as a novel biomarker for T1DM. Hub gene GSN (gelsolin) regulates actin dynamics, insulin secretion, and inflammation in T1DM [Noren Hooten et al. \u003cspan citationid=\"CR362\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. Hub gene ADRB2 regulates insulin secretion, glucose metabolism, and lipolysis in T1DM [Schouwenberg et al. \u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e2008\u003c/span\u003e]. CEP128 is a centrosomal and ciliary protein that regulates cell division, polarity, and signaling. Its dysfunction leads to neurological disorders mainly via ciliary signaling disruption and centrosome defect [Lin et al. \u003cspan citationid=\"CR287\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. CEP128 might be considerd as a novel biomarker for T1DM associated neurodevelopmental disorders. FLNA (Filamin A) mediates disease by altering actin filament dynamics, protein interactions, and mechanotransduction. Specific hub gene FLNA mutations disrupt protein folding and function, leading to structural defects and loss of essential cellular signaling in cardiac defects [Haataja et al. \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. CEP128 might be considered as a novel biomarker for T1DM associated cardiac defects. Hub gene CD74 potentiates the immune visibility of β-cells, escalating lymphocytic infiltration and targeting for autoimmune attack in T1DM [Mangano et al. \u003cspan citationid=\"CR318\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. Hub gene EFEMP2\u0026rsquo;s disruption leads to widespread connective tissue pathologies due to combined defects in elastic fiber and collagen synthesis in aortic aneurysm [Sadeghipour et al. \u003cspan citationid=\"CR421\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. EFEMP2 might be considered as a novel biomarker for T1DM associated aortic aneurysm. The POU6F2 hub gene encodes a transcription factor with key roles in ocular development. Mutations and dysregulation of POU6F2 leads to. glaucoma [Lin et al. \u003cspan citationid=\"CR287\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. POU6F2 might be considered as a novel biomarker for T1DM associated glaucoma. Dysregulation of hub gene P4HA2 is implicated in glycolysis in cancer [Wu et al. \u003cspan citationid=\"CR538\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. P4HA2 might be considered as a novel biomarker for T1DM associated cancer. Hub gene BCL6 expression is controlled by cytokine signaling and costimulatory signals, which are important in the immune networks implicated in T1DM pathogenesis [McNitt et al. \u003cspan citationid=\"CR328\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. Hub gene GEM loss leads to increased brain injury after ischemic stroke [Takahashi et al. \u003cspan citationid=\"CR472\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. GEM might be considered as a novel biomarker for T1DM associated stroke. Hub gene CCHCR1 regulates cytoskeletal dynamics critical for centriole duplication and proper mitotic spindle formation, abnormalities in which can leads to psoriasis progression [Tervaniemi et al. \u003cspan citationid=\"CR485\" class=\"CitationRef\"\u003e2018\u003c/span\u003e]. CCHCR1 might be considerd as a novel biomarker for T1DM associated psoriasis. Dysregulation of hub gene SIAE (sialic acid acetylesterase) might influence autoimmune pathogenesis by modifing antigen presentation and helper T cell polarization [Sevdali et al. \u003cspan citationid=\"CR439\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. SIAE might be considered as a novel biomarker for T1DM associated autoimmune diseases. Dysregulation of hub gene OS-9 can alter HIF-1α levels, impacting hypoxia responses critical in cancer progression [Baek et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e]. OS-9 might be considered as a novel biomarker for T1DM associated cancer. NAAA regulates the local concentrations of palmitoylethanolamide (PEA) and anandamide (AEA) signaling molecules impacting in inflammation [Xie et al. \u003cspan citationid=\"CR552\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. NAAA might be considered as a novel biomarker for T1DM associated inflammation. Recent literature shows TCTN1 molecular mechanisms center on modulation of the Hh pathway in cancer progression [Wang et al. \u003cspan citationid=\"CR519\" class=\"CitationRef\"\u003e2015\u003c/span\u003e].TCTN1 might be considered as a novel biomarker for T1DM associated cancer. Our results showed that hub genes might involved in progression of T1DM. Novel biomarkers responsible for T1DM progression listed in Supplementary Table S6.\u003c/p\u003e\u003cp\u003eA miRNA-hub gene regulatory network and miRNA-hub gene regulatory network are a complex biological system where miRNAs and TFs regulate the expression of hub genes in T1DM. The abnormal expression of hsa-miR-142-5p [Collares et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], hsa-miR-501-3p [Pinheiro et al. 2013], hsa-miR-101-3p [Santos et al. \u003cspan citationid=\"CR427\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], NOTCH1 [Wang et al. \u003cspan citationid=\"CR526\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], RUNX1 [Zhong et al. \u003cspan citationid=\"CR594\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] and PPARG [Zusi et al. \u003cspan citationid=\"CR607\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] contributes to the progreesion of T1DM. Novel biomarkers include hsa-mir-657, hsa-miR-200a-5p, hsa-mir-6834-5p, hsa-miR-1266-5p, hsa-mir-373, hsa-miR-33b-3p, hsa-mir-183-5p, CEBPB, HAND2, ASXL1, GTF3C2, AF4, RELA and SUZ12 were might be involved in the pathogenesis of T1DM.\u003c/p\u003e\u003cp\u003eOur investigation focused on understanding the action of drug on expression of hub genes. Our findings suggest that drugs- Clenbuterol, Diethylstilbestrol, Minocycline, Exenatide, Phosphatidyl, Selegiline, Isoflurophate, 2-Pyridinethiol, MB07803 and Crotonaldehyde concurrently target to hub genes include ADRB2, ESR2, ALOX5, GLP1R, PRKCA, MAOB, BCHE, CTSB, FBP1 and ALDH2, potentially controlling the development of T1DM.\u003c/p\u003e\u003cp\u003eThe docking analyses consistently revealed CMNPD30283 as the most potent FN1 inhibitor, which is stabilized by electrostatic anchoring to ARG503 and GLN330 and hydrophobic interactions with LEU407 and PHE531. CMNPD30363 and CMNPD30456 both had positive binding free energies, although CMNPD5805 showed adaptability in IFD, indicating its potential as a flexible binder. Across all ligands, residues ARG503, SER373, TYR372, GLN330, and GLN409 revealed as conserved hotspots for hydrogen bonding and electrostatic interactions, which were supplemented by hydrophobic packing from LEU407 and PHE531. Although solvation penalties were positive, they were balanced out by substantial Coulombic and van der Waals contributions, indicating that FN1-ligand binding is mediated by a combination of polar and hydrophobic forces.\u003c/p\u003e\u003cp\u003eMD simulations validated these findings, with CMNPD5805 exhibiting low RMSD values, minor residue fluctuations, and sustained hydrogen bonds with ARG503 and SER373, which are maintained by hydrophobic interactions with LEU407 and ALA465. In contrast, CMNPD20863 was somewhat stable, relying on hydrophobic interactions with PHE531 and intermittent polar contacts with GLU505. Recurrent interactions with ARG503, SER373, TYR372, GLN330, GLN409, and PHE531 during docking and MD corroborated their involvement as structural hotspots in FN1-ligand recognition.\u003c/p\u003e\u003cp\u003eThese findings point to CMNPD30283 as the most promising FN1 inhibitor, with CMNPD5805 as a dynamically stable alternative and CMNPD30363, CMNPD30456, and CMNPD20863 as other candidates of interest. The tiered computational approach which included Glide docking, IFD, MM-GBSA, and MDwas effective not only in evaluating candidate inhibitors but also in understanding the molecular mechanisms underlying FN1 binding, providing a solid foundation for future optimization and experimental validation.\u003c/p\u003e\u003cp\u003eIn conclusion, the present study identified FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6 as key genes in the pathogenesis of T1DM by integrated analysis of NGS dataset. The results of this investigation further provide useful evidence for investigation into molecular mechanisms, selection of biomarkers, and treatment targets exploration of T1DM. However, further in vitro and in vivo analyses experiments are needed to confirm the functional pathways and hub genes linked with T1DM.\u003c/p\u003e\u003cp\u003eThis work finds CMNPD30283 as the best FN1 inhibitor candidate, validated by docking, IFD, and free-energy studies, whereas CMNPD5805 emerged as a dynamically stable ligand with persistent polar and hydrophobic contacts. Additional compounds, such as CMNPD30363, CMNPD30456, and CMNPD20863, also showed significant binding, lability with different stability. The repeated participation of ARG503, SER373, TYR372, GLN330, GLN409, and PHE531 underscores their importance in ligand recognition. Overall, this integrated computational method establishes a solid platform for choosing marine derived scaffolds while also laying the groundwork for future FN1 inhibitor optimization and experimental validation from \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eT1DM: Type 1 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eDEGs: Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eNGS: Next generation sequencing\u003c/p\u003e\n\u003cp\u003eGEO: Gene expression omnibus\u003c/p\u003e\n\u003cp\u003eGO: Gene ontology\u003c/p\u003e\n\u003cp\u003ePPI: Protein-protein interaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emiRNA: Micro ribonuclic acid\u003c/p\u003e\n\u003cp\u003eTF: Transcription factor\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFN1: Fibronectin 1\u003c/p\u003e\n\u003cp\u003eGSN: Gelsolin\u003c/p\u003e\n\u003cp\u003eADRB2: Adrenoceptor beta 2\u003c/p\u003e\n\u003cp\u003eCEP128: Centrosomal protein 128\u003c/p\u003e\n\u003cp\u003eFLNA: Filamin A\u003c/p\u003e\n\u003cp\u003eCD74: CD74 molecule\u003c/p\u003e\n\u003cp\u003eEFEMP2: EGF containing fibulin extracellular matrix protein 2\u003c/p\u003e\n\u003cp\u003ePOU6F2: \u0026nbsp;POU class 6 homeobox 2\u003c/p\u003e\n\u003cp\u003eP4HA2: \u0026nbsp;Prolyl 4-hydroxylase subunit alpha 2\u003c/p\u003e\n\u003cp\u003eBCL6: BCL6 transcription repressor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWritten Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE270484) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270484]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. V. - Writing original draft, and review and editing \u003c/p\u003e\n\u003cp\u003eS.P. - Formal analysis and validation\u003c/p\u003e\n\u003cp\u003eV.S. - Resources and investigation\u003c/p\u003e\n\u003cp\u003eK,P. - Investigation and validation\u003c/p\u003e\n\u003cp\u003eC. V. - Software and investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasavaraj Vastrad ORCID ID: 0000-0003-2202-7637\u003c/p\u003e\n\u003cp\u003eShivaling Pattanashetti ORCID ID: 0009-0003-9246-1604\u003c/p\u003e\n\u003cp\u003eVeeresh Sadashivanavar ORCID ID: 0009-0002-1054-8996\u003c/p\u003e\n\u003cp\u003eKSR Pai ORCID ID: 0000-0002-2017-9533\u003c/p\u003e\n\u003cp\u003eChanabasayya Vastrad ORCID ID: 0000-0003-3615-4450\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003edos Santos T, Ellis C, Ewald J, Jones RC, Camunas-Soler J, Quake SR, MacDonald PE, University of Alberta, Edmonton, Alberta, Canada, thanks very much, the author who deposited their NGS dataset GSE270484, into the public GEO database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbad C, Jayaram B, Becquet L, Wang Y, O\u0026apos;Dorisio MS, Waschek JA, Tan YV. 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Acta Diabetol. 2023;60(10):1351-1358. doi:10.1007/s00592-023-02128-6\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 To 4 and 7 are available in the Supplementary Files section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u0026nbsp; MiRNA - hub gene and TF \u0026ndash; hub gene \u0026nbsp;topology table\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.9122%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003ePRKCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eNOTCH1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eFLNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-200a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCDK8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ePRKCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-142-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eGSN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eRUNX1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eSRGAP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-6834-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eHAND2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eMAPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-501-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eSRGAP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eASXL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eGSN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-4533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eVPS37C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eEOMES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCREB5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-155-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCREB5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eISL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eVPS37C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-6771-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eMAPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eESRRB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCDK8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-224-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eADRB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eTAL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eGEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-3690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eGEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eCRX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCEP128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-103a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCEP128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eNFE2L2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eADRB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-30d-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eFLNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eJUN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eFFAR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-431-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCCHCR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eSOX9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCCHCR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-15b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eFFAR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eGATA4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCCDC102B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-1911-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCCDC102B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003ePHOX2B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCTNNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-1266-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eBCL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eGTF3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCTNNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eAF4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eASPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-101-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eLGALS3BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-1252-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eASPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eRELA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eBCL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-33b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eP4HA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eSUZ12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ePSEN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-183-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eUNC93B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eHSPA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-7977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003ePSEN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eBRD4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eUNC93B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-151a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eEFEMP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eTFEB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eFANCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-mir-106b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eLGALS3BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eHOXC9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ePOU6F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-19b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eHSPA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eNR0B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eP4HA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-100-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eTCTN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eGATA3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eEFEMP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-16-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eFANCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eMYBL2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eCD74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-7706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eVTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eTFAP2A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eTCTN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-130a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003ePOU6F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eFOXH1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003eVTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.7335%;\"\u003e\n \u003cp\u003ehsa-miR-221-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003eCD74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.069%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9122%;\"\u003e\n \u003cp\u003eWDR5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Drug- hub gene topology table\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategeory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug (one examples)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eADRB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eClenbuterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eESR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eDiethylstilbestrol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eALOX5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eMinocycline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eGLP1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eExenatide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003ePRKCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003ePhosphatidyl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eSLC15A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eBenzylpenicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eIbuprofen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eMAPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eDocetaxel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eCTSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003e3-amino-5-phenylpentane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eTUBB2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eCYT997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003ePHGDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eNADH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eMAOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eSelegiline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eBCHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eIsoflurophate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eCTSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003e2-Pyridinethiol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eFBP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eMB07803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eALDH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eCrotonaldehyde\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eHPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eBentiromide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003ePPIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eCyclosporine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eQPCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eGlutamine t-butyl ester\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eB2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eDoxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eP4HA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eL-Proline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eASPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eL-Aspartic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003ePTGER4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eMisoprostol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003eCPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eInsulin Human\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.0066%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.1579%;\"\u003e\n \u003cp\u003ePDK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5263%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.3092%;\"\u003e\n \u003cp\u003eRadicicol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"KLE College of Pharamacy, Gadag 582101, Karanataka, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatics analysis, Differentially expressed genes, Type 1 diabetes mellitus, Biomarkers, Diagnosis, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-7640932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7640932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 1 diabetes mellitus (T1DM) is a metabolic disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in T1DM through adopting integrated bioinformatics tools. The gene expression Omnibus (GEO) database was used to obtain next generation sequencing data of T1DM and normal control samples. Furthermore, differentially expressed genes (DEGs) were screened using the Limma package in R bioconductor package. Gene Ontology (GO) and pathway enrichment analyses were performed by g:Profiler. The protein-protein interaction (PPI) network was plotted with IID PPI database and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC. Then, microRNAs (miRNAs) and transcription factors (TFs) in T1DM were screened out from the miRNet and NetworkAnalyst database. Then, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Moreover, a drug-hub gene interaction network of the hub genes was constructed and predicted the drug molecule against hub genes. The receiver operating characteristic (ROC) curves were generated to predict diagnostic value of hub genes. Finally we performed molecular docking, ADMET profiling and molecular dynamics simulation studies of marine derived chemical constituents using Schrodinger Suite 2025-1. A total of 958 DEGs were screened: 479 up regulated genes and 479 down regulated genes. DEG were mainly enriched in the terms of developmental process, membrane, cation binding, response to stimulus, cell periphery, ion binding, neuronal system and metabolism. Based on the data of protein-protein interaction (PPI), the top 10 hub genes (5 up regulated and 5 down regulated) were ranked, including FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6. The miRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-657, hsa-miR-1266-5p, NOTCH1 and GTF3C2 might play an important role in the pathogenesis of T1DM. The drug-hub gene interaction network showed that Clenbuterol, Diethylstilbestrol, Selegiline and Isoflurophate predicted therapeutic drugs for the T1DM. Molecular docking and molecular dynamics simulation study revealed that CMNPD5805 and CMNPD30286 as potential inhibitors of FN1 (pdb id : 3M7P) a key biomarker in pathogenesis of T1DM. These findings promote the understanding of the molecular mechanism and clinically related molecular targets for T1DM.\u003c/p\u003e","manuscriptTitle":"Integrative Gene Target Mapping, RNA Sequencing, In Silico Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Study of Marine Derived Molecules for Type 1 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 06:53:52","doi":"10.21203/rs.3.rs-7640932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54886018,"name":"Bioinformatics"},{"id":54886019,"name":"Drug Discovery, Design, \u0026 Development"},{"id":54886020,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2025-09-18T06:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 06:53:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7640932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7640932","identity":"rs-7640932","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}