Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies.

Background: Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach. Methods: We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions. Results: Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin. Conclusion: This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00390-4.