Zinc finger Protein207 orchestrates glioma migration through regulation of epithelial-mesenchymal transition.

BACKGROUND: Glioma represents the predominant primary malignant brain tumor. For several years, molecular profiling has been instrumental in the management and therapeutic stratification of glioma, providing a deeper understanding of its biological complexity. Accumulating evidence unveils the putative involvement of zinc finger proteins (ZNFs) in cancer. This study aimed to elucidate the role and significance of ZNF207 in glioma. METHODS: Utilizing online data such as The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), the Genotype-Tissue Expression (GTEx) project, the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA) databases, in conjunction with bioinformatics methodologies including GO, KEGG, GSEA, CIBERSORT immune cell infiltration estimation, and protein-protein interaction (PPI) analysis, enabled a comprehensive exploration of ZNF207's involvement in gliomagenesis. Immunohistochemistry and RT-PCR techniques were employed to validate the expression level of ZNF207 in glioma samples. Subsequently, the biological effects of ZNF207 on glioma cells were explored through in vitro assays. RESULTS: Our results demonstrate elevated expression of ZNF207 in gliomas, correlating with unfavorable patient outcomes. Stratification analyses were used to delineate the prognostic efficacy of ZNF207 in glioma with different clinicopathological characteristics. Immunocorrelation analysis revealed a significant association between ZNF207 expression and the infiltration levels of T helper cells, macrophages, and natural killer (NK) cells. Utilizing ZNF207 expression and clinical features, we constructed an OS prediction model and displayed well discrimination with a C-index of 0.861. Moreover, the strategic silencing of ZNF207 attenuated glioma cell advancement, evidenced by diminished cellular proliferation, weakened cell tumorigenesis, augmented apoptotic activity, and curtailed migratory capacity alongside the inhibition of the epithelial-mesenchymal transition (EMT) pathway. CONCLUSIONS: ZNF207 may identify as a prospective biomarker and therapeutic candidate for glioma prevention, providing valuable insights into understanding glioma pathogenesis and treatment strategies.

Incidence, prevalence and characteristics of multimorbidity in different age groups among urban hospitalized patients in China.

The aim of the study was to investigate the incidence, prevalence and characteristics of multimorbidity in urban inpatients of different age groups. This study used data from the National Insurance Claim for Epidemiology Research (NICER) to calculate the overall incidence, prevalence, geographic and age distribution patterns, health care burden, and multimorbidity patterns for multimorbidity in 2017. According to our study, the overall prevalence of multimorbidity was 6.68%, and the overall prevalence was 14.87% in 2017. The prevalence of multimorbidity increases with age. The pattern of the geographic distribution of multimorbidity shows that the prevalence of multimorbidity is relatively high in South East China. The average annual health care expenditure of patients with multimorbidity increased with age and rose rapidly, especially among older patients. Patients with cancer and chronic kidney disease have higher treatment costs. Patients with hypertension or ischemic heart disease had a significantly higher relative risk of multimorbidity than other included noncommunicable diseases (NCDs). Hyperlipidemia has generated the highest number of association rules, which may suggest that hyperlipidemia may be both a risk factor for other NCDs and an outcome of them.

Development and Validation of a Clinical Prediction Model for Venous Thromboembolism Following Neurosurgery: A 6-Year, Multicenter, Retrospective and Prospective Diagnostic Cohort Study.

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.

Efficacy and safety of prophylaxis for venous thromboembolism in brain neoplasm patients undergoing neurosurgery: a systematic review and Bayesian network meta-analysis.

Neurosurgeons often face this dilemma. Brain neoplasm patients undergoing neurosurgery are at a high risk of venous thrombosis. However, antithrombotic drugs may induce bleeding complications. Therefore, we compared the efficacy and safety of prophylaxis for venous thromboembolism (VTE) in brain neoplasm patients undergoing neurosurgery. We searched Cochrane Central Register of Controlled Trials, Ovid MEDLINE(R), and Embase from inception to January 2022 for randomized controlled trials (RCTs) comparing the prophylactic measures efficacy and safety for VTE in brain neoplasm patients undergoing neurosurgery. The main efficacy outcome was symptomatic or asymptomatic VTE. The safety outcomes included major bleeding, minor bleeding, all occurrences of bleeding, and all-cause mortality. We used (Log) odds ratio (OR) of various chemoprophylaxis regimens to judge the safety and effectiveness of VTE. Additionally, all types of intervention were ranked by the Surface Under the Cumulative Ranking (SUCRA) value. We included 10 RCTs with 1128 brain neoplasm patients undergoing neurosurgery. For symptomatic or asymptomatic VTE and proximal DVT or PE, DOACs, compared with placebo, can significantly reduce the events. DOACs were superior to all other interventions in the rank plot of these events. For major bleeding reduction, unfractionated heparin (SUCRA value = 0.21) demonstrated better safety efficacy than others. For minor bleeding reduction, DOACs had a significantly higher risk of minor bleeding compared with placebo [Log OR 16.76, 95% CrI (1.53, 61.13)], LMWH [Log OR 15.68, 95% CrI (0.26, 60.10)] and UFH [Log OR 15.93, 95% CrI (0.22, 60.16)] respectively. Except for placebo (SUCRA values of 0.13), UFH (SUCRA values of 0.37) depicted better safety efficacy than others. For all-cause mortality, we found UFH always had significantly lower all-cause mortality compared with low-molecular-weight heparin (LMWH) [Log OR = 14.17, 95% CrI (0.05, 48.35)]. UFH plus intermittent pneumatic compression (IPC) (SUCRA value of 0.12) displayed the best safety for all-cause mortality. In our study, DOACs were more effective as prophylaxis for VTE in brain neoplasm patients undergoing neurosurgery. Regarding the safety of prophylaxis for VTE, UFH of chemoprophylaxis consistently demonstrated better safety efficacy, involving either major bleeding, minor bleeding, bleeding, or all-cause mortality.

A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment.

OBJECTIVE: To construct and validate a radiomics nomogram for preoperative prediction of survival stratification in glioblastoma (GBM) patients with standard treatment according to radiomics features extracted from multiparameter magnetic resonance imaging (MRI), which could facilitate clinical decision-making. METHODS: A total of 125 eligible GBM patients (53 in the short and 72 in the long survival group, separated by an overall survival of 12 months) were randomly divided into a training cohort (n = 87) and a validation cohort (n = 38). Radiomics features were extracted from the MRI of each patient. The T-test and the least absolute shrinkage and selection operator algorithm (LASSO) were used for feature selection. Next, three feature classifier models were established based on the selected features and evaluated by the area under curve (AUC). A radiomics score (Radscore) was then constructed by these features for each patient. Combined with clinical features, a radiomics nomogram was constructed with independent risk factors selected by the logistic regression model. The performance of the nomogram was assessed by AUC, calibration, discrimination, and clinical usefulness. RESULTS: There were 5,216 radiomics features extracted from each patient, and 5,060 of them were stable features judged by the intraclass correlation coefficients (ICCs). 21 features were included in the construction of the radiomics score. Of three feature classifier models, support vector machines (SVM) had the best classification effect. The radiomics nomogram was constructed in the training cohort and exhibited promising calibration and discrimination with AUCs of 0.877 and 0.919 in the training and validation cohorts, respectively. The favorable decision curve analysis (DCA) indicated the clinical usefulness of the radiomics nomogram. CONCLUSIONS: The presented radiomics nomogram, as a non-invasive tool, achieved satisfactory preoperative prediction of the individualized survival stratification of GBM patients.

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.

This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.

Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics.

OBJECTIVES: The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making. METHODS: A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination. RESULTS: A total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts. CONCLUSIONS: The presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.

Effects of Preoperative Carbamazepine Treatment on Microvascular Decompression for Classical Trigeminal Neuralgia.

BACKGROUND: Carbamazepine (CBZ) is the first-line therapy for trigeminal neuralgia (TN), and microvascular decompression (MVD) is considered to be an effective surgical treatment for TN. However, the effect of preoperative CBZ treatment on MVD outcome is not clear. METHODS: From 2013 to 2019, 63 patients with classical TN underwent MVD at the First Affiliated Hospital of Zhengzhou University, China. Data were collected through telephone follow-up and electronic medical records in April 2020. Short-term surgical outcome and long-term follow-up data were estimated by univariate and multivariate analysis. RESULTS: Multivariate analysis indicated that preoperative CBZ treatment was not a significant predictor for short-term outcomes of MVD (P > 0.05). Multivariate analysis for the long-term outcome of MVD indicated that preoperative CBZ treatment could predict postoperative recurrence of TN (P < 0.05). CONCLUSIONS: For patients with classical TN, a longer preoperative medication history of CBZ treatment had no significant effect on short-term outcome of MVD, but CBZ treatment was associated with a poor long-term outcome following MVD.

Preoperative Predictors of Early Mortality Risk in Isocitrate Dehydrogenase-Wild-Type Glioblastoma Patients Treated with Standard Therapy.

PURPOSE: Early identification of early mortality for glioblastoma (GBM) patients based on laboratory findings at the time of diagnosis could improve the overall survival. The study aimed to explore preoperative factors associated with higher risk of early death (within 1 year after surgery) for isocitrate dehydrogenase (IDH) -wild-type (wt) GBM patients. PATIENTS AND METHODS: We conducted a retrospective analysis of 194 IDH-wt GBM patients who underwent standard treatment. The probability of dying within 1 year after gross total resection (GTR) was defined as the end point "early mortality". Retrospective collection of predictive factors including clinical characteristics and laboratory data at diagnosis. RESULTS: Median follow-up time after GTR was 16 months (3-41 months). Forty-two patients died within 1 year after surgery (1-year mortality rate: 21.6%). All potential predictive factors were assessed on univariate analyses, which revealed the following factors as associated with higher risk of early death: older age (P = 0.013), occurrence of non-seizures symptoms (P = 0.042), special tumor positions (P = 0.046), higher neutrophil-to-lymphocyte ratio (NLR) (P = 0.015), higher red blood cell distribution width (RDW) (P = 0.019), higher lactate dehydrogenase (LDH) (P = 0.005), and higher fibrinogen (FIB) (P = 0.044). In a multivariate analysis, tumor location (P = 0.012), NLR (P = 0.032) and LDH (P = 0.002) were independent predictors of early mortality. The C-index of the nomogram was 0.795. The calibration curve showed good agreement between prediction by nomogram and actual observation. CONCLUSION: Tumor location, preoperative elevated NLR and serum LDH level were independent predictors for 1-year mortality after GTR. We indicate that increased preoperative NLR or LDH may guide patients to review head magnetic resonance imaging (MRI) more frequently and regularly to monitor tumor progression.

A Hematological-Related Prognostic Scoring System for Patients With Newly Diagnosed Glioblastoma.

BACKGROUND: Glioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma. PATIENTS AND METHODS: A total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors. RESULTS: The optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P < 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups. CONCLUSIONS: The HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.