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May 15, 2026Open Access

Exploring key biomarkers associated with axillary lymph node metastasis in breast cancer using single-cell RNA sequencing and Mendelian randomization

Key Points

This study aims to identify key biomarkers and cellular mechanisms associated with axillary lymph node metastasis in breast cancer using advanced sequencing and genetic analysis.
Analyzed GSE195861 single-cell RNA sequencing dataset from six invasive ductal carcinoma samples and paired axillary lymph node specimens.
Applied t-distributed Stochastic Neighbor Embedding for clustering and differential expression analysis to identify metastasis-associated genes.
Conducted Mendelian randomization analysis using five methods to link genetic data with breast cancer risk.
Identified 27 cell clusters with ALNM-specific expansions of monocytes, pre-B cells, and CD34+ hematopoietic stem cells.
Found 2421 differentially expressed genes in epithelial cells, with 12 genes showing significant MR associations with breast cancer risk (P < 0.05).
Prioritized CD53 (OR = 1.110, P = 0.017) and TIPARP (OR = 1.153, P = 0.012) as candidate genes linked to increased breast cancer risk.

Abstract

BackgroundAxillary lymph node metastasis (ALNM) serves as a critical prognostic determinant in breast cancer, yet the molecular drivers governing lymphatic dissemination remain poorly characterized. Integrating single-cell transcriptomic profiling with Mendelian-randomization (MR)-based genetic prioritization may help reveal cell type-specific mechanisms underlying metastatic progression.MethodsWe analyzed the GSE195861 single-cell RNA sequencing dataset encompassing six invasive ductal carcinoma (IDC) samples and paired ALNM specimens. t-distributed Stochastic Neighbor Embedding-based clustering and SingleR annotation delineated cellular heterogeneity, while differential expression analysis identified metastasis-associated genes in epithelial compartments. MR analysis employing five robust methods (inverse variance-weighted, weighted median, MR-Egger, simple/weighted mode) integrated genome-wide association study data (GCST90018799) to establish causal gene-breast cancer associations. CellChat reconstructed ligand-receptor networks across nine annotated cell types.ResultsUnsupervised clustering resolved 27 cell clusters into nine lineages, revealing ALNM-specific expansion of monocytes, pre-B cells, and CD34+ hematopoietic stem cells (HSCs). Epithelial cells exhibited 2421 differentially expressed genes (DEGs) between IDC and ALNM, including 12 genes whose genetically predicted expression showed significant associations with breast cancer risk in MR analysis (P < 0.05). CD53 (odds ratio (OR) = 1.110, 95% confidence interval (CI) = 1.019-1.209, P = 0.017) and TCDD-inducible poly-ADP-ribose polymerase (TIPARP) (OR = 1.153, 95% CI = 1.032-1.288, P = 0.012) were prioritized as candidate genes, as their genetically predicted expression was associated with increased breast cancer risk in weighted median MR. Cell-cell communication analysis implicated macrophage-derived midkine-nucleolin signaling and B-cell-orchestrated macrophage migration inhibitory factor-(CD74 + CXCR4) axis in metastatic crosstalk. Functional enrichment linked DEGs to extracellular matrix remodeling and MAPK/PI3K-Akt activation.ConclusionThis multi-omics integration prioritizes CD53 and TIPARP as ALNM-associated candidate genes with genetically supported associations with breast cancer risk, with macrophage-epithelial and B-cell-HSC interactions serving as potential therapeutic targets. Our findings provide a roadmap for developing metastasis-interceptive strategies through precision targeting of the ALNM-associated tumor microenvironment.

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