Highlights
Characteristics of 62 patients with spinal GCTB who underwent surgery.
A prognostic classification model was built based on features selected by SVM.
The combined histogram and texture features could predict recurrence of GCTB.
PurposeThis project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.Material and MethodsA retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.ResultsThe immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p < 0.001. For p53, SINS (p = 0.030) and Enneking stage (p = 0.017) were higher in mutant than wild-type groups. The VEGF radiomics model built using 3 features achieved an area under the curve (AUC) of 0.88, and the p53 radiomics model built using 4 features had an AUC of 0.79. The conventional model built using SINS, and the Enneking stage had a slightly lower AUC of 0.81 for VEGF and 0.72 for p53.Conclusionp53 and VEGF are associated with prognosis in patients with spinal GCTB, and the radiomics analysis based on preoperative CT provides a feasible method for the evaluation of these two biomarkers, which may aid in choosing better management strategies.
The use of differentiating human induced pluripotent stem cells (hiPSCs) in mini-tissue organoids provides an invaluable resource for regenerative medicine applications, particularly in the field of disease modeling. However, most studies using a kidney organoid model, focused solely on the transcriptomics and did not explore mechanisms of regulating kidney organoids related to metabolic effects and maturational phenotype. Here, we applied metabolomics coupled with transcriptomics to investigate the metabolic dynamics and function during kidney organoid differentiation. Not only did we validate the dominant metabolic alteration from glycolysis to oxidative phosphorylation in the iPSC differentiation process but we also showed that glycine, serine, and threonine metabolism had a regulatory role during kidney organoid formation and lineage maturation. Notably, serine had a role in regulating S-adenosylmethionine (SAM) to facilitate kidney organoid formation by altering DNA methylation. Our data revealed that analysis of metabolic characterization broadens our ability to understand phenotype regulation. The utilization of this comparative omics approach, in studying kidney organoid formation, can aid in deciphering unique knowledge about the biological and physiological processes involved in organoid-based disease modeling or drug screening.
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