Frontiers in bioscience (Landmark edition) · 2025
Eugenol as a Novel Neuroblastoma Therapeutic Identified by an Online Prognostic Assessment Platform.
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Wang Ruizong, Long Chunxi
Аннотация
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 < 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.
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 < 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.