Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures

Background

There is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented.

Methods

After strict quality control, numerous data regarding the lymphoma metabolism were mined, quantified, cleaned, fused, and visualized from documents (n = 2925) limited from 2013 to 2022 using R packages, VOSviewer, and GraphPad Prism.

Results

The linear fitting analysis generated functions predicting the annual publication number (y = 31.685x - 63628, R² = 0.93614, Prediction in 2027: 598) and citation number (y = 1363.7x - 2746019, R² = 0.94956, Prediction in 2027: 18201). In the last decade, the most academically performing author, journal, country, and affiliation were Meignan Michel (n = 35), European Journal of Nuclear Medicine and Molecular Imaging (n = 1653), USA (n = 3114), and University of Pennsylvania (n = 86), respectively. The hierarchical clustering based on unsupervised learning further divided research signatures into five clusters, including the basic study cluster (Cluster 1, Total Link Strength [TLS] = 1670, Total Occurrence [TO] = 832) and clinical study cluster (Cluster 3, TLS = 3496, TO = 1328). The timeline distribution indicated that radiomics and artificial intelligence (Cluster 4, Average Publication Year = 2019.39 ± 0.21) is a relatively new research cluster, and more endeavors deserve. Research signature burst and linear regression analysis further confirmed the findings above and revealed additional important results, such as tumor microenvironment (a = 0.6848, R² = 0.5194, p = 0.019) and immunotherapy (a = 1.036, R² = 0.6687, p = 0.004). More interestingly, by performing a "Walktrap" algorithm, the community map indicated that the "apoptosis, metabolism, chemotherapy" (Centrality = 12, Density = 6), "lymphoma, pet/ct, prognosis" (Centrality = 11, Density = 1), and "genotoxicity, mutagenicity" (Centrality = 9, Density = 4) are crucial but still under-explored, illustrating the potentiality of these research signatures in the field of the lymphoma metabolism.

Conclusion

This study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice.

Advances in Understanding of Metabolism of B-Cell Lymphoma: Implications for Therapy

There have been significant recent advances in the understanding of the role of metabolism in normal and malignant B-cell biology. Previous research has focused on the role of MYC and mammalian target of rapamycin (mTOR) and how these interact with B-cell receptor signaling and hypoxia to regulate glycolysis, glutaminolysis, oxidative phosphorylation (OXPHOS) and related metabolic pathways in germinal centers. Many of the commonest forms of lymphoma arise from germinal center B-cells, reflecting the physiological attenuation of normal DNA damage checkpoints to facilitate somatic hypermutation of the immunoglobulin genes. As a result, these lymphomas can inherit the metabolic state of their cell-of-origin. There is increasing interest in the potential of targeting metabolic pathways for anti-cancer therapy. Some metabolic inhibitors such as methotrexate have been used to treat lymphoma for decades, with several new agents being recently licensed such as inhibitors of phosphoinositide-3-kinase. Several other inhibitors are in development including those blocking mTOR, glutaminase, OXPHOS and monocarboxylate transporters. In addition, recent work has highlighted the importance of the interaction between diet and cancer, with particular focus on dietary modifications that restrict carbohydrates and specific amino acids. This article will review the current state of this field and discuss future developments.