RESUMEN
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.
Asunto(s)
Inteligencia Artificial , Linfoma , Humanos , Tomografía Computarizada por Tomografía de Emisión de Positrones , Linfoma/terapia , Algoritmos , Apoptosis , Microambiente TumoralRESUMEN
OBJECTIVE: To investigate the expression level and clinical significance of Wilms' tumor 1 (WT1) in bone marrow of patients with myelodysplastic syndromes (MDS). METHODS: The clinical data of 147 MDS patients who accepted real-time quantitative polymerase chain reaction (RT-PCR) to detect the expression level of WT1 in bone marrow before treated in Nanfang Hospital, Southern Medical University from January 2017 to April 2021 were retrospectively analyzed. According to the expression level of WT1, the patients were divided into WT1+ group and WT1- group, their clinical characteristics and prognosis were analyzed. RESULTS: The positive rate of WT1 in 147 MDS patients was 82.3%. There were significant differences in bone marrow blast count, aberrant karyotypes, WHO 2016 classification, and IPSS-R stratification between WT1+ group and WT1- group (all P<0.05). Furthermore, the higher the malignant degree of MDS subtype and the risk stratification of IPSS-R, the higher expression level of WT1. Compared with WT1- group, there were no differences in overall survival (OS) time and the time of transformation to AML in WT1+ group (both P>0.05). In patients who did not accept transplantation, the median OS time of WT1+ patients was significantly shorter than that of WT1- patients (P=0.049). Besides, regarding WT1+ group, patients who underwent transplantation had longer OS time and lower mortality than those who received hypomethylating agents (P=0.002, P=0.005). CONCLUSION: WT1 expression level directly reflects the disease progression, and it is also associated with prognosis of MDS patients.