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Identifying Distinct Molecular Subtypes and Establishing a Prognostic Framework for DLBCL Patients via Multiomics Analysis and Machine Learning Approaches
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DOI：10.1155/humu/7614954.png)
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Abstract
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Diffuse large B-cell lymphoma (DLBCL) is characterized by profound heterogeneity that underpins varied clinical outcomes. To decipher this complexity, we performed an integrated single-cell and genomic analysis. Using scRNA-seq data (GSE182434), we identified six distinct malignant B-cell subclusters (MB1-MB6) within the DLBCL ecosystem. Cell–cell communication analysis revealed intricate interaction networks, particularly involving the MIF and Complement pathways. Prognostic analysis of bulk transcriptomic data (GSE32918) identified the MB5-related gene signature as the most critical factor associated with poor overall survival. This MB5 subgroup was associated with enhanced proliferative processes, a higher tumor mutational burden, and specific comutations. Leveraging MB5 marker genes, we developed and validated a robust CoxBoost-RSF machine-learning model that effectively stratified patient risk in independent cohorts. Our study defines the MB5 malignant B-cell subgroup as a key driver of DLBCL aggressiveness and provides both a novel prognostic biomarker and a framework for personalized therapeutic targeting.
Keywords:
diffuse large B-cell lymphoma (DLBCL)
machine learning approaches
molecular subtyping
prognostic framework
single-cell RNA sequencing (scRNA-seq)
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