Background: The effectiveness of PD-1/PD-L1 immune checkpoint blockade therapy in glioblastoma (GBM) is limited due to the tumor immunosuppressive microenvironment (TIME). Therefore, strategies of reprogramming TIME to a pro-inflammatory state offer a promising therapeutic approach. Methods: We applied bioinformatics analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (stRNA-seq) to identify a significant accumulation of cancer-associated fibroblasts (CAFs) subclusters with elevated LRRC15 expression in the nonresponders to anti-PD-1 therapy. The molecular mechanism of LRRC15 was functionally validated in vitro and in vivo. Results: These CAFs subclusters drive the infiltration of macrophages (Mφ) into the tumor microenvironment and promote their polarization toward the M2 phenotype. Deletion of Lrrc15 in CAFs significantly restrained tumor growth and prolonged survival in mouse models. Mechanistically, LRRC15 in CAFs promotes IL8 expression by activating the downstream FAK/SRC/NF-κB pathways, leading to Mφ migration and M2-like polarization. In turn, M2-like Mφs secrete TGF-β, which induces LRRC15 expression in CAFs via SMAD2-dependent transcriptional activation. Targeting CAFs subcluster with elevated LRRC15 expression in combination with anti-PD-1 treatment enhanced antitumor efficacy. Conclusions: Our findings suggest that targeting LRRC15 may provide a novel strategy to augment anti-PD-1 therapy and overcome immunotherapy resistance in GBM.

Introduction: Glioma is the most common primary tumor in the central nervous system, and prognostic biomarkers are still lacking. HIC ZBTB transcriptional repressor 2 (HIC2) is a hypermethylated gene that plays an important functional role in cardiac development. However, the actual role of HIC2 in glioma progression remains unclear. This study aimed to investigate the function of HIC2 and whether it could be a prognostic biomarker in glioma.Methods: The DNA methylation and mRNA expression profiles of HIC2 were downloaded from public databases. The prognostic prediction ability and mechanism research of HIC2 were evaluated.Results: We found that HIC2 was hypermethylated and expressed at low levels in glioma samples. Hypermethylation and low expression of HIC2 predicted poor prognosis. Multivariate Cox regression analysis suggested that HIC2 was an independent prognostic factor for gliomas. Co‐IP assays demonstrated that HIC2 interacts with RNF44, and dual‐luciferase reporter assays and ChIP assays revealed that HIC2 transcriptionally inhibits PTPRN2 expression.Conclusions: Our findings suggest that HIC2 represents a tumor suppressor gene and prognostic biomarker for glioma progression and that overexpression of HIC2 inhibits the proliferation of glioma in vitro and in vivo by interacting with RNF44 and PTPRN2.

Glioblastoma (GBM) is a highly aggressive brain tumor with limited effective treatment options. Ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, has emerged as a potential vulnerability in GBM, yet its regulatory mechanisms remain poorly defined. In this study, we investigated the impact of TP53 mutations on ferroptosis sensitivity and identified TP63 as a critical modulator in this process. Integrative transcriptomic and mutational analyses of GBM samples from The Cancer Genome Atlas (TCGA) revealed that TP53 mutations are associated with poor prognosis and altered expression of genes involved in iron homeostasis and glutathione metabolism. Notably, TP63 (mainly ΔNp63 isoform) expression was markedly upregulated in TP53-mutant GBM. Functional experiments demonstrated that TP63 suppresses ferroptosis by reducing reactive oxygen species (ROS) accumulation and lipid peroxidation. Mechanistic studies further showed that TP53 mutations activate the Wnt/β-catenin signaling pathway, leading to nuclear accumulation of β-catenin, which transcriptionally upregulates TP63. In turn, TP63 directly enhances GPX4 expression, a key inhibitor of ferroptosis. These findings define a novel TP53 mutation-Wnt/β-catenin-TP63-GPX4 signaling axis that promotes ferroptosis resistance in GBM and deepen our understanding of ferroptosis regulation in this malignancy.

SignificancePreclinical ischemic stroke models frequently rely on inhalational anesthetics, which can alter cerebrovascular dynamics and confound ischemic outcomes. The specific impact of anesthesia at the microvascular level remains poorly defined, limiting the translational relevance of preclinical findings.AimWe aim to utilize an optical resolution photoacoustic microscopy (OR-PAM) system for targeted photothrombosis (PT) induction and functional imaging and compare ischemic responses between awake and anesthetized mice to avoid anesthesia-induced confounding effects.ApproachWe employed an OR-PAM system integrated with vascular stimulation capabilities, enabling PT induction in specific vessels and functional imaging of cortical brain regions. By combining with a head-restrained apparatus and photochemical thrombosis, we compared oxygen saturation (sO2), oxygen extraction fraction (OEF), and vascular morphology [arteriovenous diameter, total vessel length (TVL), branch index (BI), vessel area density (VAD)] between awake and anesthetized mice. Behavioral tests and pathological examination were conducted to validate cerebrovascular outcomes.ResultsBefore PT, awake mice exhibited narrower arteriovenous diameter and higher OEF compared with anesthetized mice induced by anesthesia. Following PT, awake mice displayed more severe acute sO2 reduction, greater chronic vascular structure degeneration, and more delayed recovery among TVL, BI, and VAD, compared with anesthetized mice. These cerebrovascular outcomes of photothrombotic stroke in anesthetic neuroprotection-free awake mice were further validated by behavioral tests and pathological examination.ConclusionsOur findings address the critical gap of microvascular-level analysis in anesthesia-confounded stroke models. The proposed approach provides a reliable and reproducible tool for the precise induction of cerebral ischemia and dynamic monitoring of vascular changes.

Purpose: This study aimed to develop a nomogram to predict the preoperative diagnostic probabilities of central lymphoma and glioma, as well as glioblastoma versus non-glioblastoma. Patients and methods: Retrospective analysis was performed on patients with central nervous system lymphoma or glioma who received treatment at our department, between 2016 and 2025. From 2016 to 2024, Eligible patients were randomly assigned to training and validation sets in a 7:3 ratio. Patients at our department from 2024 to 2025 ( n = 104) and two other medical centers (External Center1: n = 95, External Center2: n = 123) will be included as prospective external validation cohorts. Key variables for nomogram construction were identified through the integration of least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. To assess the performance of the nomogram, seven machine learning models were constructed, including logistic regression, decision tree, random forest, support vector machine (SVM), neural network, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (lightGBM), which were then evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis. The validation set was used for further model evaluation. Results: This retrospective study included 712 cases: 127 patients with newly diagnosed primary central nervous system lymphoma (PCNSL) and 586 patients with newly diagnosed glioma. In the diagnostic model for PCNSL versus glioma, the following five risk factors were included: age, Karnofsky Performance Status (KPS), neutrophil count (NEUT), neutrophil ratio (NEUT1), and monocyte count (MONO). The area under the curve (AUC) for the seven models ranged from 0.784 to 0.889, and the optimal AUC values obtained from the external validation sets at our center (2024-2025) and two other medical centers were 0.877, 0.716, and 0.743, respectively. In the diagnostic model for glioblastoma versus non-glioblastoma, three risk factors were included: age, neutrophil ratio (NEUT1), and monocyte count (MONO). The AUC for the seven models ranged from 0.778 to 0.857, while the optimal AUC values obtained from the external validation sets at our center (2024-2025) and two other medical centers were 0.861, 0.842, and 0.710. Conclusion: This study developed and validated diagnostic probability models for central lymphoma versus glioma, and glioblastoma versus non-glioblastoma. These models may assist clinicians in determining the type of central malignant tumor affecting patients, thereby facilitating the development of more personalized and optimized treatment strategies.