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Abstract Lacking effective therapeutic targets heavily restricts the improvement of clinical prognosis for patients diagnosed with esophageal squamous cell carcinoma (ESCC). Ubiquitin Specific Peptidase 21 (USP21) is dysregulated in plenty of human cancers, however, its potential function and relevant molecular mechanisms in ESCC malignant progression as well as its value in clinical translation remain largely unknown. Here, in vitro and in vivo experiments revealed that aberrant upregulation of USP21 accelerated the proliferation and metastasis of ESCC in a deubiquitinase-dependent manner. Mechanistically, we found that USP21 binds to, deubiquitinates, and stabilizes the G3BP Stress Granule Assembly Factor 1 (G3BP1) protein, which is required for USP21-mediated ESCC progression. Further molecular studies demonstrated that the USP21/G3BP1 axis played a tumor-promoting role in ESCC progression by activating the Wnt/β-Catenin signaling pathway. Additionally, disulfiram (DSF), an inhibitor against USP21 deubiquitylation activity, markedly abolished the USP21-mediated stability of G3BP1 protein and significantly displayed an anti-tumor effect on USP21-driving ESCC progression. Finally, the regulatory axis of USP21/G3BP1 was demonstrated to be aberrantly activated in ESCC tumor tissues and closely associated with advanced clinical stages and unfavorable prognoses, which provides a promising therapeutic strategy targeting USP21/G3BP1 axis for ESCC patients.

PurposeThe single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various creatinine clearance rates (CLcr).MethodsThe PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUCR) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUCR of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria.ResultsFour CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUCR value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various CLcr were predicted.ConclusionCDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting “mathematical knowledge transfer and application” and also providing reference for quantitative and personalized research on similar drugs.