{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T15:11:47Z","timestamp":1767885107633,"version":"3.49.0"},"reference-count":41,"publisher":"IMR Press","issue":"12","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Children\u2019s Hospital of Chongqing Medical University Nursing Department-Level Research Project","award":["CHCQMU2024.11"],"award-info":[{"award-number":["CHCQMU2024.11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Front. Biosci. (Landmark Ed)"],"abstract":"<jats:p>Background: High-risk neuroblastoma (NB) remains a therapeutic challenge with a poor prognosis, necessitating the identification of novel prognostic biomarkers and effective therapeutics. Methods: We analyzed transcriptomic datasets from public repositories using weighted gene co-expression network analysis to identify gene modules associated with prognosis. Consensus clustering stratified patients into distinct subgroups. Differential expression analysis, integrated with 80 machine learning algorithm combinations, prioritized hub genes to construct a risk-scoring model, which was implemented via an online platform. We then screened the new drug eugenol using the proximity algorithm and explored its feasibility through in vitro trials. Results: Consensus clustering based on prognosis-associated modules revealed two patient subgroups with significantly different survival differences (p &lt; 0.001). A set of six hub genes (chromosome 21 open reading frame 58 (C21orf58), cannabinoid receptor 1 (CNR1), laminin alpha 4 (LAMA4), leptin receptor (LEPR), solute carrier family 25 member 10 (SLC25A10), and telomerase reverse transcriptase (TERT)) was identified and used to build a risk-scoring model, accessible at online platform. Leveraging this model, we identified eugenol as a potential therapeutic candidate. Transcriptome analysis showed that eugenol selectively targeted high-risk NB cell populations predicted by the model. Preliminary in vitro experiments investigating its mechanism of action indicated that eugenol primarily functions by inhibiting the MYC-cyclin D1 (CCND1) axis. Conclusions: This study establishes an integrated precision medicine framework for NB, identifying a new high-risk group and suggesting eugenol as a promising therapeutic candidate for this group, potentially acting through MYC-CCND1 axis suppression.<\/jats:p>","DOI":"10.31083\/fbl46406","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:51:47Z","timestamp":1767181907000},"source":"Crossref","is-referenced-by-count":0,"title":["Eugenol as a Novel Neuroblastoma Therapeutic Identified by an Online Prognostic Assessment Platform"],"prefix":"10.31083","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9019-0301","authenticated-orcid":true,"given":"Ruizong","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Neurosurgery Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Municipal Health Commission Key Laboratory of Children\u2019s Vital Organ Development and Diseases, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Intelligent Application of Big Data in Pediatrics Engineering Research Center of Chongqing Education Commission of China, 401122 Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4193-7766","authenticated-orcid":true,"given":"Chunxi","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Children\u2019s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents\u2019 Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Municipal Health Commission Key Laboratory of Children\u2019s Vital Organ Development and Diseases, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Intelligent Application of Big Data in Pediatrics Engineering Research Center of Chongqing Education Commission of China, 401122 Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16242","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Wienke J, Dierselhuis MP, Tytgat GAM, K\u00fcnkele A, Nierkens S, Molenaar JJ. The immune landscape of neuroblastoma: Challenges and opportunities for novel therapeutic strategies in pediatric oncology. European Journal of Cancer (Oxford, England: 1990). 2021; 144: 123\u2013150.","DOI":"10.1016\/j.ejca.2020.11.014"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"Irwin MS, Naranjo A, Zhang FF, Cohn SL, London WB, Gastier-Foster JM, et al. Revised Neuroblastoma Risk Classification System: A Report From the Children\u2019s Oncology Group. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology. 2021; 39: 3229\u20133241.","DOI":"10.1200\/JCO.21.00278"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"Rozenblatt-Rosen O, Regev A, Oberdoerffer P, Nawy T, Hupalowska A, Rood JE, et al. The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell. 2020; 181: 236\u2013249.","DOI":"10.1016\/j.cell.2020.03.053"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"Xu J, Liao K, Yang X, Wu C, Wu W. Using single-cell sequencing technology to detect circulating tumor cells in solid tumors. Molecular Cancer. 2021; 20: 104.","DOI":"10.1186\/s12943-021-01392-w"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"Bidwell SS, Peterson CC, Demanelis K, Zarins KR, Meza R, Sriplung H, et al. Childhood cancer incidence and survival in Thailand: A comprehensive population-based registry analysis, 1990-2011. Pediatric Blood & Cancer. 2019; 66: e27428.","DOI":"10.1002\/pbc.27428"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"Lundberg KI, Treis D, Johnsen JI. Neuroblastoma Heterogeneity, Plasticity, and Emerging Therapies. Current Oncology Reports. 2022; 24: 1053\u20131062.","DOI":"10.1007\/s11912-022-01270-8"},{"key":"ref7","doi-asserted-by":"publisher","unstructured":"Mlakar V, Morel E, Mlakar SJ, Ansari M, Gumy-Pause F. A review of the biological and clinical implications of RAS-MAPK pathway alterations in neuroblastoma. Journal of Experimental & Clinical Cancer Research: CR. 2021; 40: 189.","DOI":"10.1186\/s13046-021-01967-x"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"Zafar A, Wang W, Liu G, Xian W, McKeon F, Zhou J, et al. Targeting the p53-MDM2 pathway for neuroblastoma therapy: Rays of hope. Cancer Letters. 2021; 496: 16\u201329.","DOI":"10.1016\/j.canlet.2020.09.023"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"Gladden AB, Diehl JA. Cell cycle progression without cyclin E\/CDK2: breaking down the walls of dogma. Cancer Cell. 2003; 4: 160\u2013162.","DOI":"10.1016\/s1535-6108(03)00217-4"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"Xu XL, Deng SL, Lian ZX, Yu K. Estrogen Receptors in Polycystic Ovary Syndrome. Cells. 2021; 10: 459.","DOI":"10.3390\/cells10020459"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9: 559.","DOI":"10.1186\/1471-2105-9-559"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"Li Y, Hu Y, Jiang F, Chen H, Xue Y, Yu Y. Combining WGCNA and machine learning to identify mechanisms and biomarkers of ischemic heart failure development after acute myocardial infarction. Heliyon. 2024; 10: e27165.","DOI":"10.1016\/j.heliyon.2024.e27165"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"Pan R, Shi H, Shen Y, Wang X, Zhao S, Zhang N, et al. Development and validation of a postoperative prognostic model for hormone receptor positive early stage breast cancer recurrence. Scientific Reports. 2025; 15: 9905.","DOI":"10.1038\/s41598-025-92872-2"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"Lamichhane B, Zhou J, Sano A. Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model. IEEE Journal of Biomedical and Health Informatics. 2023; 27: 3246\u20133257.","DOI":"10.1109\/JBHI.2023.3265684"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine. 1997; 16: 385\u2013395. <385::aid-sim380>3.0.co;2-3.","DOI":"10.1002\/(sici)1097-0258(19970228)16:4"},{"key":"ref16","doi-asserted-by":"publisher","unstructured":"Shi J, Liu C, Zheng X, Chen Y, Zhang H, Liu T, et al. Novel metabolic prognostic score for predicting survival in patients with cancer. Scientific Reports. 2025; 15: 1322.","DOI":"10.1038\/s41598-025-85287-6"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"Henke DM, Renwick A, Zoeller JR, Meena JK, Neill NJ, Bowling EA, et al. Bio-primed machine learning to enhance discovery of relevant biomarkers. NPJ Precision Oncology. 2025; 9: 39.","DOI":"10.1038\/s41698-025-00825-9"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science (New York, N.Y.). 2015; 347: 1257601.","DOI":"10.1126\/science.1257601"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"Guney E, Menche J, Vidal M, Bar\u00e1basi AL. Network-based in silico drug efficacy screening. 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Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models. The Journal of Clinical Investigation. 2025; 135: e176655.","DOI":"10.1172\/JCI176655"},{"key":"ref23","doi-asserted-by":"publisher","unstructured":"Abdullah ML, Al-Shabanah O, Hassan ZK, Hafez MM. Eugenol-Induced Autophagy and Apoptosis in Breast Cancer Cells via PI3K\/AKT\/FOXO3a Pathway Inhibition. International Journal of Molecular Sciences. 2021; 22: 9243.","DOI":"10.3390\/ijms22179243"},{"key":"ref24","doi-asserted-by":"publisher","unstructured":"Al-Harbi S, Alkholiwy EMA, Ali Ahmed SO, Aljurf M, Al-Hejailan R, Aboussekhra A. Eugenol Promotes Apoptosis in Leukemia Cells via Targeting the Mitochondrial Biogenesis PPRC1 Gene. Cells. 2025; 14: 260.","DOI":"10.3390\/cells14040260"},{"key":"ref25","doi-asserted-by":"publisher","unstructured":"Xiao JF, Sun QY, Ding LW, Chien W, Liu XY, Mayakonda A, et al. The c-MYC-BMI1 axis is essential for SETDB1-mediated breast tumourigenesis. The Journal of Pathology. 2018; 246: 89\u2013102.","DOI":"10.1002\/path.5126"},{"key":"ref26","doi-asserted-by":"publisher","unstructured":"Luo C, Zhu Y, Zhou J, Sun X, Zhang S, Tan S, et al. Increased CYR61 expression activates CCND1\/c-Myc pathway to promote nasal epithelial cells proliferation in chronic rhinosinusitis with nasal polyps. Clinical Immunology (Orlando, Fla.). 2023; 247: 109235.","DOI":"10.1016\/j.clim.2023.109235"},{"key":"ref27","doi-asserted-by":"publisher","unstructured":"Yogev O, Almeida GS, Barker KT, George SL, Kwok C, Campbell J, et al. In Vivo Modeling of Chemoresistant Neuroblastoma Provides New Insights into Chemorefractory Disease and Metastasis. Cancer Research. 2019; 79: 5382\u20135393.","DOI":"10.1158\/0008-5472.CAN-18-2759"},{"key":"ref28","doi-asserted-by":"publisher","unstructured":"Braal CL, Jongbloed EM, Wilting SM, Mathijssen RHJ, Koolen SLW, Jager A. Inhibiting CDK4\/6 in Breast Cancer with Palbociclib, Ribociclib, and Abemaciclib: Similarities and Differences. Drugs. 2021; 81: 317\u2013331.","DOI":"10.1007\/s40265-020-01461-2"},{"key":"ref29","doi-asserted-by":"publisher","unstructured":"Vijayaraghavan S, Karakas C, Doostan I, Chen X, Bui T, Yi M, et al. CDK4\/6 and autophagy inhibitors synergistically induce senescence in Rb positive cytoplasmic cyclin E negative cancers. Nature Communications. 2017; 8: 15916.","DOI":"10.1038\/ncomms15916"},{"key":"ref30","doi-asserted-by":"publisher","unstructured":"Rekha P, Gupta A, Goud KS, Biswas B, Bhattar S, Vijayakumar G, et al. GPER induces mitochondrial fission through p44\/42 MAPK - Drp1 pathway in breast cancer cells. Biochemical and Biophysical Research Communications. 2023; 643: 16\u201323.","DOI":"10.1016\/j.bbrc.2022.12.061"},{"key":"ref31","doi-asserted-by":"publisher","unstructured":"Huang L, Liu D, Wang N, Ling S, Tang Y, Wu J, et al. Integrated genomic analysis identifies deregulated JAK\/STAT-MYC-biosynthesis axis in aggressive NK-cell leukemia. Cell Research. 2018; 28: 172\u2013186.","DOI":"10.1038\/cr.2017.146"},{"key":"ref32","doi-asserted-by":"publisher","unstructured":"Xue H, Hu Z, Liu S, Zhang S, Yang W, Li J, et al. The mechanism of NF-\u03baB-TERT feedback regulation of granulosa cell apoptosis in PCOS rats. PloS One. 2024; 19: e0312115.","DOI":"10.1371\/journal.pone.0312115"},{"key":"ref33","doi-asserted-by":"publisher","unstructured":"Yan MM, Wu SS, Qi YP, Li ZR, Zhang Q, Zhao H, et al. Association between cyclin-dependent kinase 4\/6 inhibitors and venous thromboembolism: analysis of F.A.E.R.S. data. Expert Opinion on Drug Safety. 2022; 21: 277\u2013283.","DOI":"10.1080\/14740338.2021.1981856"},{"key":"ref34","doi-asserted-by":"publisher","unstructured":"O\u2019Leary B, Finn RS, Turner NC. Treating cancer with selective CDK4\/6 inhibitors. Nature Reviews. Clinical Oncology. 2016; 13: 417\u2013430.","DOI":"10.1038\/nrclinonc.2016.26"},{"key":"ref35","doi-asserted-by":"publisher","unstructured":"Poei D, Ali S, Ye S, Hsu R. ALK inhibitors in cancer: mechanisms of resistance and therapeutic management strategies. Cancer Drug Resistance (Alhambra, Calif.). 2024; 7: 20.","DOI":"10.20517\/cdr.2024.25"},{"key":"ref36","doi-asserted-by":"publisher","unstructured":"Peng S, Zou L, Liu W, Gan L, Liu W, Liang R, et al. Storage stability and antibacterial activity of eugenol nanoliposomes prepared by an ethanol injection-dynamic high-pressure microfluidization method. Journal of Food Protection. 2015; 78: 22\u201330.","DOI":"10.4315\/0362-028X.JFP-14-246"},{"key":"ref37","doi-asserted-by":"publisher","unstructured":"Harb AA, Bustanji YK, Almasri IM, Abdalla SS. Eugenol Reduces LDL Cholesterol and Hepatic Steatosis in Hypercholesterolemic Rats by Modulating TRPV1 Receptor. Scientific Reports. 2019; 9: 14003.","DOI":"10.1038\/s41598-019-50352-4"},{"key":"ref38","doi-asserted-by":"publisher","unstructured":"Monterrubio C, Paco S, Olaciregui NG, Pascual-Pasto G, Vila-Ubach M, Cuadrado-Vilanova M, et al. Targeted drug distribution in tumor extracellular fluid of GD2-expressing neuroblastoma patient-derived xenografts using SN-38-loaded nanoparticles conjugated to the monoclonal antibody 3F8. Journal of Controlled Release: Official Journal of the Controlled Release Society. 2017; 255: 108\u2013119.","DOI":"10.1016\/j.jconrel.2017.04.016"},{"key":"ref39","doi-asserted-by":"publisher","unstructured":"van Ewijk R, Cleirec M, Herold N, le Deley MC, van Eijkelenburg N, Boudou-Rouquette P, et al. A systematic review of recent phase-II trials in refractory or recurrent osteosarcoma: Can we inform future trial design? Cancer Treatment Reviews. 2023; 120: 102625.","DOI":"10.1016\/j.ctrv.2023.102625"},{"key":"ref40","doi-asserted-by":"publisher","unstructured":"Fukuda A, Tatsuya Y. Treatment of advanced ALK-rearranged NSCLC following second-generation ALK-TKI failure. Expert Review of Anticancer Therapy. 2023; 23: 1157\u20131167.","DOI":"10.1080\/14737140.2023.2265566"},{"key":"ref41","doi-asserted-by":"publisher","unstructured":"Tan AC, Pavlakis N. Anti-Angiogenic Therapy in ALK Rearranged Non-Small Cell Lung Cancer (NSCLC). International Journal of Molecular Sciences. 2022; 23: 8863.","DOI":"10.3390\/ijms23168863"}],"container-title":["Frontiers in Bioscience-Landmark"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.imrpress.com\/journal\/FBL\/30\/12\/10.31083\/FBL46406","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T11:36:49Z","timestamp":1767872209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.imrpress.com\/journal\/FBL\/30\/12\/10.31083\/FBL46406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,22]]},"references-count":41,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025]]}},"alternative-id":["S2768-6701(25)01252-0"],"URL":"https:\/\/doi.org\/10.31083\/fbl46406","relation":{},"ISSN":["2768-6701","2768-6698"],"issn-type":[{"value":"2768-6701","type":"print"},{"value":"2768-6698","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,22]]},"article-number":"46406"}}