Establishment and validation of five autophagy-related signatures for predicting survival and immune microenvironment in glioma

Autophagy in brain tumors: molecular mechanisms, challenges, and therapeutic opportunities

Autophagy is responsible for maintaining cellular balance and ensuring survival. Autophagy plays a crucial role in the development of diseases, particularly human cancers, with actions that can either promote survival or induce cell death. However, brain tumors contribute to high levels of both mortality and morbidity globally, with resistance to treatments being acquired due to genetic mutations and dysregulation of molecular mechanisms, among other factors. Hence, having knowledge of the role of molecular processes in the advancement of brain tumors is enlightening, and the current review specifically examines the role of autophagy. The discussion would focus on the molecular pathways that control autophagy in brain tumors, and its dual role as a tumor suppressor and a supporter of tumor survival. Autophagy can control the advancement of different types of brain tumors like glioblastoma, glioma, and ependymoma, demonstrating its potential for treatment. Autophagy mechanisms can influence metastasis and drug resistance in glioblastoma, and there is a complex interplay between autophagy and cellular responses to stress like hypoxia and starvation. Autophagy can inhibit the growth of brain tumors by promoting apoptosis. Hence, focusing on autophagy could offer fresh perspectives on creating successful treatments.

Advancing glioblastoma treatment by targeting metabolism

Alterations in cellular metabolism are important hallmarks of glioblastoma(GBM). Metabolic reprogramming is a critical feature as it meets the higher nutritional demand of tumor cells, including proliferation, growth, and survival. Many genes, proteins, and metabolites associated with GBM metabolism reprogramming have been found to be aberrantly expressed, which may provide potential targets for cancer treatment. Therefore, it is becoming increasingly important to explore the role of internal and external factors in metabolic regulation in order to identify more precise therapeutic targets and diagnostic markers for GBM. In this review, we define the metabolic characteristics of GBM, investigate metabolic specificities such as targetable vulnerabilities and therapeutic resistance, as well as present current efforts to target GBM metabolism to improve the standard of care.

CAMK1D Inhibits Glioma Through the PI3K/AKT/mTOR Signaling Pathway

Calcium/calmodulin-dependent protein ID (CAMK1D) is widely expressed in many tissues and involved in tumor cell growth. However, its role in gliomas has not yet been elucidated. This study aimed to investigate the roles of CAMK1D in the proliferation, migration, and invasion of glioma. Through online datasets, Western blot, and immunohistochemical analysis, glioma tissue has significantly lower CAMK1D expression levels than normal brain (NB) tissues, and CAMK1D expression was positively correlated with the WHO classification. Kaplan-Meier survival analysis shows that CAMK1D can be used as a potential prognostic indicator to predict the overall survival of glioma patients. In addition, colony formation assay, cell counting Kit-8, and xenograft experiment identified that knockdown of CAMK1D promotes the proliferation of glioma cells. Transwell and wound healing assays identified that knockdown of CAMK1D promoted the invasion and migration of glioma cells. In the above experiments, the results of overexpression of CAMK1D were all contrary to those of knockdown. In terms of mechanism, this study found that CAMK1D regulates the function of glioma cells by the PI3K/AKT/mTOR pathway. In conclusion, these findings suggest that CAMK1D serves as a prognostic predictor and a new target for developing therapeutics to treat glioma.

Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection

The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.

Procollagen C-protease enhancer protein is a prognostic factor for glioma and promotes glioma development by regulating multiple tumor-related pathways and immune microenvironment

OBJECTIVES

Glioma is a common type of brain tumor with high incidence and mortality rates. Procollagen C-protease enhancer protein (PCOLCE) has been shown to regulate tumor growth and metastasis in several cancers. However, the role of PCOLCE in glioma is unknown. This study aims to assess the association between PCOLCE and prognosis of glioma, and investigated the potential mechanisms.

METHODS

The prognostic value of PCOLCE was determined using data from nine publicly available glioma cohorts. We also investigated the relationship between PCOLCE and glioma immune microenvironment and predicted response to immunotherapy based on the expression levels of PCOLCE. The potential roles of PCOLCE in glioma were also explored and validated in cell experiment.

RESULTS

Survival analysis suggested that high-PCOLCE expression was associated with poor prognosis in glioma. Upregulation of PCOLCE enhanced an immune suppressive microenvironment in glioma by regulating immunocyte infiltration and Cancer-Immunity Cycle. Cox and ROC analysis revealed that PCOLCE was a prognostic factor for glioma and could be used to predict survival of the patients. Patients with low-PCOLCE expression were more likely to respond to Immunotherapy with ICI (immune checkpoint inhibitor) and survive longer. Enrichment analysis showed that PCOLCE was associated with multiple tumor-related pathways. Finally, we demonstrated that the knockdown of PCOLCE inhibited glioma development by regulating cell cycle and promoting apoptosis in in vitro experiments.

CONCLUSION

PCOLCE promotes glioma progression by regulating multiple tumor-related pathways and immune microenvironment and can be used as a prognostic factor for glioma.