Identification of intratumoral microbiome-driven immune modulation and therapeutic implications in diffuse large B-cell lymphoma.

Objective: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, with significant clinical heterogeneity. Recent studies suggest that the intratumoral microbiome may influence the tumor microenvironment, affecting patient prognosis and therapeutic responses. This study aims to identify microbiome-related subtypes in DLBCL and assess their impact on prognosis, immune infiltration, and therapeutic sensitivity.

Methods: Transcriptomic and microbiome data from 48 DLBCL patients were obtained from public databases. Consensus clustering was used to classify patients into distinct microbiome-related subtypes. Functional enrichment analysis, immune infiltration assessments, and single-cell RNA sequencing were performed to explore the biological characteristics of these subtypes. Drug sensitivity predictions were made using the OncoPredict tool. Hub genes' expression and biological function were validated and inferred in cell lines and independent cohorts of DLBCL.

Results: Two distinct microbiome-related subtypes were identified. Patients in Cluster 1 exhibited significantly better overall survival (P < 0.05), with higher immune infiltration of regulatory T cells and M0 macrophages compared to Cluster 2, which was associated with poorer outcomes. Functional enrichment analysis revealed that genes in Cluster 1 were involved in immune regulatory pathways, including cytokine-cytokine receptor interactions and chemokine signaling, suggesting enhanced anti-tumor immune responses. In contrast, genes in Cluster 2 were enriched in immunosuppressive pathways, contributing to a less favorable prognosis. Single-cell RNA sequencing analysis revealed significant heterogeneity in immune cell populations within the tumor microenvironment. B cells exhibited the most notable heterogeneity, as indicated by stemness and differentiation potential scoring. Intercellular communication analysis demonstrated that B cells played a key role in immune cell interactions, with significant differences observed in MIF signaling between B-cell subgroups. Pseudo-time analysis further revealed distinct differentiation trajectories of B cells, highlighting their potential heterogeneity across different immune environments. Metabolic pathway analysis showed significant differences in the average expression levels of metabolic pathways among B-cell subgroups, suggesting functional specialization. Furthermore, interaction analysis between core genes involved in B-cell differentiation and microbiome-driven differentially expressed genes identified nine common genes (GSTM5, LURAP1, LINC02802, MAB21L3, C2CD4D, MMEL1, TSPAN2, and CITED4), which were found to play critical roles in B-cell differentiation and were influenced by the intratumoral microbiome. DLBCL cell lines and clinical cohorts validated that MMEL1 and CITED4 with important biologically function in DLBCL cell survival and subtype classification.

Conclusions: This study demonstrates the prognostic significance of the intratumoral microbiome in DLBCL, identifying distinct microbiome-related subtypes that impact immune infiltration, metabolic activity, and therapeutic responses. The findings provide insights into the immune heterogeneity within the tumor microenvironment, focusing on B cells and their differentiation dynamics. These results lay the foundation for microbiome-based prognostic biomarkers and personalized treatment approaches, ultimately aiming to enhance patient outcomes in DLBCL.