Background: Based on the literature and data on its clinical trials, the incidence of venous thromboembolism (VTE) in patients undergoing neurosurgery has been 3.0%~26%. We used advanced machine learning techniques and statistical methods to provide a clinical prediction model for VTE after neurosurgery. Methods: All patients (n = 5867) who underwent neurosurgery from the development and retrospective internal validation cohorts were obtained from May 2017 to April 2022 at the Department of Neurosurgery at the Sanbo Brain Hospital. The clinical and biomarker variables were divided into pre-, intra-, and postoperative. A univariate logistic regression (LR) was applied to explore the 67 candidate predictors with VTE. We used a multivariable logistic regression (MLR) to select all significant MLR variables of MLR to build the clinical risk prediction model. We used a random forest to calculate the importance of significant variables of MLR. In addition, we conducted prospective internal (n = 490) and external validation (n = 2301) for the model. Results: Eight variables were selected for inclusion in the final clinical prediction model: D-dimer before surgery, activated partial thromboplastin time before neurosurgery, age, craniopharyngioma, duration of operation, disturbance of consciousness on the second day after surgery and high dose of mannitol, and highest D-dimer within 72 h after surgery. The area under the curve (AUC) values for the development, retrospective internal validation, and prospective internal validation cohorts were 0.78, 0.77, and 0.79, respectively. The external validation set had the highest AUC value of 0.85. Conclusions: This validated clinical prediction model, including eight clinical factors and biomarkers, predicted the risk of VTE following neurosurgery. Looking forward to further research exploring the standardization of clinical decision-making for primary VTE prevention based on this model.

Backgrounds: Adamantinomatous craniopharyngiomas (ACPs) are benign intracranial tumors that behave aggressively due to their location, infiltration of the surrounding nervous tissue and high capacity for recurrence. In this study, we aimed to construct ACP primary cell models for further investigation of tumorigenic and recurrent mechanisms. Methods: Primary cells were isolated from primary (one case) and recurrent (one case) ACP. Short tandem repeat (STR) analysis was used to clarify the identity of the ACP primary cells we isolated. Whole exome sequencing (WES), immunofluorescence (IF) and immunohistochemistry (IHC) were performed on primary cells and corresponding ACP tissues, to determine the mutational profile and to clarify the tissue origin and phenotypic of primary cells. Transcriptome RNA-seq was performed to obtain the gene expression characteristics of ACP primary cells. Subsequently, a heterotopic ACP xenograft mouse model was established to confirm the tumorigenesis capacity of ACP primary cells. Results: ACP primary cells were successfully cultured. The genetic variants were similar to the original tumor tissue, and they owned expression of cancer-associated fibroblast (CAF) markers (FSP1/S100A4, Vimentin) and nuclear translocation β-catenin. Meanwhile, they had an high level expression of extracellular matrix components (Fibronectin). The tumor formation ability of ACP primary cells was verified. The transcriptional signatures of ACP primary cells were also explored. Conclusions: We successfully isolated and characterized ACP primary cells that acquired multiple CAF features and demonstrated stable propagation through dozens of passages. These PDC models laid the foundation for further research on ACP.

Background: Glioblastoma is highly heterogeneous, highly invasive, and resistant to temozolomide, with an extremely low 5-year survival rate. The spatial association between polyamine metabolism and ferroptosis remains unclear. Spatial metabolic heterogeneity is a core challenge in glioblastoma treatment. Methods: In this study, 17 patients with primary or recurrent high-grade glioma were included. Spatial metabolomics (n = 9), multiplex immunofluorescence (exploratory experiment n = 10, observational experiment n = 1), flow cytometry (n = 13), and mouse orthotopic models (n = 3) were combined to analyze the characteristics of the polyamine–ferroptosis axis in the para-carcinoma, tumor, and necrotic regions. Results: Differences in the polyamine-metabolism–ferroptosis axis were observed across distinct pathological regions of glioblastoma. Regional differences in polyamine distribution were associated with regional differences that lower lipid-peroxidation signals in the tumor region and higher lipid-peroxidation signals in the necrotic region. Moreover, in glioblastoma patient specimens, lipid-peroxidation-associated signals were detected in immune-cell compartments, alongside immune-cell phenotypic features suggestive of dysfunction, which show correlation with an immunosuppressive microenvironment. At the regional level, enzymes related to polyamine metabolism, namely GLS and SMS, exhibited spatial co-localization trends with lipid-peroxidation markers. Alterations in markers of the GSH-GPX4 and FSP1 antioxidant systems were spatially associated with the tumor-region-specific lipid-peroxidation profile. Partial spatial patterns of polyamine metabolism observed in human samples were partially recapitulated in mouse orthotopic tumor models. Conclusions: Glioblastoma exhibits spatially distinct distribution of the polyamine–ferroptosis-associated molecular signature. Notable correlative spatial features include spermidine and spermine enrichment, altered regional abundance within the glutamine–glutamate metabolic axis, immune-cell lipid-peroxidation-associated signals and immune-cell-dysfunction-related phenotypes, and regional shifts in GSH-GPX4/FSP1-related markers. This study identifies potential targets and provides a correlative spatial framework for region-specific therapeutic investigation, but the establishment of the causal relationship still requires further functional verification.