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

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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Journal
The International Journal of Biological Markers
Published
2026-05-12
DOI
https://doi.org/10.1177/03936155261443650
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

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

Limeng Qu, Shirong Ding, Wenjun Yi, Qian Long et al.
The International Journal of Biological Markers
Single-cell and spatial transcriptomics
article

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

Limeng Qu, Shirong Ding, Wenjun Yi, Qian Long, Jinyang Li
article en

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.

The International Journal of Biological Markers
Central South University (CN), Hunan Provincial Center for Disease Control and Prevention (CN), Second Xiangya Hospital of Central South University (CN)
Good health and well-being
Openalex Percentile: Top 7%
Single-cell and spatial transcriptomics
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