Background: Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma‐associated macrophages and analyse their correlation with clinical features and immune infiltration. Methods: We analysed single‐cell RNA sequencing (scRNA‐seq) data from two paired meningioma and normal meninges to identify meningioma‐associated macrophages. High‐dimensional weighted gene co‐expression network analysis (hdWGCNA) was employed to identify module genes linked to these macrophages, followed by functional enrichment and pseudotime trajectory analyses. A machine learning‐based model using the module genes was developed to predict tumour grades. Finally, meningiomas were classified into two molecular subtypes based on the module genes, followed by a comparison of clinical characteristics and immune cell infiltration. Results: Meningiomas exhibited a significantly higher proportion of macrophages than normal meninges, including novel macrophage clusters referred to as meningioma‐associated macrophages. The hdWGCNA analysis of macrophages within meningiomas unveiled 12 distinct modules, with the blue, black, and turquoise modules closely correlated with the meningioma‐associated macrophages. Hub genes within these modules were enriched in immune regulation, cellular communication, and metabolism pathways. Machine learning analysis identified 13 module genes (RSBN1, TIPRL, ATIC, SPP1, MALSU1, CDK1, MGP, DDIT3, SUPT16H, NFKBIA, SRSF5, ATXN2L, and UBB) strongly correlated with meningioma grade and constructed a predictive model with high accuracy and robustness. Based on the module genes, meningiomas were classified into two subtypes with distinct clinical and tumour microenvironment characteristics. Conclusions: Our findings provide insights into the molecular characteristics underlying macrophage infiltration in meningiomas. The molecular signatures of macrophages demonstrate correlations with clinical features and immune cell infiltration in meningiomas.

Extensive research has demonstrated the critical role of selenium (Se) and selenoproteins in brain function and cognition. However, the impact of Se on brain cortical structure remains enigmatic. Therefore, this study used Mendelian randomization (MR) analysis to investigate the causal effect between Se levels and brain cortical structure.Methods: This study utilizes 11 genetic variants associated with Se level variations, extracted from a large‐scale genome‐wide association study (GWAS) encompassed circulating Se levels (n = 5477) and toenail Se levels (n = 4162) in the European population. Outcome data were sourced from the summary statistics of the ENIGMA Consortium, comprising GWAS data from 51,666 individuals. The variables include cortical surface area (SA), thickness (TH) at the global level, and 34 functional cortical regions evaluated by magnetic resonance imaging. The inverse‐variance‐weighted method was used as the primary estimate. Additionally, sensitivity analyses were conducted to detect potential violations of assumptions underlying MR.Results: At the global level, Se levels were not correlated with SA but showed a significant negative correlation with TH (β = −0.00485 mm, SE = 0.00192, p = .0115). Heterogeneity was observed across different brain regions, with positive correlations found between Se levels and the TH of the parahippocampal gyrus, superior frontal gyrus, and frontal pole, whereas negative correlations were found with the TH of the inferior parietal lobe and middle temporal lobe. Regarding SA, Se levels exhibit positive correlations with the pars triangularis, caudal anterior cingulate, inferior parietal lobe, and banks of the superior temporal sulcus. Conversely, negative correlations were observed with the medial orbitofrontal cortex, posterior cingulate gyrus, insula, and the middle, superior, and transverse gyrus of the temporal lobe. No pleiotropy was detected.Results: This MR study indicated that Se levels causally influence the brain cortical structure.

Background The impact of chemotherapy on cardiocerebrovascular disease (CCVD) outcomes in patients with colorectal cancer (CRC) remains insufficiently explored. This research aimed to clarify the association between chemotherapy and CCVD-related mortality in middle-aged and elderly CRC patients. Methods CRC patients aged ≥ 50 years at diagnosis were identified from the Surveillance, Epidemiology, and End Results (SEER) database for the period 2010–2015. The primary outcome was CCVD-related mortality. Survival analyses included Kaplan-Meier (KM) curves to characterize CCVD-related survival patterns and multivariate competing risks regression analysis to evaluate the independent association between chemotherapy and CCVD-related mortality. Subgroup analyses and Fine-Gray competing risks models were additionally performed to enhance the robustness of study findings. Results A total of 136 095 CRC patients were included. KM analysis showed a significantly lower cumulative risk of CCVD-related mortality in chemotherapy treatment group than in non-chemotherapy treatment group. After adjusting for age, race, gender, primary tumor site, and tumor stage, multivariate competing risks regression analysis confirmed that chemotherapy was an independent protective factor against CCVD-related mortality (sHR = 0.75, 95% CI 0.71–0.79, P < 0.001). Subgroup analyses and Fine-Gray competing risks models further validated the robustness of chemotherapy’s beneficial effect on CCVD outcomes. Conclusion Chemotherapy may serve as an independent protective factor against CCVD-related mortality in CRC patients. These findings can help alleviate concerns regarding chemotherapy-associated CCVD risk in CRC management and provide novel insights for clinical decision-making and future research.

Background: Gliomas exhibit substantial intratumoral heterogeneity, which limits prognostic precision and therapeutic efficacy. Selenoproteins are key regulators of redox homeostasis, but their role in glioma progression remains insufficiently defined. This study aimed to characterize glioma cells with high selenoprotein activity and to determine their biological and clinical significance. Methods: We performed integrated multi-omic analyses combining bulk transcriptomic, single-cell transcriptomic, and spatial transcriptomic data to identify and characterize glioma cell states associated with elevated selenoprotein expression. Functional validation was conducted using SELENOS knockdown assays to evaluate effects on glioma proliferation, invasion, tumor growth, macrophage recruitment, CSF1 expression, and macrophage polarization. Results: We identified a malignant glioma cell state, termed SehighMali, characterized by elevated selenoprotein expression and distinct metabolic and immunological features. SehighMali cells showed enhanced oxidative phosphorylation, MYC-associated transcription, and DNA repair activity, and preferentially engaged in immunosuppressive crosstalk with myeloid cells through the CSF1–CSF1R axis. Spatial analyses demonstrated enrichment of SehighMali cells in tumor cores and close colocalization with immunosuppressive myeloid populations. Across bulk cohorts, higher SehighMali abundance was associated with aggressive molecular features, poor clinical outcomes, and a predicted temozolomide-resistant phenotype. SELENOS knockdown suppressed glioma proliferation, invasion, and tumor growth, reduced macrophage recruitment, decreased CSF1 expression, and promoted macrophage polarization toward a pro-inflammatory phenotype. Conclusions: These findings define a selenoprotein-driven malignant glioma state associated with immune evasion and therapeutic vulnerability. They further identify SELENOS as a potential therapeutic target and provide insight into how selenoprotein-related programs contribute to glioma progression.