Repressing iron overload ameliorates central post-stroke pain via the Hdac2-Kv1.2 axis in a rat model of hemorrhagic stroke.

JOURNAL/nrgr/04.03/01300535-202412000-00027/figure1/v/2024-04-08T165401Z/r/image-tiff Thalamic hemorrhage can lead to the development of central post-stroke pain. Changes in histone acetylation levels, which are regulated by histone deacetylases, affect the excitability of neurons surrounding the hemorrhagic area. However, the regulatory mechanism of histone deacetylases in central post-stroke pain remains unclear. Here, we show that iron overload leads to an increase in histone deacetylase 2 expression in damaged ventral posterolateral nucleus neurons. Inhibiting this increase restored histone H3 acetylation in the Kcna2 promoter region of the voltage-dependent potassium (Kv) channel subunit gene in a rat model of central post-stroke pain, thereby increasing Kcna2 expression and relieving central pain. However, in the absence of nerve injury, increasing histone deacetylase 2 expression decreased Kcna2 expression, decreased Kv current, increased the excitability of neurons in the ventral posterolateral nucleus area, and led to neuropathic pain symptoms. Moreover, treatment with the iron chelator deferiprone effectively reduced iron overload in the ventral posterolateral nucleus after intracerebral hemorrhage, reversed histone deacetylase 2 upregulation and Kv1.2 downregulation, and alleviated mechanical hypersensitivity in central post-stroke pain rats. These results suggest that histone deacetylase 2 upregulation and Kv1.2 downregulation, mediated by iron overload, are important factors in central post-stroke pain pathogenesis and could serve as new targets for central post-stroke pain treatment.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Comprehensive analysis of the clinical and biological significances of cholesterol metabolism in lower-grade gliomas.

BACKGROUND: As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs). METHODS: The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature. RESULTS: Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients. CONCLUSION: The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.