Colorectal cancer (CRC) remains a major global health challenge due to high incidence, poor prognosis, and limited therapeutic options. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a promising anti-tumor strategy, yet pharmacological inducers remain underexplored. Hederacolchiside A1 (HeA1), a natural bioactive compound, was investigated for its potential anti-CRC effects and underlying mechanisms. In vitro, HeA1 inhibited proliferation, migration, and invasion of HT-29, SW-480, HCT-116, and MC-38 cells in a dose-dependent manner, induced apoptosis, and modulated epithelial-mesenchymal transition (EMT) markers, increasing E-cadherin and decreasing Vimentin, Slug, MMP2, and MMP9. Ferroptosis induction was evidenced by increased reactive oxygen species (ROS), lipid peroxidation, and intracellular Fe2+, along with a decrease in reduced glutathione (GSH) and a concomitant increase in oxidized glutathione (GSSG). HeA1 directly targeted endoplasmic reticulum oxidoreductase 1 alpha (ERO1A), resulting in suppression of PI3K/AKT/mTOR signaling and downregulation of ferroptosis defense proteins SLC7A11 and GPX4. In HT-29 xenograft models, HeA1 significantly reduced tumor volume and weight, decreased Ki67 and CD31 expression, increased TUNEL-positive cells, and exhibited minimal systemic toxicity. Immunofluorescence and Western blot analyses confirmed inhibition of PI3K/AKT/mTOR pathway components and ferroptosis-related proteins in vivo. Collectively, these findings demonstrate that HeA1 exerts potent anti-CRC effects by directly targeting ERO1A to induce ferroptosis through inhibition of the PI3K/AKT/mTOR-SLC7A11/GPX4 axis. This study provides a mechanistic rationale for further development of HeA1 as a potential therapeutic agent for colorectal cancer.
Peipei Li, Linguo Cao, Tao Bi et al.· Cellular Signalling· 0 citations
Opioid use disorder (OUD) remains a major public health challenge, and the human µ-opioid receptor (µOR) is a central target in opioid pharmacology. Here, we report a reproducible ligand-based workflow for µOR antagonist classification named µORScreen which integrates rigorous split design, systematic model benchmarking, interpretation, virtual screening and lightweight local deployment. A curated set of 982 human µOR ligands was partitioned under three complementary strategies (similarity-based, scaffold-based, and random-based), each with a held-out test set and five train/validation folds. On the test evaluation, LightGBM (ECFP4 with RDKit 2D descriptors) generalized best (AUROC 0.714), closely followed by TabPFN (0.705) and Random Forest (0.696). The three top-ranked models were combined into a consensus classifier that prioritized unanimously predicted compounds as high-confidence antagonist-like candidates. Applied to GPCRdb, ZINC, REINVENT, and OUROBOROS, µORScreen revealed pronounced source dependence, with the strongest enrichment of antagonist-like candidates in GPCRdb. On an independent set of 17 non-overlapping, literature-derived ligands (10 antagonists, 7 non-antagonists), the consensus achieved a balanced accuracy of 0.68. SHAP analysis attributed predictions to a concentrated subset of fingerprint features, and the workflow was deployed as a web server supporting SMILES-based prediction and RF-based SHAP analysis. µORScreen thus provides a computationally efficient, openly accessible framework for early-stage µOR ligand prioritization and external-library triage.
Keyu Chen, Juan Huang, Jiangcheng Xu et al.· RSC Advances· 0 citations