胰腺导管腺癌 (PDAC) 是癌症死亡的主要原因，其发病机制复杂，涉及多种免疫细胞，包括 B 细胞及其亚群。尽管关于这些细胞在肿瘤微环境 (TME) 中的作用的研究不断涌现，但与肿瘤浸润免疫细胞 (TIIC) 的详细分子相互作用尚未完全了解。我们应用 CIBERSORT 来量化 TIIC 和初始 B 细胞，这些细胞可预测肿瘤的预后PDAC。整合来自 scRNA-seq 的标记基因和来自加权基因共表达网络分析 (WGCNA) 的模块基因，以识别初始 B 细胞相关基因。利用十种机器学习算法构建了预后特征，并在外部队列中进行了验证。我们进一步评估了按风险分层的患者组之间的免疫细胞多样性、估计评分和免疫检查点基因 (ICG)，以阐明 PDAC 中的免疫状况。我们的分析在单细胞和批量转录组中鉴定了 994 个初始 B 细胞相关基因，其中 247 与总体生存率相关。我们使用 Lasso 和 plsRcox 算法开发了一个 12 基因预后特征，该特征通过 10 倍交叉验证得到证实，并在训练和现实队列中显示出强大的预测能力。值得注意的是，我们观察到高风险和低风险患者之间的免疫浸润存在显着差异。我们的研究提出了强有力的预后特征，有效地绘制了 PDAC 中复杂的免疫相互作用，强调了初始 B 细胞的关键功能，并提出了免疫治疗干预的新途径。该特征在个性化 PDAC 治疗、增强对免疫动力学的理解和指导免疫治疗策略方面具有潜在的临床应用。版权所有 © 2024。由 Elsevier B.V. 出版。

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood.We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC.Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk.Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.Copyright © 2024. Published by Elsevier B.V.