Heat Shock Protein Inhibitor 17-Allyamino-17-Demethoxygeldanamycin, a Potent Inductor of Apoptosis in Human Glioma Tumor Cell Lines, Is a Weak Substrate for ABCB1 and ABCG2 Transporters

Heat shock protein 90: biological functions, diseases, and therapeutic targets

Heat shock protein 90 (Hsp90) is a predominant member among Heat shock proteins (HSPs), playing a central role in cellular protection and maintenance by aiding in the folding, stabilization, and modification of diverse protein substrates. It collaborates with various co-chaperones to manage ATPase-driven conformational changes in its dimer during client protein processing. Hsp90 is critical in cellular function, supporting the proper operation of numerous proteins, many of which are linked to diseases such as cancer, Alzheimer's, neurodegenerative conditions, and infectious diseases. Recognizing the significance of these client proteins across diverse diseases, there is a growing interest in targeting Hsp90 and its co-chaperones for potential therapeutic strategies. This review described biological background of HSPs and the structural characteristics of HSP90. Additionally, it discusses the regulatory role of heat shock factor-1 (HSF-1) in modulating HSP90 and sheds light on the dynamic chaperone cycle of HSP90. Furthermore, the review discusses the specific contributions of HSP90 in various disease contexts, especially in cancer. It also summarizes HSP90 inhibitors for cancer treatment, offering a thoughtful analysis of their strengths and limitations. These advancements in research expand our understanding of HSP90 and open up new avenues for considering HSP90 as a promising target for therapeutic intervention in a range of diseases.

A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas

Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the "limma" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the "Seurat" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

Introduction

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Methods and results

In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.

Conclusion

Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Natural Products Targeting Hsp90 for a Concurrent Strategy in Glioblastoma and Neurodegeneration

Glioblastoma multiforme (GBM) is one of the most common aggressive, resistant, and invasive primary brain tumors that share neurodegenerative actions, resembling many neurodegenerative diseases. Although multiple conventional approaches, including chemoradiation, are more frequent in GBM therapy, these approaches are ineffective in extending the mean survival rate and are associated with various side effects, including neurodegeneration. This review proposes an alternative strategy for managing GBM and neurodegeneration by targeting heat shock protein 90 (Hsp90). Hsp90 is a well-known molecular chaperone that plays essential roles in maintaining and stabilizing protein folding to degradation in protein homeostasis and modulates signaling in cancer and neurodegeneration by regulating many client protein substrates. The therapeutic benefits of Hsp90 inhibition are well-known for several malignancies, and recent evidence highlights that Hsp90 inhibitors potentially inhibit the aggressiveness of GBM, increasing the sensitivity of conventional treatment and providing neuroprotection in various neurodegenerative diseases. Herein, the overview of Hsp90 modulation in GBM and neurodegeneration progress has been discussed with a summary of recent outcomes on Hsp90 inhibition in various GBM models and neurodegeneration. Particular emphasis is also given to natural Hsp90 inhibitors that have been evidenced to show dual protection in both GBM and neurodegeneration.