Fang Jiale, Yu Siyuan, Wang Wei, Liu Cheng, Lv Xiaojia, Jin Jiaqi, Han Xiaomin, Zhou Fang, Wang Yukun
Department of Pharmacology, Southern University of Science and Technology, Shenzhen, China.
Department of Pharmacology, Air Force Medical University, Xi'an, China.
Front Immunol. 2025 Mar 24;16:1524120. doi: 10.3389/fimmu.2025.1524120. eCollection 2025.
Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly and systematically delineated the characteristics of TILBs in LUAD.
The research employed single-cell RNA sequencing from the GSE117570 dataset to identify markers linked to TILBs. A comprehensive machine learning approach, utilizing ten distinct algorithms, facilitated the creation of a TILB-related index (BRI) across the TCGA, GSE31210, and GSE72094 datasets. We used multiple algorithms to evaluate the relationships between BRI and TIME, as well as immune therapy-related biomarkers. Additionally, we assessed the role of BRI in predicting immune therapy response in two datasets, GSE91061 and GSE126044.
BRI functioned as an independent risk determinant in LUAD, demonstrating a robust and reliable capacity to predict overall survival rates. We observed significant differences in the scores of B cells, M2 macrophages, NK cells, and regulatory T cells between the high and low BRI score groups. Notably, BRI was found to inversely correlate with cytotoxic CD8+ T-cell infiltration (r = -0.43, p < 0.001) and positively correlate with regulatory T cells (r = 0.31, p = 0.008). We also found that patients with lower BRI were more likely to respond to immunotherapy and were associated with reduced IC50 values for standard chemotherapy and targeted therapy drugs, in contrast to higher BRI. Additionally, the BRI-based survival prediction nomogram demonstrated significant promise for clinical application in predicting the 1-, 3-, and 5-year overall survival rates among LUAD patients.
Our study developed a BRI model using ten different algorithms and 101 algorithm combinations. The BRI could be a valuable tool for risk stratification, prognosis, and selection of treatment approaches.
肿瘤浸润性B淋巴细胞（TILBs）在塑造肿瘤免疫微环境（TIME）和肺腺癌（LUAD）进展中起关键作用。然而，目前仍缺乏全面系统地描述LUAD中TILBs特征的研究。
本研究采用来自GSE117570数据集的单细胞RNA测序来鉴定与TILBs相关的标志物。一种综合的机器学习方法，利用十种不同算法，在TCGA、GSE31210和GSE72094数据集中构建了一个与TILB相关的指数（BRI）。我们使用多种算法评估BRI与TIME以及免疫治疗相关生物标志物之间的关系。此外，我们在GSE91061和GSE126044两个数据集中评估了BRI在预测免疫治疗反应中的作用。
BRI在LUAD中作为一个独立的风险决定因素发挥作用，显示出强大且可靠的预测总生存率的能力。我们观察到高BRI评分组和低BRI评分组在B细胞、M2巨噬细胞、NK细胞和调节性T细胞的评分上存在显著差异。值得注意的是，发现BRI与细胞毒性CD8 + T细胞浸润呈负相关（r = -0.43，p < 0.001），与调节性T细胞呈正相关（r = 0.31，p = 0.008）。我们还发现，与高BRI患者相比，低BRI患者更可能对免疫治疗有反应，并且与标准化疗和靶向治疗药物的IC50值降低有关。此外，基于BRI的生存预测列线图在预测LUAD患者1年、3年和5年总生存率方面显示出显著的临床应用前景。
我们的研究使用十种不同算法和101种算法组合开发了一个BRI模型。BRI可能是用于风险分层、预后评估和治疗方法选择的有价值工具。
