No abstract

Targeting aberrant metabolism is a promising strategy for inhibiting cancer growth and metastasis. Research is now geared towards investigating the inhibition of glycolysis for anticancer drug development. Betulinic acid (BA) has demonstrated potent anticancer activities in multiple malignancies. However, its regulatory effects on glycolysis and the underlying molecular mechanisms are still unclear. BA inhibited invasion and migration of highly aggressive breast cancer cells. Moreover, BA could suppress aerobic glycolysis of breast cancer cells presenting as a reduction of lactate production, quiescent energy phenotype transition, and downregulation of aerobic glycolysis-related proteins. In this study, glucose-regulated protein 78 (GRP78) was also identified as the molecular target of BA in inhibiting aerobic glycolysis. BA treatment led to GRP78 overexpression, and GRP78 knockdown abrogated the inhibitory effect of BA on glycolysis. Further studies demonstrated that overexpressed GRP78 activated the endoplasmic reticulum (ER) stress sensor PERK. Subsequent phosphorylation of eIF2α led to the inhibition of β-catenin expression, which resulted in the inhibition of c-Myc-mediated glycolysis. Coimmunoprecipitation assay revealed that BA interrupted the binding between GRP78 and PERK, thereby initiating the glycolysis inhibition cascade. Finally, the lung colonization model validated that BA inhibited breast cancer metastasis in vivo, as well as suppressed the expression of aerobic glycolysis-related proteins. In conclusion, our study not only provided a promising drug for aerobic glycolysis inhibition but also revealed that GRP78 is a novel molecular link between glycolytic metabolism and ER stress during tumor metastasis.

Transplantation of neural stem cells (NSCs) is a promising treatment paradigm to replace lost neurons and reconstruct the damaged neural circuit after ischemic stroke. However, most transplanted NSCs often differentiate into astrocytes rather than functional neurons, and the poor neuronal differentiation adversely affects the therapeutic outcome of NSCs and limits their clinical translation for stroke therapy. Herein, a theranostic nanomedicine is developed to codeliver superparamagnetic iron oxide nanoparticles (SPIO) and small interfering RNA/antisense oligonucleotides (siRNA/ASO) against Pnky long noncoding RNA (lncRNA) into NSCs. This nanomedicine not only directs neuronal differentiation of NSCs through silencing the Pnky lncRNA but also allows an in vivo tracking of NSCs with magnetic resonance imaging. The enhanced neuronal differentiation of NSCs significantly improved the structural and functional recovery of the damaged brain after a stroke. The results demonstrate the great potential of the multifunctional nanomedicine targeting lncRNA to enhance stem cell-based therapies for a stroke.

◥Purpose: Bevacizumab is considered a promising therapy for brain necrosis after radiotherapy, while some patients fail to derive benefit or even worsen. Hence, we developed and validated a radiomics model for predicting the response to bevacizumab in patients with brain necrosis after radiotherapy.Experimental Design: A total of 149 patients (with 194 brain lesions; 101, 51, and 42 in the training, internal, and external validation sets, respectively) receiving bevacizumab were enrolled. In total, 1,301 radiomic features were extracted from the pretreatment MRI images of each lesion. In the training set, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm. Multivariable logistic regression analysis was then used to develop a radiomics model incorporated in the radiomics signature and independent clinical predictors. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with internal and external validation.Results: The radiomics signature consisted of 18 selected features and showed good discrimination performance. The model, which integrates the radiomics signature, the interval between radiotherapy and diagnosis of brain necrosis, and the interval between diagnosis of brain necrosis and treatment with bevacizumab, showed favorable calibration and discrimination in the training set (AUC 0.916). These findings were confirmed in the validation sets (AUC 0.912 and 0.827, respectively). Decision curve analysis confirmed the clinical utility of the model.Conclusions: The presented radiomics model, available as an online calculator, can serve as a user-friendly tool for individualized prediction of the response to bevacizumab in patients with brain necrosis after radiotherapy.