ArticleJournal of oncology2022
Defining Diffuse Large B-Cell Lymphoma Immunotypes by CD8+ T Cells and Natural Killer Cells.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Prognostic significance and pathogenesis of RFC3 gene expression in diffuse large B-cell lymphoma.PeerJ · 2025Article
- Article
- Harnessing Unconventional T Cells and Innate Lymphoid Cells to Prevent and Treat Hematological Malignancies: Prospects for New Immunotherapy.Biomolecules · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere 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.
methodsImmune 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.
resultsA 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.
conclusionThis 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.