Online calculator to predict early mortality in patient with surgically treated recurrent lower-grade glioma.

PURPOSE: The aim of this study was to investigate the epidemiological characteristics and associated risk factors of recurrent lower-grade glioma [LGG] (WHO grades II and III) according to the 2016 updated WHO classification paradigm and finally develop a model for predicting early mortality (succumb within a year after reoperation) in recurrent LGG patients. METHODS: Data were obtained from consecutive patients who underwent surgery for primary LGG and reoperation for tumor recurrence. The end point "early mortality" was defined as death within 1 year after the reoperation. Predictive factors, including basic clinical characteristics and laboratory data, were retrospectively collected. RESULTS: A final nomogram was generated for surgically treated recurrent LGG. Factors that increased the probability of early mortality included older age (P = 0.042), D-dimer> 0.187 (P = 0.007), RDW > 13.4 (P = 0.048), PLR > 100.749 (P = 0.014), NLR > 1.815 (P = 0.047), 1p19q intact (P = 0.019), IDH1-R132H Mutant (P = 0.048), Fib≤2.80 (P = 0.018), lack of Stupp concurrent chemoradiotherapy (P = 0.041), and an initial symptom of epilepsy (P = 0.047). The calibration curve between the prediction from this model and the actual observations showed good agreement. CONCLUSION: A nomogram that predicts individualized probabilities of early mortality for surgically treated recurrent LGG patients could be a practical clinical tool for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free online software implementing this nomogram is provided at https://warrenwrl.shinyapps.io/RecurrenceGliomaEarlyM/.

RIZ1 and histone methylation status in pituitary adenomas.

RIZ1 displays strong tumor-suppressive activities, which has a potential histone methyltransferase activity. The objective of the study was to evaluate the level and the methylation status of RIZ1 and analyze its association with clinicopathological features and the histone in the pituitary adenomas. We found that RIZ1-positive cases were 11/50 and H-Scores 22.75 ± 11.83 in invasive pituitary adenomas and 26/53 and 66.3 ± 21.7 in non-invasive pituitary adenomas (χ2 = 8.182, p = 0.004). RIZ1 and C-myc showed the opposite trend in these cases. The methylation levels of RIZ1 were more than 50% in 30.4% (7/23) CpG sites through MALDI-TOF Mass array. There was significant difference (p < 0.01) in 4 CpG sites between invasive pituitary adenoma group and non-invasive pituitary adenoma group; furthermore, the relieved methylation levels of H3K4/H3K9 and enhanced methylation levels of H3K27 in the patients' serum were found. Furthermore, there was statistic difference of H3K4 and H3K27 methylation between invasive pituitary adenoma and non-invasive pituitary adenoma group (p < 0.01). The average progression-free survival in high RIZ1 group was 52.63 ± 7.62 months and 26.06 ± 4.23 months in low RIZ1 group (p < 0.05). Promoter region methylation of RIZ1 may play an important role in the epigenetic silencing of RIZ1 expression in pituitary adenomas, which may translate into important diagnostic and therapeutic applications.

The new oncogene transmembrane protein 60 is a potential therapeutic target in glioma.

Glioma is a malignant tumor with a high fatality rate, originating in the central nervous system. Even after standard treatment, the prognosis remains unsatisfactory, probably due to the lack of effective therapeutic targets. The family of transmembrane proteins (TMEM) is a large family of genes that encode proteins closely related to the malicious behavior of tumors. Thus, it is necessary to explore the molecular and clinical characteristics of newly identified oncogenes, such as transmembrane protein 60 (TMEM60), to develop effective treating options for glioma. We used bioinformatic methods and basic experiments to verify the expression of transmembrane protein 60 in gliomas and its relationship with 1p and 19q (1p19q) status, isocitrate dehydrogenase (IDH) status, patient prognosis, and immune cell infiltration using public databases and clinical samples. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to detect co-expressed genes. Thus, we inhibited the expression of transmembrane protein 60 to observe the proliferation and activity of glioma LN229 cells. We found transmembrane protein 60 was significantly upregulated in glioma compared with that in normal brain tissue at the mRNA. In the subgroups of World Health Organization high grade, isocitrate dehydrogenase wildtype, 1p and 19q non-codeletion, or isocitrate dehydrogenase wild combined with 1p and 19q non-codeletion, the expression of transmembrane protein 60 increased, and the prognosis of glioma patients worsened. In the transmembrane protein 60 high expression group, infiltration of immune cells and stromal cells in the tumor microenvironment increased, tumor purity decreased, and immune cells and pathways were activated. The immune cells mainly included regulatory T-cell, gamma delta T-cell, macrophages M0, neutrophils, and CD8+ T-cells. Overexpression of co-inhibitory receptors (CTLA4, PDL1 and CD96) may promote the increase of depletion of T-cell, thus losing the anti-tumor function in the transmembrane protein 60 high expression group. Finally, we found that transmembrane protein 60 silencing weakened the viability, proliferation, and colony formation of glioma LN229 cells. This is the 0 report on the abnormally high expression of transmembrane protein 60 in glioma and its related clinical features, such as tumor microenvironment, immune response, tumor heterogeneity, and patient prognosis. We also found that transmembrane protein 60 silencing weakened the proliferation and colony formation of glioma LN229 cells. Thus, the new oncogene transmembrane protein 60 might be an effective therapeutic target for the clinical treatment of glioma.

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.

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.

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.

Behavior-Oriented Nomogram for the Stratification of Lower-Grade Gliomas to Improve Individualized Treatment.

Secondary glioblastomas (sGBM) are derived from previously lower-grade [World Health Organization (WHO) grades II or III] gliomas. Lower-grade benign-behaving gliomas may retain their former grade following recurrence, or may become malignant higher-grade glioblastomas. Prediction of tumor behavior in lower-grade gliomas is critical for individualized glioma therapy. A total of 89 patients were included between January 2000 and January 2019 in the present study to establish a nomogram via univariate and multivariate logistic regression analyses. Nomogram predictive performance was tested in the validation group. We then analyzed 36 O-6-methylguanine-DNA methyltransferase (MGMT) unmethylated lower-grade gliomas from patients seen at West China Hospital of Sichuan University. Survival statistics were calculated with the Kaplan-Meier method. Two clinical factors (molecular diagnosis and WHO grade), five radiological factors (location, cortical involvement, multicentricity, uniformity, and margin enhancement), one biomarker (1p19q codeletion), and a combination of three biomarkers (IDH+/ATRX-/TP53-) were associated with glioma upgrading. Nomograms positive for these prognostic factors had an AUC of 0.880 in the derivation group and 0.857 in the validation group. The calibration and score-stratified survival curves for the derivation group and validation group were good. An operational nomogram was published at https://warrenwrl.shinyapps.io/DynNomapp/. The overall survival of secondary gliomas in the MGMT-unmethylated cohort were influenced independently by the use of temozolomide during the treatment of formerly low-grade gliomas (p=0.00096). Clinical and radiological factors and biomarker-based behavior-oriented nomograms may offer a feasible identification tool for the detection of sGBM precursors. This method may further assist neurosurgeons with the stratification of lower-grade glioma cases and thus the development of better, more individualized treatment plans.

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.