Haoran Wang

The intratumoral microbiome influences cancer progression, but its role in meningiomas is unknown. We characterized the meningioma intratumoral microbiome and investigated its functional significance. 16 S rRNA sequencing was performed on 80 meningioma specimens (44 benign, 36 non-benign). Microbial findings were validated via fluorescence in situ hybridization (FISH) and transmission electron microscopy. Among differentially abundant genera, Lactobacillus was identified as the core genus due to its 100% detection rate and significant enrichment in benign tumors, and was thus selected for functional interrogation. We employed in vitro assays (proliferation, migration), non-targeted metabolomics, RNA sequencing, and co-culture systems with primary human macrophages derived from peripheral blood mononuclear cells (PBMCs) and THP-1 cells to dissect microbial-metabolite-immune interactions. Meningiomas harbor a distinct intratumoral microbiome dominated by Firmicutes and Bacteroidota, with beta diversity revealing significant compositional differences between benign and non-benign tumors. The Lactobacillus genus was identified as a core genus enriched in benign tumors, and its abundance negatively correlated with the Ki-67 proliferation index. Logistic regression analysis further indicated an association between Lactobacillus abundance and low-aggressiveness tumor phenotypes (benign histology and low Ki-67 index); however, this association was not independent after adjusting for key clinical confounders. Functionally, the supernatant from cultured Lactobacillus intestinalis (putative L. intestinalis, a species belonging to the Lactobacillus genus that showed a differential abundance pattern between benign and non-benign meningiomas) suppressed IOMM-Lee cell proliferation and migration. Non-targeted metabolomics identified indole-3-lactic acid (ILA) and niacin as the predominant metabolites in the bacterial supernatant. ILA was established as the primary effector, recapitulating the direct antitumor effects and skewing human macrophages towards an M1-polarized phenotype with enhanced secretion of IL-12 and IL-6. While niacin also promoted M1 cytokine secretion, it lacked direct antiproliferative activity. Transcriptomic profiling of ILA-treated tumor cells confirmed the suppression of multiple oncogenic pathways. This study characterizes the intratumoral microbiome of meningiomas and reveals that the Lactobacillus genus may serve as a potential microbial marker for a low-proliferation, low-necrosis tumor microenvironment. Furthermore, we uncover a tumor-suppressive mechanism mediated by Lactobacillus, identifying its metabolite, indole-3-lactic acid (ILA), as a key effector. As an exogenous compound, ILA exerts dual anti-tumor activity through direct anti-proliferative effects and immunomodulation in vitro experiments. While these findings are preliminary and require further validation in vivo, they suggest ILA as a potential preclinical candidate for informing future therapeutic strategies.

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.