Faculty, Staff and Student Publications
Language
English
Publication Date
10-1-2025
Journal
Cell Oncology
DOI
10.1007/s13402-025-01082-5
PMID
40526246
PMCID
PMC12528239
PubMedCentral® Posted Date
6-17-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Backgroud: Previous 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.
Methods: We 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.
Results: We 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.
Conclusions: Our 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.
Keywords
Humans, Gene Expression Profiling, Neoplasms, Single-Cell Analysis, Prognosis, Transcriptome, B-Lymphocyte Subsets, Gene Expression Regulation, Neoplastic, B-Lymphocytes, Cluster Analysis
Published Open-Access
yes
Recommended Citation
, "Single-Cell and Bulk Transcriptome Analysis Identifies B-cell Subpopulations and Associated Cancer Subtypes With Distinct Clinical and Molecular Characteristics" (2025). Faculty, Staff and Student Publications. 1017.
https://digitalcommons.library.tmc.edu/uthshis_docs/1017