ArticleBlood cancer journal2021
Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma.
Article in Blood cancer journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- 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
- Pooled it
- Identification of CXCR4 Upregulation in Diffuse Large B-Cell Lymphoma Associated with Prognostic Significance and Clinicopathological Characteristics.Disease markers · 2022Pooled it
- Article
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Genetic Subtype-Based International Prognostic Index Prognostic Model in Diffuse Large B-Cell Lymphoma.MedComm · 2025Article
- Development of a Multilayered Prognostic Model for Wilms' Tumor Based on Characteristic Lymphocyte Genes.Genetics research · 2025Article
- An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network.Computational and structural biotechnology journal · 2024Article
- A Cluster of Evolutionarily Recent KRAB Zinc Finger Proteins Protects Cancer Cells from Replicative Stress-Induced Inflammation.Cancer research · 2024Article
- Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma.Scientific reports · 2024Article
- Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis.Leukemia · 2024Article
- Article
- Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type diffuse large B-cell lymphoma.Blood advances · 2023Article
- Article
- Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.Frontiers in oncology · 2023Article
- Baseline tumor gene expression signatures correlate with chemoimmunotherapy treatment responsiveness in canine B cell lymphoma.PloS one · 2023Article
- IdentifyingPeerJ · 2023Article
- Article
- Genomic crossroads between non-Hodgkin's lymphoma and common variable immunodeficiency.Frontiers in immunology · 2022Article
- Clinical Impact of the Histopathological Index and Neuroimaging Features Status in Primary Central Nervous System Diffuse Large B-Cell Lymphoma: A Single-Center Retrospective Analysis of 51 Cases.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
The clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model's biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.
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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.