Prognostic model for predicting overall survival in patients with glioblastoma: an analysis based on the SEER database.

Predicting the prognosis of glioblastoma (GBM) has always been important for improving survival. An understanding of the prognostic factors for patients with GBM can help guide treatment. Herein, we aimed to construct a prognostic model for predicting overall survival (OS) for patients with GBM. We identified 11,375 patients with pathologically confirmed GBM from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. The 1-, 2-, and 3-year survival probabilities were 48.8%, 22.5%, and 13.1%, respectively. The patients were randomly divided into the training cohort (n = 8531) and the validation cohort (n = 2844). A Cox proportional risk regression model was used to analyze the prognostic factors of patients in the training cohort, and a nomogram was constructed. Then concordance indexes (C-indexes), calibration curves, and receiver operating characteristic (ROC) curves were used to assess the performance of the nomograms by internal (training cohort) and external validation (validation cohort). Log-rank test and univariate analysis showed that age, race, marital status, extent of surgical resection, chemotherapy, and radiation were the prognostic factors for patients with GBM (p < 0.05), which were used to construct nomogram. The C-index of the nomogram was 0.717 (95% confidence interval (CI), 0.710-0.724) in the training cohort, and 0.724 (95% CI, 0.713-0.735) in the validation cohort. The nomogram had a higher areas under the ROC curve value. The nomogram was well validated, which can effectively predict the OS of patients with GBM. Thus, this nomogram could be applied in clinical practice.

High cerebrospinal fluid lactate concentration at 48 h of hospital admission predicts poor outcomes in patients with tuberculous meningitis: A multicenter retrospective cohort study.

Objective: This study aimed to analyze the cerebrospinal fluid (CSF) parameters affecting the outcomes of patients with tuberculous meningitis (TBM). Methods: This is a multi-center, retrospective, cohort study involving 81 patients who were diagnosed with TBM and treated in Haihe Clinical College of Tianjin Medical University, Tianjin Medical University General Hospital, and General Hospital of Air Force PLA from January 2016 to December 2019. Baseline data, Glasgow Coma Scale (GCS) score, and clinical presentations of all patients were collected at admission. CSF samples were collected at 48 h, 1, 2, and 3 weeks after admission. CSF lactate, adenosine deaminase, chloride, protein, glucose levels and intracranial pressure were measured. After a follow-up of 16.14 ± 3.03 months, all patients were assessed using the modified Rankin Scale (mRS) and divided into good (mRS scores of 0-2 points) and poor outcome groups (mRS scores of 3-6 points). The differences in patients' baseline data, GCS score, clinical presentations, and levels of CSF parameters detected at 48 h, 1, 2, and 3 weeks after admission between two groups were compared. Statistically significant variables were added to the binary logistic regression model to identify the factors impacting the outcomes of patients with TBM. Receiver operating characteristic (ROC) curve was used to assess the predictive ability of the model. Results: The CSF lactate level exhibited a decreasing trend within 3 weeks of admission in the two groups. For the within-group comparison, statistically significant differences in the lactate level was found in both groups between four different time points. A binary logistic regression model revealed that CSF lactate level at 48 h after admission, age, and GSC score on admission were independently associated with the outcomes of patients with TBM. ROC curve analysis showed that the area under the ROC curve (AUC) was 0.786 for the CSF lactate level (48 h), 0.814 for GCS score, and 0.764 for age. Conclusion: High CSF lactate level at 48 h after admission is one of the important factors for poor outcomes in patients with TBM.

MicroRNA-151 regulates the growth, drug sensitivity and epithelial mesenchymal transition of human glioma cells by targeting profilin 2.

The microRNA-151 (miR-151) has been reported to be involved in the growth, development, and tumorigenesis of different types of human cancers. This study was designed to unravel the role and therapeutic potential of miR-151 in glioma. The results showed glioma was found to be associated with significant (P<0.05) downregulation of miR-151. Low expression of miR-151 was also associated with poor survival of the glioma patients. Overexpression of miR-151 resulted in a significant (P<0.05) decline of glioma cell proliferation and colony formation. The sensitivity of the glioma cells to adriamycin also increased significantly (P<0.05) upon miR-151 overexpression. Additionally, overexpression of miR-151 also suppressed the migration and invasion of the human glioma cells. This was also associated with alteration in the expression of epithelial mesenchymal transition proteins. The expression of E-cadherin was increased while as that of N-cadherin, vimentin, and Snail was considerably decreased upon miR-151 overexpression. Bioinformatic analysis and ducal luciferase assay showed miR-151 targets profilin 2 (PFN2) in human glioma cells. The expression of PFN2 was found to be significantly (P<0.05) upregulated in human glioma tissues cells and cell lines. Nonetheless, the PFN2 expression was considerably suppressed upon miR-151 overexpression. Knockdown of PFN2 resulted in decrease of glioma cells proliferation. In contrary, overexpression of PFN2 could avoid the tumor-suppressive effects of miR-151. Taken together, present study points towards the tumor-suppressive effects of miR-151 and prospective therapeutic implications in human glioma.