Untargeted Metabolomics and Metabolite-Gene Network Analysis Predict NF-κB Inhibition in Artemisian B-Treated Triple-Negative Breast Cancer Cells.

Background

Triple-negative breast cancer (TNBC) remains a formidable clinical challenge due to the scarcity of targeted therapies and profound metabolic heterogeneity. Although Artemisian B, a dimeric sesquiterpene lactone derived from Artemisia argyi, exhibits potent antiproliferative activity, its comprehensive metabolic footprint and the translation of these perturbations into downstream signaling regulation remain poorly characterized.

Methods

To address this gap, we employed an integrative analytical framework combining untargeted metabolomics, topology-guided metabolite-gene network mapping, and parallel experimental validation in MDA-MB-231 cells. This workflow systematically profiled cellular phenotypes, global metabolic reprogramming, and key signaling nodes, enabling the prioritization of high-confidence mechanistic links between metabolic alterations and signal transduction.

Results

Artemisian B dose-dependently suppressed TNBC cell viability (IC50 = 12.12 μM) and triggered mitochondrial apoptosis, characterized by Bax upregulation, Bcl-2 downregulation, and caspase-9/3 activation. Untargeted metabolomics identified 129 significantly altered metabolites, reflecting extensive dysregulation across lipid peroxidation, bioenergetics, and nucleotide metabolism. Topological analysis of the metabolite-gene network identified the NF-κB pathway as a highly interconnected hub within this perturbed landscape. Parallel experimental validation corroborated this prediction, demonstrating that Artemisian B consistently suppressed the phosphorylation of IKKα/β, IκBα, and p65, while markedly attenuating p65 nuclear translocation.

Conclusions

Artemisian B induces TNBC apoptosis through extensive metabolic reprogramming coupled with concurrent inhibition of NF-κB signaling. By seamlessly integrating untargeted metabolomics with network topology, our framework not only successfully bridges metabolic perturbations with signaling outcomes but also establishes a versatile, dual-perspective strategy applicable to both biochemical reaction networks and signal transduction pathways. This approach provides a robust predictive paradigm for decoding the multi-target pharmacological mechanisms of natural products.