Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.

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.

Summary Aims In this study, we examined the expression of lncRNA ENST00000413528 in glioma and determined its role in glioma development. Methods LncRNA ENST00000413528 was detected in glioma tissues by lncRNA microarray. Then, we performed real‐time PCR, CCK‐8, colony formation assay, flow cytometry, caspase‐3/7 assay and animal experiment to detect the function of ENST00000413528 in glioma after ENST00000413528 knockdown. Subsequent bioinformatics analysis, luciferase reporter assays and RNA immunoprecipitation (RIP) assay western blotting indicated possible downstream regulatory molecules. The expression of PLK1 in glioma tissues was also examined by immunohistochemistry staining. Results Expression of ENST00000413528 was significantly increased in glioma tissues and LN229 and U251 cells. PLK1 protein could not be detected in peritumoral brain edema (PTBE) tissues; however, it showed an increasing number of positively cytoplasmic stained from WHO‐Grade II to Grade III gliomas. Knockdown of ENST00000413528 in glioma cells inhibited cell proliferation and colony formation abilities, induced the G0/G1 arrest of the cell cycle, and promoted apoptosis. The dual reporter assay and RNA immunoprecipitation assay verified the interaction between ENST00000413528 and miR‐593. We also demonstrated that polo‐like kinase 1 (PLK1) was regulated by miR‐593; PLK1 messenger RNA lacking 3’UTR partially reversed the effects caused by ENST00000413528 knockdown or miR‐593 upregulation. Conclusion lncRNA ENST00000413528 is closely related to the development of glioma via the miR‐593‐5p/PLK1 pathway.

BackgroundGlioblastoma 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 MethodsA 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.ResultsThe 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.ConclusionsThe HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.

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/