ArticleCellular oncology (Dordrecht, Netherlands)2025
Single-cell and bulk transcriptome analysis identifies B-cell subpopulations and associated cancer subtypes with distinct clinical and molecular characteristics.
Article in Cellular oncology (Dordrecht, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroudPrevious studies have identified B cell subpopulations with pro- and anti-tumoral activities, while the clinical relevance of B cell subpopulations-specific markers in pan-cancer remains understudied.
methodsWe integrated 14 scRNA-seq datasets (102,504 cells from 424 patients, 15 cancer types) to identify B cell subpopulations via unsupervised clustering. We characterized their functional dynamics and prognostic relevance through analyzing single-cell, bulk and spatial transcriptomic data. Moreover, using B cell subpopulations-specific gene signatures, we constructed models for predicting cancer prognosis and immunotherapy response.
resultsWe identified eight B cell subpopulations (b00-b07) which were classified into naive, plasma, memory, germinal center (GC), and cycling B cells. Trajectory analysis revealed b02-naive and b04-GC cells in early phases, evolving into b01- and b03-plasma/b05- and b06-memory/b07-cycling and b05-memory subpopulations. Anti-tumor responses were activated in early pseudotime, complement/immunoglobulin pathways peaked in mid-pseudotime, and energy metabolism increased in late-pseudotime. The enrichment of b07-cycling and b04-GC was negatively correlated with cancer prognosis, while b02-naive had a positive correlation. Spatial transcriptomic analysis showed clustered b00-b06 versus dispersed b07 cells, with b04-GC and b07-cycling cells distant from tertiary lymphoid structure cores. Based on the expression profiles of 1,047 B cell subpopulations-specific signatures, we identified three pan-cancer subtypes with distinct clinical and molecular characteristics. Using 13 B cell subpopulations-specific signatures, we constructed models to accurately predict cancer survival outcomes and immunotherapy response.
conclusionsOur study delineates eight B cell subpopulations with distinct prognostic relevance. Signature-based stratification and models underscore their clinical relevance in cancer outcomes and therapy response, advancing understanding of B cell heterogeneity in cancer.
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