Abstract
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.
Highlights
diffuse large B-cell lymphoma (DLBCL), responsible for nearly 40% of non-Hodgkin lymphoma, is a hematological cancer of B cells [1, 2]
About 30% of DLBCL samples exhibited the loss of HLA-I and CD58 on the surface, which are crucial in the recognition of malignant cells by T cells and natural killer cells [10]. e analysis of tumor microenvironment (TME) could explore the relationships of its components with prognosis, making treatment planning in DLBCL more personalized
In TCGA-DLBCL and GSE21846, none of the immune cells had a significant impact on prognosis. e immune cell data from different data sets were combined
Summary
DLBCL, responsible for nearly 40% of non-Hodgkin lymphoma, is a hematological cancer of B cells [1, 2]. For patients with DLBCL, chemotherapy agents are the first treatment choice [1]. E analysis of TME could explore the relationships of its components with prognosis, making treatment planning in DLBCL more personalized. Ere are currently several monoclonal antibodies being developed and evaluated for the treatment of DLBCL that target the PD-1/PD-L1 pathway [5]. Using 432 samples of DLBCL collected from five data sets in the current study, we identified two immune subtypes. In the training and testing data sets, we have analyzed the association between immune subtypes and prognosis, immune cells, and immune pathways. Using the random forest algorithm, 12 genes were selected for the construction of the machine learning model to predict immune subtypes for patients with DLBCL. We constructed a 12-gene panel to predict the prognosis of DLBCL patients and validated the prediction using a validation data set of DLBCL patients
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