Ferroptosis in glioma treatment: Current situation, prospects and drug applications

Research progress on ferroptosis in gliomas (Review)

Glioma is the most prevalent type of brain tumor characterized by a poor 5-year survival rate and a high mortality rate. Malignant gliomas are commonly treated by surgery, chemotherapy and radiotherapy. However, due to toxicity and resistance to chemoradiotherapy, these treatments can be ineffective. Anxiety and depression are highly prevalent in patients with glioma, adversely affecting disease prognosis and posing societal concerns. Ferroptosis is a type of non-apoptotic, iron-dependent cell death characterized by the accumulation of lethal reactive oxygen species produced by iron metabolism, and it serves a key role in numerous diseases. Regulation of iron phagocytosis may serve as a therapeutic strategy for the development of novel glioma treatments. The present review discusses the mechanisms underlying the occurrence and regulation of ferroptosis, its role in the genesis and evolution of gliomas, and its association with glioma-related anxiety and depression. By exploring potential targets for glioma treatment, the present review provides a theoretical basis for the development of novel therapeutic strategies against glioma.

Novel Insight into Ferroptosis in Kidney Diseases

BACKGROUND

Various kidney diseases such as acute kidney injury, chronic kidney disease, polycystic kidney disease, renal cancer, and kidney stones, are an important part of the global burden, bringing a huge economic burden to people around the world. Ferroptosis is a type of nonapoptotic iron-dependent cell death caused by the excess of iron-dependent lipid peroxides and accompanied by abnormal iron metabolism and oxidative stress. Over the past few decades, several studies have shown that ferroptosis is associated with many types of kidney diseases. Studying the mechanism of ferroptosis and related agonists and inhibitors may provide new ideas and directions for the treatment of various kidney diseases.

SUMMARY

In this review, we discuss the differences between ferroptosis and other types of cell death such as apoptosis, necroptosis, pyroptosis, cuprotosis, pathophysiological features of the kidney, and ferroptosis-induced kidney injury. We also provide an overview of the molecular mechanisms involved in ferroptosis and events that lead to ferroptosis. Furthermore, we summarize the possible clinical applications of this mechanism among various kidney diseases.

KEY MESSAGE

The current research suggests that future therapeutic efforts to treat kidney ailments would benefit from a focus on ferroptosis.

Multiparametric magnetic resonance imaging-derived deep learning network to determine ferroptosis-related gene signatures in gliomas

Introduction

Ferroptosis-related gene (FRG) signature is important for assessing novel therapeutic approaches and prognosis in glioma. We trained a deep learning network for determining FRG signatures using multiparametric magnetic resonance imaging (MRI).

Methods

FRGs of patients with glioma were acquired from public databases. FRG-related risk score stratifying prognosis was developed from The Cancer Genome Atlas (TCGA) and validated using the Chinese Glioma Genome Atlas. Multiparametric MRI-derived glioma images and the corresponding genomic information were obtained for 122 cases from TCGA and The Cancer Imaging Archive. The deep learning network was trained using 3D-Resnet, and threefold cross-validation was performed to evaluate the predictive performance.

Results

The FRG-related risk score was associated with poor clinicopathological features and had a high predictive value for glioma prognosis. Based on the FRG-related risk score, patients with glioma were successfully classified into two subgroups (28 and 94 in the high- and low-risk groups, respectively). The deep learning networks TC (enhancing tumor and non-enhancing portion of the tumor core) mask achieved an average cross-validation accuracy of 0.842 and an average AUC of 0.781, while the deep learning networks WT (whole tumor and peritumoral edema) mask achieved an average cross-validation accuracy of 0.825 and an average AUC of 0.781.

Discussion

Our findings indicate that FRG signature is a prognostic indicator of glioma. In addition, we developed a deep learning network that has high classification accuracy in automatically determining FRG signatures, which may be an important step toward the clinical translation of novel therapeutic approaches and prognosis of glioma.