ArticleDiscover oncology2025
An integrative analysis combining bioinformatics, network pharmacology and experimental methods identified key genes of EGCG targets in Nasopharyngeal Carcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods.Frontiers in oncology · 2025Article
- Network-based insights into miR-30a-5p-mediated regulation and EGCG targeting in triple-negative breast cancer.Frontiers in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundEpigallocatechin gallate (EGCG), a frequently studied catechin in green tea, has been shown to be involved in the antiproliferation and apoptosis of human Nasopharyngeal carcinoma (NPC) cells. However, the pharmacological targets and mechanism by which EGCG can combat NPC patients remain to be studied in detail.
methodsNetwork pharmacology and bioinformatics were employed to investigate the molecular mechanisms underlying EGCG's therapeutic effects on NPC, with an emphasis on developing a prognostic risk model and identifying potential therapeutic targets.
resultsA novel prognostic risk model was developed using univariate Cox regression, LASSO regression and multivariable Cox regression analyses, incorporating six genes to stratify patients into low- and highrisk groups. Kaplan-Meier analysis demonstrated significantly shorter progression-free survival in the high-risk group. The model's accuracy was further validated using time-dependent Receiver Operating Characteristic (ROC) curves. ESTIMATE analysis revealed significantly higher immune, stromal and overall ESTIMATE scores in the low-risk group compared to the high-risk group. Immune profiling indicated significant differences in five immune cell subtypes (memory B cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells and activated dendritic cells) between the two risk groups. Additionally, the low-risk group showed greater sensitivity to conventional chemotherapeutic agents. Immunohistochemistry and molecular docking analyses identified CYCS and MYL12B as promising targets for EGCG treatment.
conclusionThis study utilised network pharmacology and bioinformatics to identify shared genes between EGCG and NPC, aiming to elucidate the molecular mechanisms through which EGCG inhibits NPC and to develop a prognostic model for assessing patient outcomes. The findings provide potential insights for the development of anti-NPC therapies and their clinical applications.
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