Neoplasia (New York, N.Y.) · 2026
Integrating single-cell and spatial transcriptomics to dissect mast-cell heterogeneity and arginine-metabolism-associated markers in BRCA.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Gao Mengli, Ran Yuge, Qi Juan, Han Xiao, Wei Yali, Wang Kunjie, Wu Xiaoxi, Sun Chengcheng, Li Yanhong, Wang Wenyan, Xie Wenjie, Zhang Peng, Liu Kuan, Shi Hongyun
Аннотация
BACKGROUND: Mast cells (MCs) are immunometabolic sentinels, yet their heterogeneity and functional specialization in breast cancer (BRCA) remain unclear. We hypothesized that arginine metabolism defines transcriptionally and functionally distinct MC subpopulations that shape the BRCA microenvironment.
METHODS: We integrated single-cell RNA-seq (GSE161529; 272,592 cells, 38 clusters), spatial transcriptomics (GSE243022) and bulk RNA-seq (TCGA, GSE42568). After harmony batch-correction and Seurat-Louvian clustering, MCs were split by median arginine score (AUCell/UCell/AddModuleScore/singscore) into high- (HAS) and low-activity (LAS) subsets. Monocle2 pseudotime, CellChat, hdWGCNA (power = 15), LASSO-Cox and MiloR were used to trace differentiation, communication, prognostic value and triple-negative breast cancer (TNBC) enrichment. Functional validation of the model-prioritized gene OAT was subsequently conducted in clinical tissues and breast cancer cell lines through loss-of-function assays.
RESULTS: HAS cells represented 18.7 % of all MCs and were enriched in TNBC (OR = 2.4, p < 0.001). They displayed higher differentiation potential (CytoTRACE: 0.72 vs 0.41, p < 0.001) and trajectory progression (pseudotime τ = 0.68). Arginine score correlated with differentiation (r = 0.52) and tumor risk signature (TRS, r = 0.35). CellChat revealed 1.8-fold increased incoming signals in HAS; VEGF and TGF-β pathways were most active (p < 0.001). hdWGCNA identified 19 modules; cyan and green modules (kME > 0.9) contained 214 HAS-up genes driving cell-cycle and arginine/glutamine metabolism. A five-gene (ARG1, NOS2, ASL, OAT, AZIN1) LASSO model predicted 5-year survival (AUC = 0.82; HR = 1.68, p < 0.001). Spatial maps confirmed ASL+ MC hotspots in tumor cores (AUC = 0.89 vs normal). Experimentally, OAT expression was elevated in TNBC tissues and cell lines. Knockdown of OAT impaired proliferation, induced apoptosis, suppressed migration/invasion, and modulated apoptosis- and EMT-related protein expression, functionally supporting its role in BRCA progression.
CONCLUSION: Arginine metabolism stratifies MCs into pro-tumorigenic HAS and quiescent LAS subsets; ASL-high MCs constitute a metabolically wired, highly communicating population that fuels TNBC progression and furnishes an exploitable prognostic signature. OAT, a key HAS-associated gene, promotes breast cancer aggressiveness through proliferation, survival, and invasion.
METHODS: We integrated single-cell RNA-seq (GSE161529; 272,592 cells, 38 clusters), spatial transcriptomics (GSE243022) and bulk RNA-seq (TCGA, GSE42568). After harmony batch-correction and Seurat-Louvian clustering, MCs were split by median arginine score (AUCell/UCell/AddModuleScore/singscore) into high- (HAS) and low-activity (LAS) subsets. Monocle2 pseudotime, CellChat, hdWGCNA (power = 15), LASSO-Cox and MiloR were used to trace differentiation, communication, prognostic value and triple-negative breast cancer (TNBC) enrichment. Functional validation of the model-prioritized gene OAT was subsequently conducted in clinical tissues and breast cancer cell lines through loss-of-function assays.
RESULTS: HAS cells represented 18.7 % of all MCs and were enriched in TNBC (OR = 2.4, p < 0.001). They displayed higher differentiation potential (CytoTRACE: 0.72 vs 0.41, p < 0.001) and trajectory progression (pseudotime τ = 0.68). Arginine score correlated with differentiation (r = 0.52) and tumor risk signature (TRS, r = 0.35). CellChat revealed 1.8-fold increased incoming signals in HAS; VEGF and TGF-β pathways were most active (p < 0.001). hdWGCNA identified 19 modules; cyan and green modules (kME > 0.9) contained 214 HAS-up genes driving cell-cycle and arginine/glutamine metabolism. A five-gene (ARG1, NOS2, ASL, OAT, AZIN1) LASSO model predicted 5-year survival (AUC = 0.82; HR = 1.68, p < 0.001). Spatial maps confirmed ASL+ MC hotspots in tumor cores (AUC = 0.89 vs normal). Experimentally, OAT expression was elevated in TNBC tissues and cell lines. Knockdown of OAT impaired proliferation, induced apoptosis, suppressed migration/invasion, and modulated apoptosis- and EMT-related protein expression, functionally supporting its role in BRCA progression.
CONCLUSION: Arginine metabolism stratifies MCs into pro-tumorigenic HAS and quiescent LAS subsets; ASL-high MCs constitute a metabolically wired, highly communicating population that fuels TNBC progression and furnishes an exploitable prognostic signature. OAT, a key HAS-associated gene, promotes breast cancer aggressiveness through proliferation, survival, and invasion.