Search

Abstract Glioblastoma is the most common type of brain tumor. Due to the presence of the blood-brain barrier, the effects of chemotherapy have been unsatisfactory. The combination of focused ultrasound and microbubbles to reversibly open the blood-brain barrier is now considered a key factor in improving treatment outcomes of glioblastoma. In this study, we developed bionic drug delivery microbubbles, which in combination with focused ultrasound had an obvious inhibitory effect on glioblastoma. We extracted the brain microvascular cell membranes, combined them with lipid components, and loaded them with superparamagnetic iron oxide and doxorubicin to prepare biomimetic drug delivery microbubbles (FeDOX@cellMBs). We demonstrated that FeDOX@cellMBs retained the intrinsic properties of loading, such as magnetic properties and drug toxicity, both in vitro and in vivo. FeDOX@cellMBs exhibited good tumor targeting and uptake under the combined action of magnetic and focused ultrasound. Importantly, the FeDOX@cellMBs demonstrated excellent internal stability and effectively inhibited tumor growth in orthotopic glioblastoma mice. Finally, organ H&E staining confirmed that FeDOX@cellMBs were safe for use. In conclusion, FeDOX@cellMBs successfully penetrated the blood-brain barrier and effectively inhibited glioblastoma growth under the combined effects of focused ultrasound and magnetic stimulation. These results provide a new approach for the treatment of glioblastoma, with implications for future clinical translation.

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04–4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77–3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.