Bufotalin enhances apoptosis and TMZ chemosensitivity of glioblastoma cells by promoting mitochondrial dysfunction via AKT signaling pathway.

Glioblastoma multiforme (GBM) is the most prevalent and lethal primary intracranial neoplasm in the adult population, with treatments of limited efficacy. Recently, bufotalin has been shown to have anti-cancer activity in a variety of cancers. This investigation aims to investigate the effect of bufotalin on GBM and elucidate its potential underlying mechanism. Our results show that bufotalin not only inhibits the proliferation and epithelial-mesenchymal transition (EMT) but also triggers apoptosis in GBM cells. The result of RNA-seq indicated that bufotalin could induce mitochondrial dysfunction. Moreover, our observations indicate that bufotalin induces an excessive accumulation of intracellular reactive oxygen species (ROS) in GBM cells, leading to mitochondrial dysfunction and the dephosphorylation of AKT. Moreover, bufotalin improved TMZ sensitivity of GBM cells in vitro and in vivo. In conclusion, bufotalin enhances apoptosis and TMZ chemosensitivity of glioblastoma cells by promoting mitochondrial dysfunction via AKT signaling pathway.

Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.

Background: 2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. Methods: This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Discussion: Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. Clinical trail registration: This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.