ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1663517
This article is part of the Research TopicBioinformatics and Systems Biology Strategies in Disease Management with a Special Emphasis on Cancer, Alzheimer's Disease and AgingView all 8 articles
Elucidating the multiscale mechanisms and therapeutic targets of caffeic acid in gastric cancer: A synergy of computational and experimental approaches
Provisionally accepted
- Anhui University of Chinese Medicine, Hefei, China
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Gastric cancer is a malignant tumor with high incidence and mortality rates worldwide, and effective therapeutic strategies targeting its complex pathological processes are limited. Caffeic acid is a phenolic compound derived from natural plants and has attracted attention for its potential anticancer properties; however, its mechanism of action in gastric cancer has not been fully elucidated. In this study, a multimodal computational framework integrating multiomics, machine learning, and molecular dynamics simulations, combined with in vitro experiments, was used to systematically investigate the molecular mechanism of caffeic acid against gastric cancer. Among the predicted targets, FZD2—a major receptor that mediates noncanonical WNT/Ca²⁺ signaling—was identified as a core regulatory hub associated with tumor progression and metastasis. Molecular dynamics simulations further revealed a stable binding interaction between caffeic acid and FZD2. An in vitro EMT model was established by treating human gastric cancer cells with TGF-β1. The results showed that caffeic acid intervention inhibited cell migration, invasion, and EMT progression while reducing FZD2 protein expression. This study confirmed that caffeic acid regulates FZD2 expression and inhibits the activation of the noncanonical Wnt5a/Ca²⁺/NFAT signaling pathway, thereby interfering with gastric cancer–related pathological processes. These findings reveal the molecular mechanism of caffeic acid in gastric cancer and reflect the value of natural products in cancer research.
Keywords: Caffeic acid, gastric cancer, machine learning, molecular dynamics simulations, experimental verification
Received: 10 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Zhang, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ya Li, ly20220307@163.com
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