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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
Published on: December 28, 2015
Identifying Distinct Molecular Subtypes and Establishing a Prognostic Framework for DLBCL Patients via Multiomics
Hongyu Shen1,2, Jinbo Lu3, Qi Yan4
1Department of Hematology, The First Affiliated Hospital With Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu, China, jsph.net.
Human Mutation
|July 13, 2026
Summary
Researchers identified a specific malignant B-cell subgroup (MB5) driving Diffuse Large B-cell Lymphoma (DLBCL) aggressiveness. This discovery offers a new prognostic biomarker and therapeutic target for DLBCL patients.
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Diffuse Large B-cell Lymphoma (DLBCL) exhibits significant heterogeneity, leading to diverse clinical outcomes.
- Understanding the molecular drivers of DLBCL aggressiveness is crucial for improving patient prognosis.
Purpose of the Study:
- To dissect the complexity of DLBCL by integrating single-cell and genomic analyses.
- To identify specific malignant B-cell subpopulations and their role in DLBCL progression.
- To develop a novel prognostic biomarker for DLBCL risk stratification.
Main Methods:
- Single-cell RNA sequencing (scRNA-seq) was used to identify malignant B-cell subclusters (MB1-MB6).
- Cell-cell communication analysis explored interaction networks, focusing on MIF and Complement pathways.
- Prognostic analysis of bulk transcriptomic data identified a gene signature associated with survival.
- A CoxBoost-RSF machine-learning model was developed using MB5 marker genes for risk stratification.
Main Results:
- Six distinct malignant B-cell subclusters were identified within the DLBCL ecosystem.
- The MB5 subgroup exhibited enhanced proliferation, higher tumor mutational burden, and specific comutations.
- An MB5-related gene signature was identified as a critical factor associated with poor overall survival in DLBCL.
- The developed machine-learning model effectively stratified patient risk in independent cohorts.
Conclusions:
- The MB5 malignant B-cell subgroup is a key driver of DLBCL aggressiveness.
- This study provides a novel prognostic biomarker for DLBCL.
- The findings offer a framework for developing personalized therapeutic strategies for DLBCL.
