CELL INTELLIGENCE · 肿瘤细胞治疗研究

Breast cancer remains the leading cause of cancer morbidity and mortality worldwide, with limited efficacy in advanced breast cancer patients due to tumor heterogeneity and drug resistance. Emerging evidence suggests that the gut microbiota is associated with breast cancer progression through metabolites and immunomodulatory effects.

However, mechanistic insights and therapeutic targets combining the gut microbiota and host genetics remain underexplored. In this study, we performed Mendelian randomization (MR) analysis via cis-eQTLs from breast cancer GWASs and druggable gene datasets to identify genes causally associated with breast cancer risk. Differential gene expression analysis was conducted using TCGA data. Gut microbiota-target-metabolite networks were constructed from the gutMGene database, and immunophenotyping was assessed via CIBERSORT. Molecular docking and molecular dynamics simulations were used to evaluate the interactions between gut microbiota metabolites and key targets. Magnetic resonance and transcriptome analyses revealed 14 candidate genes associated with breast cancer risk, of which CXCL10 was positively associated with disease progression (OR = 1.

124, P = 0. 007). CXCL10 expression was strongly correlated with the infiltration of CD4 + memory-activated T cells, CD4 + follicular helper T cells, CD8 + T cells, and gamma delta T cells. Network analysis revealed that Enterococcus faecalis was associated with the activation of CXCL10.

Stable molecular dynamics simulations indicated that Lariciresinol was a high-affinity ligand for CXCL10. This comprehensive study highlights the role of the gut microbiota-metabolite-gene axis in breast cancer progression, particularly in Enterococcus faecalis-mediated CXCL10 activation. By modulating CXCL10, Lariciresinol has emerged as a promising candidate for targeted therapy.

These findings suggest that the gut microbiota-metabolite-gene axis may play a regulatory role in breast cancer progression and propose Lariciresinol as a potential therapeutic candidate.

However, given the computational foundation and in vitro validation, this study should be considered hypothesis-generating, and further in vivo and clinical investigations are warranted to confirm the proposed mechanisms.