Comparative efficacy and safety of therapeutics for elderly glioblastoma patients: A Bayesian network analysis

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma

OBJECTIVE

To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.

METHODS

Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA).

RESULTS

A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance.

CONCLUSION

Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

Suppression of ITPKB degradation by Trim25 confers TMZ resistance in glioblastoma through ROS homeostasis

Temozolomide (TMZ) represents a standard-of-care chemotherapeutic agent in glioblastoma (GBM). However, the development of drug resistance constitutes a significant hurdle in the treatment of malignant glioma. Although specific innovative approaches, such as immunotherapy, have shown favorable clinical outcomes, the inherent invasiveness of most gliomas continues to make them challenging to treat. Consequently, there is an urgent need to identify effective therapeutic targets for gliomas to overcome chemoresistance and facilitate drug development. This investigation used mass spectrometry to examine the proteomic profiles of six pairs of GBM patients who underwent standard-of-care treatment and surgery for both primary and recurrent tumors. A total of 648 proteins exhibiting significant differential expression were identified. Gene Set Enrichment Analysis (GSEA) unveiled notable alterations in pathways related to METABOLISM_OF_LIPIDS and BIOLOGICAL_OXIDATIONS between the primary and recurrent groups. Validation through glioma tissue arrays and the Xiangya cohort confirmed substantial upregulation of inositol 1,4,5-triphosphate (IP3) kinase B (ITPKB) in the recurrence group, correlating with poor survival in glioma patients. In TMZ-resistant cells, the depletion of ITPKB led to an increase in reactive oxygen species (ROS) related to NADPH oxidase (NOX) activity and restored cell sensitivity to TMZ. Mechanistically, the decreased phosphorylation of the E3 ligase Trim25 at the S100 position in recurrent GBM samples accounted for the weakened ITPKB ubiquitination. This, in turn, elevated ITPKB stability and impaired ROS production. Furthermore, ITPKB depletion or the ITPKB inhibitor GNF362 effectively overcome TMZ chemoresistance in a glioma xenograft mouse model. These findings reveal a novel mechanism underlying TMZ resistance and propose ITPKB as a promising therapeutic target for TMZ-resistant GBM.

Multiomics blueprint of PANoptosis in deciphering immune characteristics and prognosis stratification of glioma patients

BACKGROUND

As the most prevalent primary brain tumor in adults, glioma accounts for the majority of all central nervous system malignant tumors. The concept of PANoptosis is a relatively new, underlining the interconnection and synergy among three distinct pathways: pyroptosis, apoptosis and necroptosis.

METHODS

We performed single-cell annotations of glioma cells and determined crucial signaling pathways through cell chat analysis. Using least absolute shrinkage and selection operator (LASSO) and Cox analyses, we identified a gene set with prognostic values. Our model was validated using independent external cohort. In addition, we employed single-sample gene set enrichment analysis and xCell analyses to describe the detailed profile of infiltrated immune cells and depicted the gene mutation landscape in the two groups.

RESULTS

We identified seven distinct cell clusters in glioma samples, including oligodendrocyte precursor cells (OPCs), myeloid cells, tumor cells, oligodendrocytes, astrocytes, vascular cells and neuronal cells. We found that myeloid cells showed the highest PANoptosis activity. An intense mutual cell communication pattern between the tumor cells and OPCs and oligodendrocytes was observed. Differentially expressed genes between the high-PANoptosis and low-PANoptosis cell groups were obtained, which were enriched to actin cytoskeleton, cell adhesion molecules and gamma R-mediated phagocytosis pathways. We determined a set of five genes of prognostic significance: SAA1, SLPI, DCX, S100A8 and TNR. The prognostic differences between the two groups in the internal and external sets were found to be statistically significant. We found a marked correlation between S100A8 and activated dendritic cell, macrophage, mast cell, myeloid derived suppressor cell and Treg infiltration. Moreover, we have observed a significant increase of PTEN mutation in the high risk (HR) group of glioma patients.

CONCLUSIONS

In the present study, we have constructed a prognostic model that is based on the PANoptosis, and we have demonstrated its significant efficacy in stratifying patients with glioma. This innovative prognostic model offers novel insights into precision immune treatments that could be used to combat this disease and improve patient outcomes, thereby providing a new avenue for personalized treatment options.

Ganciclovir as a potential treatment for glioma: a systematic review and meta-analysis

BACKGROUND

Glioma is a challenging malignant tumor with a low survival rate and no effective treatment. Recently, ganciclovir, an antiviral drug, combined with gene therapy and its own antiviral ability, has been proposed as a potential treatment for glioma. However, there are differences in the results of various clinical trials. In this study, we conducted a systematic review and meta-analysis to evaluate the efficacy of ganciclovir in treating glioma.

METHODS

We searched databases such as PubMed, EMBASE, and Cochrane Library before March 30, 2023. The search terms included glioma, ganciclovir, valganciclovir and treatment. Calculated 1, 2 and 4-year survival rate by risk difference (RD), and overall survival (OS) by odds ratio (OR).

RESULTS

Five randomized controlled trials (RCTs) with a total of 606 high-grade glioma patients were included. The results showed that ganciclovir can improve 2-yeaer (RD = 0.179, 95% CI 0.012-0.346, P = 0.036) and 4-year survival rate (RD = 0.185, 95% CI 0.069-0.3, P = 0.002) and OS (OR 2.393, 95% CI 1.212-4.728, P = 0.012) compared with the control group.

CONCLUSIONS

This meta-analysis showed that ganciclovir significantly improved the prognosis of glioma patients. Therefore, we suggest that more cases of ganciclovir as a glioma treatment can be conducted, or a large clinical trial can be designed.