Integrated bioinformatics and molecular simulation identify PIM3 as a potential mediator of nicotine-associated skin aging

Integrated bioinformatics and molecular simulation identify PIM3 as a potential mediator of nicotine-associated skin aging | 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 Integrated bioinformatics and molecular simulation identify PIM3 as a potential mediator of nicotine-associated skin aging Kun Wang, Tengfei Wang, Menghui Qin, Long Yang, Xueying Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9493613/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 Smoking is a major extrinsic risk factor for skin aging, and nicotine, the primary bioactive component of tobacco, may play an important role in this process. However, the molecular mechanisms underlying nicotine-associated skin aging remain unclear. In this study, we integrated bioinformatics, transcriptomic analysis, weighted gene co-expression network analysis, machine learning, molecular docking, and molecular dynamics simulations to identify potential targets and pathways involved in nicotine-associated skin aging. Nicotine-related targets were predicted from multiple public databases, and skin aging-related genes were obtained from the GSE85358 dataset. A total of 24 potential targets were identified, among which three core genes, PIM3, FABP3, and MAPK8, were prioritized. Functional enrichment analysis indicated that these genes were mainly involved in kinase signaling, lipid metabolism, and stress-response pathways, including PI3K–Akt and PPAR signaling. Molecular docking showed that nicotine exhibited the strongest binding affinity with PIM3, and molecular dynamics simulation supported the stability of the PIM3–nicotine complex. These findings provide evidence suggesting that nicotine-associated skin aging may be related to dysregulation of kinase signaling, lipid metabolism, and stress-response pathways, with PIM3 as a potential key mediator. Further experimental validation is warranted. nicotine skin aging WGCNA molecular docking PIM3 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Skin aging represents a prominent manifestation of organismal aging at both the morphological and tissue levels, characterized by wrinkle formation, loss of elasticity, abnormal pigmentation, and impaired skin barrier function[ 1 ]. Its development is regulated by the combined effects of intrinsic genetic factors and extrinsic environmental exposures[ 2 – 4 ], among which smoking has been recognized as a major accelerating factor[ 5 ]. Previous studies have demonstrated that long-term smoking disrupts the balance between collagen synthesis and degradation[ 6 ], promotes extracellular matrix remodeling, and induces oxidative stress[ 7 ], ultimately leading to dermal structural damage and functional impairment of the skin. Nicotine, the primary bioactive component of tobacco, is highly lipophilic and can exert systemic effects on various tissues through blood circulation[ 8 ]. It has been reported that nicotine interacts with nicotinic acetylcholine receptors (nAChRs) expressed in non-neuronal cells[ 9 ], thereby influencing cell proliferation, differentiation, and extracellular matrix metabolism in skin-related cells. In addition, nicotine can induce oxidative stress, disrupt mitochondrial function, and promote the generation of reactive oxygen species (ROS), contributing to cellular damage[ 10 ]. Key signaling pathways, including PI3K–Akt and MAPK, play essential roles in maintaining skin homeostasis and regulating cellular stress responses, and have also been implicated in nicotine-associated biological effects[ 11 , 12 ]. However, the key molecular targets and regulatory networks involved in nicotine-associated skin aging remain largely unclear. Current studies have primarily focused on the epidemiological association between smoking and skin aging or on individual signaling pathways, lacking a systematic integration of multi-source data to identify key molecular nodes[ 13 ]. In particular, for nicotine as a single active component, a comprehensive “exposure–target–pathway” regulatory framework has not yet been established[ 14 ]. Therefore, systematically identifying the core molecular targets and potential regulatory mechanisms of nicotine-associated skin aging is of great significance for understanding the pathological process and exploring potential therapeutic targets. In recent years, integrative bioinformatics and network-based approaches have been widely applied in the study of complex disease mechanisms. Through transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms, key genes closely associated with specific phenotypes can be identified at a systems level[ 15 ]. Furthermore, molecular docking and molecular dynamics simulations enable structural evaluation of interactions between small molecules and target proteins, providing additional support for candidate targets[ 16 ]. Therefore, integrating multi-database target prediction, transcriptomic analysis, machine learning, and molecular simulation offers a comprehensive strategy to elucidate the molecular mechanisms of external exposure-related biological effects from both network and structural perspectives. Based on this, we hypothesized that nicotine may contribute to skin aging by acting on specific molecular targets and perturbing key regulatory pathways. To test this hypothesis, we integrated nicotine target prediction, GEO transcriptomic data analysis, WGCNA, and machine learning methods to identify core genes, and further evaluated their interaction characteristics through molecular docking and molecular dynamics simulations[ 17 – 19 ]. This study aims to systematically identify key candidate targets and potential molecular mechanisms underlying nicotine-associated skin aging. 2. Materials and Methods The overall workflow of the study is illustrated in Fig. 1 . 2.1. Network analysis of Nicotine toxicity To obtain toxicity information on nicotine, we retrieved the SMILES structural formula (CN1CCC[C@H]1C2 = CN = CC=C2) and molecular structure of the compound from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), and exported 2D and 3D images of nicotine from this database(Fig. 2 A, B). Subsequently, the SMILES structure was imported into the ProTox-3.0 platform ( https://tox.charite.de/protox3/ ), and the prediction results were integrated to evaluate the toxicity profile of nicotine. 2.2. Targeted collection of Nicotine Using the ChEMBL database ( https://www.ebi.ac.uk/chembl/ ), Similarity Ensemble Approach (SEA, https://sea.bkslab.org/ ), TargetNet ( http://targetnet.scbdd.com/ ), and SwissTargetPrediction ( http://www.swisstargetprediction.ch/ ), we predicted potential nicotine targets. The selection criteria were as follows: targets annotated as Homo sapiens; a SwissTargetPrediction probability score ≥ 0.5 (high confidence); and a ChEMBL activity value (IC50 or Ki) ≤ 10 µM. Predictions with low confidence (probability < 0.5) and non-human targets were excluded. Gene names were normalized using UniProt, and duplicate entries were removed to construct a library of nicotine-associated targets. 2.3. Screening of skin aging targets Gene expression datasets related to skin aging were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). The datasets were normalized, and differential expression analysis was performed using the R programming language. Differentially expressed genes (DEGs) between aged (55–66 years) and young (20–25 years) skin samples in the GSE85358 dataset were identified. Weighted Gene Co-expression Network Analysis (WGCNA) was subsequently performed to identify gene co-expression modules associated with skin aging phenotypes. Key module genes obtained from WGCNA were integrated with the DEGs. Intersection analysis between DEGs and WGCNA-derived genes was conducted using a Venn diagram to identify candidate genes associated with skin aging. 2.4. Screening of Nicotine-Associated Skin Aging Genes and PPI Network Construction with Functional Enrichment Analysis The overlapping genes associated with nicotine and skin aging were imported into the STRING database (version 12.0; https://string-db.org/ ) to construct a protein–protein interaction (PPI) network, with the species restricted to Homo sapiens . The minimum interaction score was set to 0.4. Functional enrichment analysis was performed using Gene Ontology (GO; https://www.geneontology.org/ ) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/ ). GO analysis was conducted to evaluate biological process (BP), cellular component (CC), and molecular function (MF) categories, and KEGG analysis was used to identify enriched signaling pathways. The top 20 GO terms and KEGG pathways were ranked based on –log10 (P-value). Enrichment results were visualized using the ggplot2 package in R. Statistical significance was assessed using a hypergeometric test, and P-values were adjusted using the Benjamini–Hochberg method, with a false discovery rate (FDR) < 0.05. 2.5. Machine Learning-Based Identification of Core Genes Machine learning models were constructed based on the candidate genes, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). The SVM-RFE model was implemented using the e1071 and caret R packages, the LASSO model using the glmnet package, and the RF model using the randomForest package. These models were applied to the 24 overlapping genes obtained from the intersection analysis. In the LASSO model, the optimal lambda value was determined by 10-fold cross-validation. In the RF model, 500 trees (ntree = 500) were constructed, and the optimal tree number was selected based on the out-of-bag (OOB) error. Gene importance was evaluated using the mean decrease in Gini index. Genes selected by each model were further intersected to identify hub genes associated with nicotine-related skin aging. 2.6. Molecular docking Molecular docking analysis was performed using CB-Dock2 to evaluate the binding interactions between nicotine and the predicted core target proteins. The two-dimensional structure of nicotine was obtained from the PubChem database and converted from SDF (Structure Data File) format to PDB (Protein Data Bank) format using Open Babel software (version 2.4.1). The three-dimensional structures of target proteins were retrieved from the Protein Data Bank (PDB, https://www.rcsb.org/ ). CB-Dock2 was used to identify potential binding cavities and perform docking analysis. The docking process was carried out using AutoDock Vina (version 1.2.3) integrated within the CB-Dock2 platform. For each ligand–protein complex, three independent docking runs were performed. Docking conformations with a root mean square deviation (RMSD) < 2.0 Å relative to the lowest-energy conformation were retained for further analysis. All docking experiments were conducted under identical conditions. 2.7. Molecular Dynamics Simulation Molecular dynamics (MD) simulations were performed using GROMACS to evaluate the nicotine–target protein complex. The Amber99SB-ILDN force field was applied to the protein, and the General Amber Force Field (GAFF) was used for the ligand. The system was solvated in a TIP3P water box. Energy minimization and equilibration were performed under NVT and NPT conditions (100 ps each), followed by a 100-ns production simulation. 3. Results 3.1. Nicotine toxicity analysis Toxicity prediction using the ProTox-3.0 platform indicated that nicotine exhibits multiple potential toxicological activities, including high blood–brain barrier permeability (Fig. 2 C). 3.2. Identification of nicotine-related targets Targets were predicted using multiple databases, including ChEMBL, SEA, TargetNet, and SwissTargetPrediction (Fig. 2 D). A total of 202 targets were identified from ChEMBL, 25 from SEA, 23 from SwissTargetPrediction, and 119 from TargetNet. Overlap analysis showed limited intersection among the four databases. After removing duplicate entries, a total of 330 nicotine-related targets were obtained. 3.3. Identification of skin aging-related genes Differential expression analysis was performed between young and aged skin samples. The heatmap showed distinct gene expression patterns between the two groups, with samples clustering according to group (Fig. 3 A). A total of 2423 differentially expressed genes (DEGs) were identified based on the criteria of P 0.25, including 1237 up-regulated and 1186 down-regulated genes in the aged group compared with the young group. The distribution of DEGs is shown in the volcano plot (Fig. 3 B). Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify gene co-expression modules. The sample clustering dendrogram and trait heatmap showed clear grouping of samples (Fig. 3 C). The network satisfied the scale-free topology criterion (Fig. 3 D). Genes were clustered into distinct modules based on hierarchical clustering and dynamic tree cutting (Fig. 3 E). Module–trait relationship analysis identified several modules associated with skin aging. The blue module showed the strongest positive correlation with the aged group (r = 0.59, P = 1e − 05) and a negative correlation with the young group (r = − 0.59, P = 1e − 05) (Fig. 3 F). The blue module contained 5,528 genes. 3.4. Functional and pathway enrichment analysis of potential targets Venn diagram analysis identified 24 overlapping genes among the differentially expressed genes (DEGs), genes from the key WGCNA blue module, and the 330 predicted nicotine-related targets (Fig. 4 A). GO and KEGG enrichment analyses were performed for the overlapping genes. KEGG analysis showed that these genes were enriched in pathways related to lipid metabolism and metabolic homeostasis, including the PPAR signaling pathway, adipocytokine signaling pathway, insulin resistance, and lipid and atherosclerosis (Fig. 4 C). Pathways associated with oxidative stress and inflammatory responses were also enriched, including chemical carcinogenesis–reactive oxygen species and C-type lectin receptor signaling pathway. In addition, pathways related to cell survival and signal transduction were identified, including the PI3K–Akt signaling pathway, Rap1 signaling pathway, cAMP signaling pathway, and ErbB signaling pathway. GO enrichment analysis showed that, in the biological process (BP) category, the enriched terms were mainly associated with protein phosphorylation, regulation of apoptotic process, response to hypoxia, cell migration, and positive regulation of MAPK cascade (Fig. 4 D). In the cellular component (CC) category, these genes were enriched in cytosol, nucleoplasm, membrane raft, and receptor complex (Fig. 4 E). In the molecular function (MF) category, enrichment was observed in protein kinase activity, protein serine/threonine kinase activity, ATP binding, and nuclear receptor activity (Fig. 4 F). 3.5. Identification of hub genes using machine learning algorithms Three machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF), were applied to the 24 candidate genes. For the SVM-RFE model, the highest classification accuracy (0.879) and the lowest error rate (0.121) were obtained when the number of features was set to 6, and 6 genes were selected (Fig. 5 A, B). In the LASSO model, the optimal λ value was determined by cross-validation (Fig. 5 C, D), and 12 genes were retained. In the RF model, the model error stabilized with an increasing number of trees (Fig. 5 E), and genes were ranked based on the mean decrease in Gini index. The top 6 genes were selected (Fig. 5 F). The results from the three models were integrated using Venn diagram analysis. Three overlapping genes (PIM3, FABP3, and MAPK8) were identified (Fig. 5 G). 3.6. Expression validation of hub genes Expression levels of the identified hub genes were analyzed in the GSE85358 dataset. PIM3 was significantly up-regulated in the aged group (P < 0.0001), whereas FABP3 and MAPK8 were significantly down-regulated (P < 0.001) compared with the young group (Fig. 6 A–C). 3.7. Molecular docking analysis Molecular docking was performed between nicotine and the identified hub proteins (PIM3, FABP3, and MAPK8). The binding energies of nicotine with PIM3, FABP3, and MAPK8 were − 6.3 kcal/mol, − 5.6 kcal/mol, and − 5.4 kcal/mol, respectively (Fig. 6 D–F). Nicotine showed the lowest binding energy with PIM3. Interaction analysis showed that nicotine bound to the active pockets of all three proteins through hydrogen bonding and hydrophobic interactions. Compared with FABP3 and MAPK8, the nicotine–PIM3 complex exhibited more binding contacts. 3.8. Molecular dynamics simulation and free energy landscape analysis A 100-ns molecular dynamics (MD) simulation was performed for the PIM3–nicotine complex. RMSD analysis showed that the system reached a stable state after initial fluctuations. The RMSD of the protein backbone ranged from 0.20 to 0.31 nm, while the complex ranged from 0.32 to 0.40 nm, with a transient increase observed around 40 ns (Fig. 7 A). The radius of gyration (Rg) fluctuated between 1.90 and 1.95 nm during the simulation (Fig. 7 B). The solvent-accessible surface area (SASA) ranged from 130 to 140 nm² with minor fluctuations (Fig. 7 C). RMSF analysis showed that most residues exhibited low fluctuation values (0.05–0.20 nm), with higher fluctuations observed at the terminal regions (Fig. 7 D). The number of hydrogen bonds fluctuated between 180 and 220 throughout the simulation (Fig. 7 E). The free energy landscape (FEL) showed a low-energy basin concentrated within a limited conformational space (Fig. 7 F). 4. Discussion Skin aging is driven by the combined effects of intrinsic biological processes and environmental exposures, among which smoking is a well-recognized contributor[ 20 , 21 ]. However, the molecular mechanisms linking nicotine exposure to skin aging remain incompletely understood. In this study, we integrated transcriptomic analysis, WGCNA, machine learning, molecular docking, and molecular dynamics simulations to systematically identify key genes and pathways involved in nicotine-associated skin aging[ 22 ]. Our results revealed substantial transcriptional remodeling in aged skin, with the blue module identified by WGCNA showing the strongest association with the aging phenotype. By integrating module genes, DEGs, and nicotine-related targets, we identified candidate genes. Functional enrichment analysis suggested that these genes are primarily involved in metabolic regulation and stress-response pathways, including the PPAR signaling pathway, adipocytokine signaling pathway, PI3K–Akt signaling, and MAPK-related processes[ 23 ]. These findings indicate that nicotine-associated skin aging may involve coordinated dysregulation of metabolic homeostasis and intracellular signaling networks rather than isolated gene effects[ 24 ]. Notably, pathways related to lipid metabolism were prominently enriched, including PPAR signaling, adipocytokine signaling, and insulin resistance[ 25 , 26 ]. Lipid metabolism plays a critical role in maintaining skin barrier function and cellular homeostasis, and its disruption may contribute to increased vulnerability of aging skin. Consistently, FABP3, a gene involved in fatty acid transport, was identified as a hub gene and was significantly downregulated in aged samples, suggesting impaired lipid metabolic capacity during skin aging[ 27 ]. In addition, signaling pathways associated with cellular stress responses were also enriched. In particular, protein phosphorylation, regulation of the MAPK cascade, and PI3K–Akt signaling were implicated, suggesting that nicotine may influence skin aging through modulation of kinase-dependent signaling networks[ 28 ]. These pathways are closely associated with oxidative stress, apoptosis, and cellular survival, which are key processes in aging-related tissue degeneration. Using three machine learning algorithms, we identified PIM3, FABP3, and MAPK8 as hub genes. Expression analysis showed that PIM3 was upregulated, whereas FABP3 and MAPK8 were downregulated in aged skin. As a serine/threonine kinase, PIM3 may function as a stress-responsive or compensatory survival factor under chronic nicotine exposure. In contrast, the downregulation of FABP3 and MAPK8 may reflect impaired metabolic regulation and reduced adaptive stress-response capacity in aging skin[ 29 ]. Molecular docking analysis further demonstrated that nicotine could bind to all three hub proteins, with the strongest binding affinity observed for PIM3. Molecular dynamics simulations supported the structural stability of the PIM3–nicotine complex, indicating the potential biological relevance of this interaction[ 30 ]. Taken together, these findings suggest that PIM3 may represent a key mediator linking nicotine exposure to dysregulated signaling processes in skin aging. Despite these findings, several limitations should be noted. First, the present study is primarily based on in silico analyses, and experimental validation is required to confirm the biological functions of the identified targets[ 31 , 32 ]. Second, the functional roles of the hub genes were inferred from computational analyses rather than direct experimental evidence. Future studies should focus on validating these targets in cellular and animal models and further elucidating their mechanistic roles in nicotine-induced skin aging. In conclusion, nicotine-associated skin aging appears to be closely related to the dysregulation of metabolic pathways and stress-response signaling networks. PIM3, FABP3, and MAPK8 were identified as key genes, with PIM3 showing the strongest interaction potential with nicotine. These findings provide mechanistic insights into nicotine-induced skin aging and may offer potential targets for future therapeutic intervention. 5. Conclusion This study integrates transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), machine learning, molecular docking, and molecular dynamics simulation to investigate the molecular mechanisms underlying nicotine-associated skin aging. A total of 24 potential targets were identified, and three core genes (PIM3, FABP3, and MAPK8) were prioritized. The results suggest that nicotine-associated skin aging is closely related to dysregulation of metabolic homeostasis and stress-response signaling pathways, including the PPAR signaling pathway, adipocytokine signaling pathway, PI3K–Akt signaling, and MAPK-related processes. Among these, PIM3 showed the strongest binding affinity with nicotine and stable interaction dynamics, suggesting its potential role in nicotine-associated skin aging. These findings provide insights into the molecular mechanisms of nicotine-induced skin aging. Further experimental studies are required to validate these results and to explore their potential applications in skin aging research. Abbreviations DEGs: Differentially expressed genes WGCNA: Weighted gene co-expression network analysis PPI: Protein–protein interaction GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes SVM-RFE: Support Vector Machine Recursive Feature Elimination LASSO: Least Absolute Shrinkage and Selection Operator RF: Random Forest PDB: Protein Data Bank RMSD: Root mean square deviation RMSF: Root mean square fluctuation Rg: Radius of gyration SASA: Solvent-accessible surface area MD: Molecular dynamics FEL: Free energy landscape Declarations Acknowledgements Not applicable Data availability statement The datasets analysed in the current study are publicly available. Gene expression data were obtained from the Gene Expression Omnibus (GEO) under accession number GSE85358 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85358). All other data generated or analysed during this study are included in this published article and its supplementary information files. Author Contributions Kun Wang, Tengfei Wang and Menghui Qin contributed equally to this work. Mengru Pang and Yu Lei performed conceptualization. Mengru Pang and Yu Lei performed investigation. Mengru Pang performed funding acquisition. Mengru Pang and Yu Lei performed project administration. Mengru Pang and Yu Lei performed supervision. Kun Wang , Tengfei Wang and Long Yang performed writing of original draft. Xueying Zhang, Mengru Pang, and Yu Lei, performed writing, reviewing & editing. Ethics approval This study was conducted using publicly available datasets (GEO: GSE85358). All data were obtained from databases with existing ethical approvals or in compliance with relevant guidelines. No experiments involving human participants or animals were performed by the authors. Therefore, ethical approval and informed consent were waived. Competing interests The authors declare no competing interests. Funding statement: This work was supported by the National Natural Science Foundation of China (82160377); the National Natural Science Foundation (NSFC) Cultivation Project of Guizhou Medical University Affiliated Hospital(gyfynsfc-2021-28); the Science and Technology Fund Project of Guizhou Provincial Health Commission (gzwjkj-2020-2- 003). Clinical trial number Not applicable. References Naharro-Rodriguez J, Bacci S, Hernandez-Bule ML, Perez-Gonzalez A, Fernandez-Guarino M (2025) Decoding skin aging: a review of mechanisms, markers, and modern therapies. Cosmetics 12:144 Shin SH, Lee YH, Rho NK, Park K (2023) Y. Skin aging from mechanisms to interventions: focusing on dermal aging. Front Physiol 14:1195272 Furman D, Auwerx J, Bulteau AL et al (2025) Skin health and biological aging. 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Supplementary Files SupplementaryMethods.pdf SupplementaryTables.zip SupplementaryCode.zip SupplementaryMDDocking.zip 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-9493613","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633707180,"identity":"48ad5a5e-8b7a-49ea-ba3d-855e48696060","order_by":0,"name":"Kun Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":633707181,"identity":"4d6236f1-d5e7-4346-91db-3734c997e45d","order_by":1,"name":"Tengfei Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tengfei","middleName":"","lastName":"Wang","suffix":""},{"id":633707182,"identity":"832151d3-e90d-4f0a-98d8-d09b14263c0d","order_by":2,"name":"Menghui Qin","email":"","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Menghui","middleName":"","lastName":"Qin","suffix":""},{"id":633707183,"identity":"ce27dc30-32bf-48cb-95c0-139408c3cddc","order_by":3,"name":"Long Yang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Yang","suffix":""},{"id":633707184,"identity":"1fd9505b-4f6e-495a-b541-7211a3bee178","order_by":4,"name":"Xueying Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Zhang","suffix":""},{"id":633707186,"identity":"4d348d00-362a-4fea-a49c-a46e94d4d99c","order_by":5,"name":"Mengru Pang","email":"","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengru","middleName":"","lastName":"Pang","suffix":""},{"id":633707190,"identity":"a8824bf9-f52f-4735-bd57-e90934bb9f4b","order_by":6,"name":"Yu Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYPACCQZ+ZubDD0jTItnOlmZAmj0G53kUJIhSKR+RYyZdUWORZ3yYh8GAocYmmqAWwxs5ZpJnjkkUmx3mPfCA4VhabgNBLTOAWhrYJBK3HeZLMGBsOEysln8SiZubeQwkiNIiLwHU0tgmkbiBmVgtBjzPii0b+yQSZxwGBnICMX6Rb0/eeLPhW11if//hww8+1NgQYcuFDBNEdCQQUg62pf/44w/EKBwFo2AUjIIRDABkoz3p8zM1hgAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Hospital of Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2026-04-22 09:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9493613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9493613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108778157,"identity":"0ab80c8e-ad55-46c7-8829-28624c3284dd","added_by":"auto","created_at":"2026-05-08 09:42:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2092305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/ec83d56f235d4c20292cf695.png"},{"id":108778079,"identity":"92fc433b-19e2-4901-8217-edbec7e2d37d","added_by":"auto","created_at":"2026-05-08 09:42:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1311644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural characteristics, toxicity prediction, and target identification of nicotine.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Two-dimensional (2D) chemical structure of nicotine.\u003cbr\u003e\n(B) Three-dimensional (3D) molecular structure of nicotine.\u003cbr\u003e\n(C) Toxicity profile of nicotine predicted using the ProTox-3.0 platform.\u003cbr\u003e\n(D) Venn diagram showing the distribution and overlap of nicotine-related targets predicted from multiple databases, including ChEMBL, SEA, TargetNet, and SwissTargetPrediction.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/03e2e808917b4e31c096ac6a.png"},{"id":108778143,"identity":"2b415c0f-691a-4595-abd7-63c245346e4b","added_by":"auto","created_at":"2026-05-08 09:42:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2000493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of skin aging-related genes and WGCNA analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap showing the expression patterns of differentially expressed genes (DEGs) between young and aged skin samples.\u003cbr\u003e\n(B) Volcano plot of DEGs, with up-regulated genes shown in red and down-regulated genes in blue.\u003cbr\u003e\n(C) Sample clustering dendrogram and trait heatmap indicating the distribution of young and aged samples.\u003cbr\u003e\n(D) Determination of the soft-thresholding power for WGCNA based on scale-free topology fit index (left) and mean connectivity (right).\u003cbr\u003e\n(E) Gene dendrogram and module color assignment identified by dynamic tree cutting.\u003cbr\u003e\n(F) Module–trait relationship heatmap showing the correlation between gene modules and clinical traits (young and aged).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/482bba02fc9ba80854030177.png"},{"id":108778158,"identity":"2b5c83d5-fb73-424f-a66f-091572f07436","added_by":"auto","created_at":"2026-05-08 09:42:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1676727,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of nicotine-associated skin aging targets and functional enrichment analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram showing the overlap among differentially expressed genes (DEGs), WGCNA-derived module genes, and nicotine-related targets. A total of 24 overlapping genes were identified and are listed on the right.\u003cbr\u003e\n(B) Protein–protein interaction (PPI) network of the overlapping genes constructed using the STRING database.\u003cbr\u003e\n(C) KEGG pathway enrichment analysis of the overlapping genes.\u003cbr\u003e\n(D) Gene Ontology (GO) enrichment analysis for biological processes (BP).\u003cbr\u003e\n(E) GO enrichment analysis for cellular components (CC).\u003cbr\u003e\n(F) GO enrichment analysis for molecular functions (MF).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/d59fcd863de998089a8b2731.png"},{"id":108778140,"identity":"e528d2ca-cfc3-446f-9143-f74415e54315","added_by":"auto","created_at":"2026-05-08 09:42:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":912441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of hub genes using machine learning algorithms.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Classification accuracy of the support vector machine recursive feature elimination (SVM-RFE) model under different numbers of selected features.\u003cbr\u003e\n(B) Cross-validation error of the SVM-RFE model under different numbers of selected features.\u003cbr\u003e\n(C) Coefficient profiles of candidate genes in the least absolute shrinkage and selection operator (LASSO) model.\u003cbr\u003e\n(D) Cross-validation curve for selection of the optimal lambda value in the LASSO model.\u003cbr\u003e\n(E) Error rate of the random forest (RF) model with increasing number of trees.\u003cbr\u003e\n(F) Ranking of candidate genes based on importance scores in the RF model.\u003cbr\u003e\n(G) Venn diagram showing the overlap of genes identified by the LASSO, SVM-RFE, and RF algorithms.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/f20304746714a9166786343f.png"},{"id":108778075,"identity":"a2b0fe4f-6eed-4bfc-8d8a-e32790e928bd","added_by":"auto","created_at":"2026-05-08 09:42:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3106171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression validation of hub genes and molecular docking analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Expression levels of hub genes in young and aged skin samples. (A) PIM3 expression, (B) FABP3 expression, and (C) MAPK8 expression. Statistical significance is indicated (****P \u0026lt; 0.0001). (D–F) Molecular docking analysis of nicotine with hub proteins. (D) PIM3–nicotine complex, (E) FABP3–nicotine complex, and (F) MAPK8–nicotine complex. The binding modes of nicotine within the active pockets of target proteins are shown, along with corresponding two-dimensional interaction diagrams. Binding energies are indicated for each complex.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/d37c4a149583ccf79583fb94.png"},{"id":108778236,"identity":"27429d3f-c995-4ef3-bbc5-77bc3a7dcb90","added_by":"auto","created_at":"2026-05-08 09:42:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1093042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation and free energy landscape analysis of the PIM3–nicotine complex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Root mean square deviation (RMSD) of the protein and protein–ligand complex during the simulation.\u003cbr\u003e\n(B) Radius of gyration (Rg) of the protein, including total Rg and components along the X, Y, and Z axes.\u003cbr\u003e\n(C) Solvent-accessible surface area (SASA) of the complex over the simulation time.\u003cbr\u003e\n(D) Root mean square fluctuation (RMSF) of residues in the protein.\u003cbr\u003e\n(E) Number of hydrogen bonds formed during the simulation.\u003cbr\u003e\n(F) Free energy landscape (FEL) of the complex based on principal component analysis (PC1 and PC2).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/37f931db70c63274e83972e4.png"},{"id":109204982,"identity":"89865c99-b93b-4435-8915-c67cea9d4f7e","added_by":"auto","created_at":"2026-05-13 15:03:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12034245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/ed77e40a-e5ee-4857-9db0-754882f588fe.pdf"},{"id":108778120,"identity":"955c2d67-7be9-4929-8c84-b2a6298b7770","added_by":"auto","created_at":"2026-05-08 09:42:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3933,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/2e0fb46d652e470d18e3a216.pdf"},{"id":108778153,"identity":"d9a29969-89e5-4348-b39d-0227d68c39e3","added_by":"auto","created_at":"2026-05-08 09:42:33","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13535571,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.zip","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/ccb69986f181bb565d83addc.zip"},{"id":108778124,"identity":"a77630f8-7697-4438-877a-d085f78c04ce","added_by":"auto","created_at":"2026-05-08 09:42:31","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13413,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryCode.zip","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/ce77059d3a812a9b86841a0f.zip"},{"id":108778123,"identity":"1dc35d14-ce2e-413e-9b25-03d4921a9ab0","added_by":"auto","created_at":"2026-05-08 09:42:31","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1366548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMDDocking.zip","url":"https://assets-eu.researchsquare.com/files/rs-9493613/v1/03422a16ba9c6e6b8a1cb51c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated bioinformatics and molecular simulation identify PIM3 as a potential mediator of nicotine-associated skin aging","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSkin aging represents a prominent manifestation of organismal aging at both the morphological and tissue levels, characterized by wrinkle formation, loss of elasticity, abnormal pigmentation, and impaired skin barrier function[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its development is regulated by the combined effects of intrinsic genetic factors and extrinsic environmental exposures[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], among which smoking has been recognized as a major accelerating factor[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have demonstrated that long-term smoking disrupts the balance between collagen synthesis and degradation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], promotes extracellular matrix remodeling, and induces oxidative stress[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], ultimately leading to dermal structural damage and functional impairment of the skin. Nicotine, the primary bioactive component of tobacco, is highly lipophilic and can exert systemic effects on various tissues through blood circulation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has been reported that nicotine interacts with nicotinic acetylcholine receptors (nAChRs) expressed in non-neuronal cells[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], thereby influencing cell proliferation, differentiation, and extracellular matrix metabolism in skin-related cells. In addition, nicotine can induce oxidative stress, disrupt mitochondrial function, and promote the generation of reactive oxygen species (ROS), contributing to cellular damage[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Key signaling pathways, including PI3K\u0026ndash;Akt and MAPK, play essential roles in maintaining skin homeostasis and regulating cellular stress responses, and have also been implicated in nicotine-associated biological effects[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the key molecular targets and regulatory networks involved in nicotine-associated skin aging remain largely unclear. Current studies have primarily focused on the epidemiological association between smoking and skin aging or on individual signaling pathways, lacking a systematic integration of multi-source data to identify key molecular nodes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In particular, for nicotine as a single active component, a comprehensive \u0026ldquo;exposure\u0026ndash;target\u0026ndash;pathway\u0026rdquo; regulatory framework has not yet been established[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, systematically identifying the core molecular targets and potential regulatory mechanisms of nicotine-associated skin aging is of great significance for understanding the pathological process and exploring potential therapeutic targets. In recent years, integrative bioinformatics and network-based approaches have been widely applied in the study of complex disease mechanisms. Through transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms, key genes closely associated with specific phenotypes can be identified at a systems level[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, molecular docking and molecular dynamics simulations enable structural evaluation of interactions between small molecules and target proteins, providing additional support for candidate targets[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, integrating multi-database target prediction, transcriptomic analysis, machine learning, and molecular simulation offers a comprehensive strategy to elucidate the molecular mechanisms of external exposure-related biological effects from both network and structural perspectives. Based on this, we hypothesized that nicotine may contribute to skin aging by acting on specific molecular targets and perturbing key regulatory pathways. To test this hypothesis, we integrated nicotine target prediction, GEO transcriptomic data analysis, WGCNA, and machine learning methods to identify core genes, and further evaluated their interaction characteristics through molecular docking and molecular dynamics simulations[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study aims to systematically identify key candidate targets and potential molecular mechanisms underlying nicotine-associated skin aging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe overall workflow of the study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Network analysis of Nicotine toxicity\u003c/h2\u003e \u003cp\u003eTo obtain toxicity information on nicotine, we retrieved the SMILES structural formula (CN1CCC[C@H]1C2\u0026thinsp;=\u0026thinsp;CN\u0026thinsp;=\u0026thinsp;CC=C2) and molecular structure of the compound from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and exported 2D and 3D images of nicotine from this database(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Subsequently, the SMILES structure was imported into the ProTox-3.0 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tox.charite.de/protox3/\u003c/span\u003e\u003cspan address=\"https://tox.charite.de/protox3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the prediction results were integrated to evaluate the toxicity profile of nicotine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Targeted collection of Nicotine\u003c/h2\u003e \u003cp\u003eUsing the ChEMBL database (\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), Similarity Ensemble Approach (SEA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sea.bkslab.org/\u003c/span\u003e\u003cspan address=\"https://sea.bkslab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), TargetNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://targetnet.scbdd.com/\u003c/span\u003e\u003cspan address=\"http://targetnet.scbdd.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and SwissTargetPrediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we predicted potential nicotine targets. The selection criteria were as follows: targets annotated as Homo sapiens; a SwissTargetPrediction probability score\u0026thinsp;\u0026ge;\u0026thinsp;0.5 (high confidence); and a ChEMBL activity value (IC50 or Ki)\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;M. Predictions with low confidence (probability\u0026thinsp;\u0026lt;\u0026thinsp;0.5) and non-human targets were excluded. Gene names were normalized using UniProt, and duplicate entries were removed to construct a library of nicotine-associated targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Screening of skin aging targets\u003c/h2\u003e \u003cp\u003eGene expression datasets related to skin aging were obtained from the Gene Expression Omnibus (GEO) database (\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). The datasets were normalized, and differential expression analysis was performed using the R programming language. Differentially expressed genes (DEGs) between aged (55\u0026ndash;66 years) and young (20\u0026ndash;25 years) skin samples in the GSE85358 dataset were identified. Weighted Gene Co-expression Network Analysis (WGCNA) was subsequently performed to identify gene co-expression modules associated with skin aging phenotypes. Key module genes obtained from WGCNA were integrated with the DEGs. Intersection analysis between DEGs and WGCNA-derived genes was conducted using a Venn diagram to identify candidate genes associated with skin aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Screening of Nicotine-Associated Skin Aging Genes and PPI Network Construction with Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe overlapping genes associated with nicotine and skin aging were imported into the STRING database (version 12.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a protein\u0026ndash;protein interaction (PPI) network, with the species restricted to \u003cem\u003eHomo sapiens\u003c/em\u003e. The minimum interaction score was set to 0.4. Functional enrichment analysis was performed using Gene Ontology (GO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"https://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GO analysis was conducted to evaluate biological process (BP), cellular component (CC), and molecular function (MF) categories, and KEGG analysis was used to identify enriched signaling pathways. The top 20 GO terms and KEGG pathways were ranked based on \u0026ndash;log10 (P-value). Enrichment results were visualized using the ggplot2 package in R. Statistical significance was assessed using a hypergeometric test, and P-values were adjusted using the Benjamini\u0026ndash;Hochberg method, with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Machine Learning-Based Identification of Core Genes\u003c/h2\u003e \u003cp\u003eMachine learning models were constructed based on the candidate genes, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). The SVM-RFE model was implemented using the e1071 and caret R packages, the LASSO model using the glmnet package, and the RF model using the randomForest package. These models were applied to the 24 overlapping genes obtained from the intersection analysis. In the LASSO model, the optimal lambda value was determined by 10-fold cross-validation. In the RF model, 500 trees (ntree\u0026thinsp;=\u0026thinsp;500) were constructed, and the optimal tree number was selected based on the out-of-bag (OOB) error. Gene importance was evaluated using the mean decrease in Gini index. Genes selected by each model were further intersected to identify hub genes associated with nicotine-related skin aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking analysis was performed using CB-Dock2 to evaluate the binding interactions between nicotine and the predicted core target proteins. The two-dimensional structure of nicotine was obtained from the PubChem database and converted from SDF (Structure Data File) format to PDB (Protein Data Bank) format using Open Babel software (version 2.4.1). The three-dimensional structures of target proteins were retrieved from 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). CB-Dock2 was used to identify potential binding cavities and perform docking analysis. The docking process was carried out using AutoDock Vina (version 1.2.3) integrated within the CB-Dock2 platform. For each ligand\u0026ndash;protein complex, three independent docking runs were performed. Docking conformations with a root mean square deviation (RMSD)\u0026thinsp;\u0026lt;\u0026thinsp;2.0 \u0026Aring; relative to the lowest-energy conformation were retained for further analysis. All docking experiments were conducted under identical conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were performed using GROMACS to evaluate the nicotine\u0026ndash;target protein complex. The Amber99SB-ILDN force field was applied to the protein, and the General Amber Force Field (GAFF) was used for the ligand. The system was solvated in a TIP3P water box. Energy minimization and equilibration were performed under NVT and NPT conditions (100 ps each), followed by a 100-ns production simulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Nicotine toxicity analysis\u003c/h2\u003e\n \u003cp\u003eToxicity prediction using the ProTox-3.0 platform indicated that nicotine exhibits multiple potential toxicological activities, including high blood\u0026ndash;brain barrier permeability (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Identification of nicotine-related targets\u003c/h2\u003e\n \u003cp\u003eTargets were predicted using multiple databases, including ChEMBL, SEA, TargetNet, and SwissTargetPrediction (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A total of 202 targets were identified from ChEMBL, 25 from SEA, 23 from SwissTargetPrediction, and 119 from TargetNet. Overlap analysis showed limited intersection among the four databases. After removing duplicate entries, a total of 330 nicotine-related targets were obtained.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Identification of skin aging-related genes\u003c/h2\u003e\n \u003cp\u003eDifferential expression analysis was performed between young and aged skin samples. The heatmap showed distinct gene expression patterns between the two groups, with samples clustering according to group (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A total of 2423 differentially expressed genes (DEGs) were identified based on the criteria of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 0.25, including 1237 up-regulated and 1186 down-regulated genes in the aged group compared with the young group. The distribution of DEGs is shown in the volcano plot (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify gene co-expression modules. The sample clustering dendrogram and trait heatmap showed clear grouping of samples (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The network satisfied the scale-free topology criterion (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Genes were clustered into distinct modules based on hierarchical clustering and dynamic tree cutting (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Module\u0026ndash;trait relationship analysis identified several modules associated with skin aging. The blue module showed the strongest positive correlation with the aged group (r\u0026thinsp;=\u0026thinsp;0.59, P\u0026thinsp;=\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;05) and a negative correlation with the young group (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.59, P\u0026thinsp;=\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;05) (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The blue module contained 5,528 genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Functional and pathway enrichment analysis of potential targets\u003c/h2\u003e\n \u003cp\u003eVenn diagram analysis identified 24 overlapping genes among the differentially expressed genes (DEGs), genes from the key WGCNA blue module, and the 330 predicted nicotine-related targets (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). GO and KEGG enrichment analyses were performed for the overlapping genes. KEGG analysis showed that these genes were enriched in pathways related to lipid metabolism and metabolic homeostasis, including the PPAR signaling pathway, adipocytokine signaling pathway, insulin resistance, and lipid and atherosclerosis (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Pathways associated with oxidative stress and inflammatory responses were also enriched, including chemical carcinogenesis\u0026ndash;reactive oxygen species and C-type lectin receptor signaling pathway. In addition, pathways related to cell survival and signal transduction were identified, including the PI3K\u0026ndash;Akt signaling pathway, Rap1 signaling pathway, cAMP signaling pathway, and ErbB signaling pathway. GO enrichment analysis showed that, in the biological process (BP) category, the enriched terms were mainly associated with protein phosphorylation, regulation of apoptotic process, response to hypoxia, cell migration, and positive regulation of MAPK cascade (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In the cellular component (CC) category, these genes were enriched in cytosol, nucleoplasm, membrane raft, and receptor complex (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In the molecular function (MF) category, enrichment was observed in protein kinase activity, protein serine/threonine kinase activity, ATP binding, and nuclear receptor activity (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Identification of hub genes using machine learning algorithms\u003c/h2\u003e\n \u003cp\u003eThree machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF), were applied to the 24 candidate genes. For the SVM-RFE model, the highest classification accuracy (0.879) and the lowest error rate (0.121) were obtained when the number of features was set to 6, and 6 genes were selected (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). In the LASSO model, the optimal \u0026lambda; value was determined by cross-validation (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D), and 12 genes were retained. In the RF model, the model error stabilized with an increasing number of trees (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), and genes were ranked based on the mean decrease in Gini index. The top 6 genes were selected (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eThe results from the three models were integrated using Venn diagram analysis. Three overlapping genes (PIM3, FABP3, and MAPK8) were identified (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Expression validation of hub genes\u003c/h2\u003e\n \u003cp\u003eExpression levels of the identified hub genes were analyzed in the GSE85358 dataset. PIM3 was significantly up-regulated in the aged group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), whereas FABP3 and MAPK8 were significantly down-regulated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with the young group (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Molecular docking analysis\u003c/h2\u003e\n \u003cp\u003eMolecular docking was performed between nicotine and the identified hub proteins (PIM3, FABP3, and MAPK8). The binding energies of nicotine with PIM3, FABP3, and MAPK8 were \u0026minus;\u0026thinsp;6.3 kcal/mol, \u0026minus;\u0026thinsp;5.6 kcal/mol, and \u0026minus;\u0026thinsp;5.4 kcal/mol, respectively (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u0026ndash;F). Nicotine showed the lowest binding energy with PIM3. Interaction analysis showed that nicotine bound to the active pockets of all three proteins through hydrogen bonding and hydrophobic interactions. Compared with FABP3 and MAPK8, the nicotine\u0026ndash;PIM3 complex exhibited more binding contacts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8. Molecular dynamics simulation and free energy landscape analysis\u003c/h2\u003e\n \u003cp\u003eA 100-ns molecular dynamics (MD) simulation was performed for the PIM3\u0026ndash;nicotine complex. RMSD analysis showed that the system reached a stable state after initial fluctuations. The RMSD of the protein backbone ranged from 0.20 to 0.31 nm, while the complex ranged from 0.32 to 0.40 nm, with a transient increase observed around 40 ns (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The radius of gyration (Rg) fluctuated between 1.90 and 1.95 nm during the simulation (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The solvent-accessible surface area (SASA) ranged from 130 to 140 nm\u0026sup2; with minor fluctuations (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). RMSF analysis showed that most residues exhibited low fluctuation values (0.05\u0026ndash;0.20 nm), with higher fluctuations observed at the terminal regions (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The number of hydrogen bonds fluctuated between 180 and 220 throughout the simulation (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The free energy landscape (FEL) showed a low-energy basin concentrated within a limited conformational space (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSkin aging is driven by the combined effects of intrinsic biological processes and environmental exposures, among which smoking is a well-recognized contributor[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the molecular mechanisms linking nicotine exposure to skin aging remain incompletely understood. In this study, we integrated transcriptomic analysis, WGCNA, machine learning, molecular docking, and molecular dynamics simulations to systematically identify key genes and pathways involved in nicotine-associated skin aging[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results revealed substantial transcriptional remodeling in aged skin, with the blue module identified by WGCNA showing the strongest association with the aging phenotype. By integrating module genes, DEGs, and nicotine-related targets, we identified candidate genes. Functional enrichment analysis suggested that these genes are primarily involved in metabolic regulation and stress-response pathways, including the PPAR signaling pathway, adipocytokine signaling pathway, PI3K\u0026ndash;Akt signaling, and MAPK-related processes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings indicate that nicotine-associated skin aging may involve coordinated dysregulation of metabolic homeostasis and intracellular signaling networks rather than isolated gene effects[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, pathways related to lipid metabolism were prominently enriched, including PPAR signaling, adipocytokine signaling, and insulin resistance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Lipid metabolism plays a critical role in maintaining skin barrier function and cellular homeostasis, and its disruption may contribute to increased vulnerability of aging skin. Consistently, FABP3, a gene involved in fatty acid transport, was identified as a hub gene and was significantly downregulated in aged samples, suggesting impaired lipid metabolic capacity during skin aging[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, signaling pathways associated with cellular stress responses were also enriched. In particular, protein phosphorylation, regulation of the MAPK cascade, and PI3K\u0026ndash;Akt signaling were implicated, suggesting that nicotine may influence skin aging through modulation of kinase-dependent signaling networks[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These pathways are closely associated with oxidative stress, apoptosis, and cellular survival, which are key processes in aging-related tissue degeneration.\u003c/p\u003e \u003cp\u003eUsing three machine learning algorithms, we identified PIM3, FABP3, and MAPK8 as hub genes. Expression analysis showed that PIM3 was upregulated, whereas FABP3 and MAPK8 were downregulated in aged skin. As a serine/threonine kinase, PIM3 may function as a stress-responsive or compensatory survival factor under chronic nicotine exposure. In contrast, the downregulation of FABP3 and MAPK8 may reflect impaired metabolic regulation and reduced adaptive stress-response capacity in aging skin[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular docking analysis further demonstrated that nicotine could bind to all three hub proteins, with the strongest binding affinity observed for PIM3. Molecular dynamics simulations supported the structural stability of the PIM3\u0026ndash;nicotine complex, indicating the potential biological relevance of this interaction[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Taken together, these findings suggest that PIM3 may represent a key mediator linking nicotine exposure to dysregulated signaling processes in skin aging.\u003c/p\u003e \u003cp\u003eDespite these findings, several limitations should be noted. First, the present study is primarily based on in silico analyses, and experimental validation is required to confirm the biological functions of the identified targets[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Second, the functional roles of the hub genes were inferred from computational analyses rather than direct experimental evidence. Future studies should focus on validating these targets in cellular and animal models and further elucidating their mechanistic roles in nicotine-induced skin aging.\u003c/p\u003e \u003cp\u003eIn conclusion, nicotine-associated skin aging appears to be closely related to the dysregulation of metabolic pathways and stress-response signaling networks. PIM3, FABP3, and MAPK8 were identified as key genes, with PIM3 showing the strongest interaction potential with nicotine. These findings provide mechanistic insights into nicotine-induced skin aging and may offer potential targets for future therapeutic intervention.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study integrates transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), machine learning, molecular docking, and molecular dynamics simulation to investigate the molecular mechanisms underlying nicotine-associated skin aging. A total of 24 potential targets were identified, and three core genes (PIM3, FABP3, and MAPK8) were prioritized. The results suggest that nicotine-associated skin aging is closely related to dysregulation of metabolic homeostasis and stress-response signaling pathways, including the PPAR signaling pathway, adipocytokine signaling pathway, PI3K\u0026ndash;Akt signaling, and MAPK-related processes. Among these, PIM3 showed the strongest binding affinity with nicotine and stable interaction dynamics, suggesting its potential role in nicotine-associated skin aging. These findings provide insights into the molecular mechanisms of nicotine-induced skin aging. Further experimental studies are required to validate these results and to explore their potential applications in skin aging research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDEGs: Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eWGCNA: Weighted gene co-expression network analysis\u003c/p\u003e\n\u003cp\u003ePPI: Protein\u0026ndash;protein interaction\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eSVM-RFE: Support Vector Machine Recursive Feature Elimination\u003c/p\u003e\n\u003cp\u003eLASSO: Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eRF: Random Forest\u003c/p\u003e\n\u003cp\u003ePDB: Protein Data Bank\u003c/p\u003e\n\u003cp\u003eRMSD: Root mean square deviation\u003c/p\u003e\n\u003cp\u003eRMSF: Root mean square fluctuation\u003c/p\u003e\n\u003cp\u003eRg: Radius of gyration\u003c/p\u003e\n\u003cp\u003eSASA: Solvent-accessible surface area\u003c/p\u003e\n\u003cp\u003eMD: Molecular dynamics\u003c/p\u003e\n\u003cp\u003eFEL: Free energy landscape\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed in the current study are publicly available. Gene expression data were obtained from the Gene Expression Omnibus (GEO) under accession number GSE85358 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85358). All other data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKun Wang,\u0026nbsp;Tengfei Wang\u0026nbsp;and\u0026nbsp;Menghui Qin\u0026nbsp;contributed equally to this work. \u0026nbsp;Mengru Pang\u0026nbsp;and Yu Lei performed conceptualization. Mengru Pang and\u0026nbsp;Yu Lei\u0026nbsp;performed investigation.\u0026nbsp;Mengru Pang\u0026nbsp;performed funding acquisition. Mengru Pang\u0026nbsp;and\u0026nbsp;Yu Lei\u0026nbsp;performed project administration.\u0026nbsp;Mengru Pang and Yu Lei performed supervision. Kun Wang , Tengfei Wang\u0026nbsp;and Long Yang performed writing of original draft. Xueying Zhang,\u0026nbsp;Mengru Pang, and Yu Lei, performed writing, reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using publicly available datasets (GEO: GSE85358). All data were obtained from databases with existing ethical approvals or in compliance with relevant guidelines. No experiments involving human participants or animals were performed by the authors. Therefore, ethical approval and informed consent were waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82160377); the National Natural Science Foundation (NSFC) Cultivation Project of Guizhou Medical University Affiliated Hospital(gyfynsfc-2021-28); the\u0026nbsp;Science and Technology Fund Project of Guizhou Provincial Health Commission (gzwjkj-2020-2- 003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaharro-Rodriguez J, Bacci S, Hernandez-Bule ML, Perez-Gonzalez A, Fernandez-Guarino M (2025) Decoding skin aging: a review of mechanisms, markers, and modern therapies. Cosmetics 12:144\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin SH, Lee YH, Rho NK, Park K (2023) Y. Skin aging from mechanisms to interventions: focusing on dermal aging. Front Physiol 14:1195272\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurman D, Auwerx J, Bulteau AL et al (2025) Skin health and biological aging. Nat Aging 5:1195\u0026ndash;1206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrenier A, Morissette MC, Rochette PJ, Pouliot R (2023) The combination of cigarette smoke and solar rays causes effects similar to skin aging in a bilayer skin model. Sci Rep 13:17969\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHergesell K et al (2023) The effect of long-term cigarette smoking on selected skin barrier proteins and lipids. 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Nat Chem Biol 9:271\u0026ndash;276\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"nicotine, skin aging, WGCNA, molecular docking, PIM3","lastPublishedDoi":"10.21203/rs.3.rs-9493613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9493613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmoking is a major extrinsic risk factor for skin aging, and nicotine, the primary bioactive component of tobacco, may play an important role in this process. However, the molecular mechanisms underlying nicotine-associated skin aging remain unclear. In this study, we integrated bioinformatics, transcriptomic analysis, weighted gene co-expression network analysis, machine learning, molecular docking, and molecular dynamics simulations to identify potential targets and pathways involved in nicotine-associated skin aging. Nicotine-related targets were predicted from multiple public databases, and skin aging-related genes were obtained from the GSE85358 dataset. A total of 24 potential targets were identified, among which three core genes, PIM3, FABP3, and MAPK8, were prioritized. Functional enrichment analysis indicated that these genes were mainly involved in kinase signaling, lipid metabolism, and stress-response pathways, including PI3K\u0026ndash;Akt and PPAR signaling. Molecular docking showed that nicotine exhibited the strongest binding affinity with PIM3, and molecular dynamics simulation supported the stability of the PIM3\u0026ndash;nicotine complex. These findings provide evidence suggesting that nicotine-associated skin aging may be related to dysregulation of kinase signaling, lipid metabolism, and stress-response pathways, with PIM3 as a potential key mediator. Further experimental validation is warranted.\u003c/p\u003e","manuscriptTitle":"Integrated bioinformatics and molecular simulation identify PIM3 as a potential mediator of nicotine-associated skin aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 12:28:20","doi":"10.21203/rs.3.rs-9493613/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":"4dd26ffa-3c4f-46b1-8ef6-5aefe9a46bc3","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-08T09:24:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T03:52:11+00:00","index":30,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T05:59:48+00:00","index":29,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T18:16:51+00:00","index":27,"fulltext":""},{"type":"reviewerAgreed","content":"247623096726414206343881381830255463037","date":"2026-04-30T05:13:30+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T09:41:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 12:28:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9493613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9493613","identity":"rs-9493613","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}