A novel model that combines signatures of metabolic dysregulation with clinical features may aid in clinical decision-making among patients with diffuse large B cell lymphoma (DLBCL), according to research published in the Journal of Cellular and Molecular Medicine.
DLBCL, which accounts for up to 40% of all non-Hodgkin lymphomas, shows significant clinical heterogeneity. While the International Prognostic Index is frequently used for clinical decision-making, this tool does not include genomic or molecular characteristics, many of which are now known to have a significant effect on disease outcomes.
Metabolic pathways have, furthermore, been implicated in some aspects of DLBCL, and may influence and predict disease progression. For this study, researchers developed a model, relying on multiple metabolic genes, and attempted to determine whether the model was superior at prognostic judgment to conventional predictive tools.
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The authors used mRNA profiles and genomic and clinical data from the Gene Expression Omnibus database to determine genes with metabolic-related signatures; data from 579 patients from the database were included. Overall, 13 genes were identified, and included BPNT1, DCTD, DNMT1, ITPKB, and POLR3A alterations.
After the development of a nomogram model, the researchers calculated risk factors for patients, which constituted the training cohort. In both the training and validation cohorts, the model appeared to show significant clinical benefit, when compared to commonly used prognostic tools (P <.05).
The authors noted, furthermore, that the model, in addition to being more predictive of short-term and long-term overall survival, may help to inform both DLBCL diagnosis and clinical decision-making.
“The fact that our model, based only on genes involved in metabolism, had a high accuracy in predicting survival, reflects the disorder in the metabolic networks in the cancer cells and can potentially be used as biomarkers for the diagnosis and prognosis of DLBCL,” the authors wrote. “Furthermore, this gene signature, whose prognostic performance can be used in clinical practice and functional experiments, should be further investigated to ensure its true significance in personalized therapeutic strategies.”
Reference
Wang H, Shao R, Liu W, Tang H, Lu Y. Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma. J Cell Mol Med. Published online June 14, 2021. doi:10.1111/jcmm.16720
