Defining Diffuse Large B-Cell Lymphoma Immunotypes by CD8+T Cells and Natural Killer Cells

Background. There is a poor prognosis for diffuse large B-cell lymphoma (DLBCL), one of the most common types of non-Hodgkin lymphoma (NHL). Through gene expression profiles, this study intends to reveal potential subtypes among patients with DLBCL by evaluating their prognostic impact on immune cells. Methods. Immune subtypes were developed based on CD8+ T cells and natural killer cells calculated from gene expression profiles. The comparison of prognoses and enriched pathways was made between immune subtypes. Following this validation step, samples from the independent data set were analyzed to determine the correlation between immune subtype and prognosis and immune checkpoint blockade (ICB) response. To provide a model to predict the DLBCL immune subtypes, machine learning methods were used. The virtual screening and molecular docking were adopted to identify small molecules to target the immune subtype biomarkers. Results. A training data set containing 432 DLBCL samples from five data sets and a testing dataset containing 420 DLBCL samples from GSE10846 were used to develop and validate immune subtypes. There were two novel immune subtypes identified in this study: an inflamed subtype (IS) and a noninflamed subtype (NIS). When compared with NIS, IS was associated with higher levels of immune cells and a better prognosis for immunotherapy. Based on the random forest algorithm, a robust machine learning model has been established by 12 hub genes, and the area under the curve (AUC) value is 0.948. Three small molecules were selected to target NIS biomarkers, including VGF, RAD54L, and FKBP8. Conclusion. This study assessed immune cells as prognostic factors in DLBCL, constructed an immune subtype that could be used to identify patients who would benefit from ICB, and constructed a model to predict the immune subtype.

Automated summary

This study assessed the impact of immune cells on the prognosis of DLBCL patients using gene expression profiles, identified two novel immune subtypes, and developed a machine learning model to predict these immune subtypes.