He Yin, Zhao Li, Zheng Yufen, Wang Xiaosheng
Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Intelligent Pharmacy Interdisciplinary Research Center, China Pharmaceutical University, Nanjing, 211198, China.
Cell Oncol (Dordr). 2025 Jun 17. doi: 10.1007/s13402-025-01082-5.
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.
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.
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.
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.
先前的研究已经确定了具有促肿瘤和抗肿瘤活性的B细胞亚群，而B细胞亚群特异性标志物在泛癌中的临床相关性仍未得到充分研究。
我们整合了14个单细胞RNA测序数据集（来自424例患者、15种癌症类型的102,504个细胞），通过无监督聚类来识别B细胞亚群。我们通过分析单细胞、批量和空间转录组数据来表征它们的功能动态和预后相关性。此外，利用B细胞亚群特异性基因特征，我们构建了预测癌症预后和免疫治疗反应的模型。
我们识别出八个B细胞亚群（b00 - b07），它们被分类为幼稚B细胞、浆细胞、记忆B细胞、生发中心（GC）B细胞和循环B细胞。轨迹分析显示，早期阶段有b02 - 幼稚B细胞和b04 - GC细胞，它们会演变为b01 - 和b03 - 浆细胞/b05 - 和b06 - 记忆B细胞/b07 - 循环B细胞以及b05 - 记忆B细胞亚群。抗肿瘤反应在早期伪时间被激活，补体/免疫球蛋白途径在中期伪时间达到峰值，能量代谢在晚期伪时间增加。b07 - 循环B细胞和b04 - GC细胞的富集与癌症预后呈负相关，而b02 - 幼稚B细胞呈正相关。空间转录组分析显示，b00 - b06细胞聚集，而b07细胞分散，b04 - GC细胞和b07 - 循环B细胞远离三级淋巴结构核心。基于1047个B细胞亚群特异性特征的表达谱，我们识别出三种具有不同临床和分子特征的泛癌亚型。利用13个B细胞亚群特异性特征，我们构建了模型来准确预测癌症生存结果和免疫治疗反应。
我们的研究描绘了八个具有不同预后相关性的B细胞亚群。基于特征的分层和模型强调了它们在癌症结局和治疗反应中的临床相关性，推进了对癌症中B细胞异质性的理解。
