Risk Factors for Early Mortality Among Patients with Glioma: A Population-Based Study

Geriatric grade 2 and 3 gliomas: A national cancer database analysis of demographics, treatment utilization, and survival

With increasing life expectancies and population aging, the incidence of elderly patients with grade 2 and 3 gliomas is increasing. However, there is a paucity of knowledge on factors affecting their treatment selection and overall survival (OS). Geriatric patients aged between 60 and 89 years with histologically proven grade 2 and 3 intracranial gliomas were identified from the National Cancer Database between 2010 and 2017. We analyzed patients' demographic data, tumor characteristics, treatment modality, and outcomes. The Kaplan-Meier method was used to analyze OS. Univariate and multivariate analyses were performed to assess the predictive factors of mortality and treatment selection. A total of 6257 patients were identified: 3533 (56.3 %) hexagenerians, 2063 (32.9 %) septuagenarians, and 679 (10.8 %) octogenarians. We identified predictors of lower OS in patients, including demographic factors (older age, non-zero Charlson-Deyo score, non-Hispanic ethnicity), socioeconomic factors (low income, treatment at non-academic centers, government insurance), and tumor-specific factors (higher grade, astrocytoma histology, multifocality). Receiving surgery and chemotherapy were associated with a lower risk of mortality, whereas receiving radiotherapy was not associated with better OS. Our findings provide valuable insights into the complex interplay of demographic, socioeconomic, and tumor-specific factors that influence treatment selection and OS in geriatric grade 2 and 3 gliomas. We found that advancing age correlates with a decrease in OS and a reduced likelihood of undergoing surgery, chemotherapy, or radiotherapy. While receiving surgery and chemotherapy were associated with improved OS, radiotherapy did not exhibit a similar association.

Effects of coffee and tea consumption on glioma risk: An umbrella review of systematic reviews and meta-analyses

BACKGROUND

Coffee and tea are considered to have some effects on the risk of glioma as one of the most prevalent intracranial malignant tumors in adults. However, the precise effect of coffee and tea consumption on glioma is not obvious. This umbrella review aimed to evaluate the impact of tea and coffee consumption on glioma risk.

METHODS

Three online databases containing Scopus, Web of Science, and PubMed were thoroughly searched from the beginning to February 23, 2024 with no language constraints. Relying on I2 and Q statistics, a random-effect model or a fixed-effect model was applied. The PICO structure was followed as Population (Patients with glioma), Intervention (Coffee and tea consumption), Comparison (Standard treatment or placebo), and Outcome (Risk of glioma).

RESULTS

Totally, seven meta-analyses and systematic reviews contain 23,591 patients were included in this umbrella review. Coffee and tea consumption led to significant 15% and 16% reductions in glioma risk, respectively (RR = 0.85; 95% CI: 0.74, 0.98; RR = 0.84; 95% CI: 0.79, 0.89). The results did not change after subgroup analyses.

CONCLUSION

This umbrella review revealed that the coffee and tea consumption may decrease the glioma risk. Consumption of tea and coffee may be considered as dietary strategies against glioma.

PROSPERO REGISTRATION CODE

CRD42024521525.

Utility of Administrative Databases and Big Data on Understanding Glioma Treatment—A Systematic Review

Background Gliomas are a heterogeneous group of tumors where large multicenter clinical and genetic studies have become increasingly popular in their understanding. We reviewed and analyzed the findings from large databases in gliomas, seeking to understand clinically relevant information.

Methods A systematic review was performed for gliomas studied using large administrative databases up to January 2020 (e.g., National Inpatient Sample [NIS], National Surgical Quality Improvement Program [NSQIP], and Surveillance, Epidemiology, and End Results Program [SEER], National Cancer Database [NCDB], and others).

Results Out of 390 screened studies, 122 were analyzed. Studies included a wide range of gliomas including low- and high-grade gliomas. The SEER database (n = 83) was the most used database followed by NCDB (n = 28). The most common pathologies included glioblastoma multiforme (GBM) (n = 67), with the next category including mixes of grades II to IV glioma (n = 31). Common study themes involved evaluation of descriptive epidemiological trends, prognostic factors, comparison of different pathologies, and evaluation of outcome trends over time. Persistent health care disparities in patient outcomes were frequently seen depending on race, marital status, insurance status, hospital volume, and location, which did not change over time. Most studies showed improvement in survival because of advances in surgical and adjuvant treatments.

Conclusions This study helps summarize the use of clinical administrative databases in gliomas research, informing on socioeconomic issues, surgical outcomes, and adjuvant treatments over time on a national level. Large databases allow for some study questions that would not be possible with single institution data; however, limitations remain in data curation, analysis, and reporting methods.

Silencing long noncoding RNA LINC01138 inhibits aerobic glycolysis to reduce glioma cell proliferation by regulating the microRNA‑375/SP1 axis

Glioma is a primary cerebral neoplasm that originates from glial tissue and spreads to the central nervous system. Long noncoding RNAs are known to play a role in glioma cells by regulating cell proliferation, migration and invasion. The aim of the present study was to investigate the mechanism by which long intergenic non‑protein coding RNA (LINC) 01138 affects glycolysis and proliferation in glioma cells via the microRNA (miR)‑375/specificity protein 1 (SP1) axis. LINC01138 expression was assessed in glioma tissues and cells using reverse transcription‑quantitative PCR and the association between LINC01138 and patient clinicopathological features was analyzed. Glucose uptake, lactic acid secretion, cell proliferation, and glycolysis‑related enzyme levels were detected following LINC01138 silencing using CCK‑8, EDU assay and western blot analysis. miR‑375 and SP1 expression levels were also assessed, and the distribution of LINC01138 in the nucleus and cytoplasm was investigated using subcellular fractionation localization. Furthermore, the binding relationships between LINC01138 and miR‑375, and between miR‑375 and SP1 were assessed via dual‑luciferase experiment, RIP and RNA pull‑down assays. Finally, xenograft transplantation models were used to verify the in vitro results. LINC01138 was highly expressed in glioma, which was independent of patient sex or age but was significantly related to tumor diameter, the World Health Organization tumor grade and lymph node metastasis. Silencing LINC01138 significantly reduced glioma glycolysis and cell proliferation. Moreover, LINC01138 acted as a competing endogenous RNA to sponge miR‑375 and promote SP1 expression. miR‑375 inhibition significantly reversed the effect of LINC01138 silencing. In addition, silencing LINC01138 significantly reduced tumor growth in vivo. The present study demonstrated that silencing LINC01138 inhibited aerobic glycolysis and thus reduced glioma cell proliferation, potentially by modulating the miR‑375/SP1 axis.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.

Do elderly patients (≥ 75 years old) with glioblastoma benefit from more radical surgeries in the era of temozolomide?

This study assesses the effect of extent of resection (EOR) on the longer-term survival and early mortality of elderly patients (≥ 75 years old) with glioblastoma. We used the Surveillance, Epidemiology, and End Results (SEER) database and data from our center to evaluate the effect of EOR on the long-term survival and early mortality of patients with glioblastoma. We included 50 elderly patients (≥ 75 years old) with glioblastoma visiting our hospital. The median overall survival of the patients who underwent a gross total resection, a subtotal resection, and a partial resection were 278, 200, and 83 days, respectively. The multivariate analysis showed that gross total resection (HR: 0.100; 95% CI: 0.015-0.671, p < 0.001) and subtotal reresection (HR: 0.134, 95% CI: 0.022-0.831, p < 0.001) were independent predictors of favorable prognosis when compared with partial resection. The data extracted from the SEER database also indicated that EOR was an independent predictor of OS, CCS, and early mortality. The stratification analysis revealed that gross total resection was the best protective factor of OS, early mortality, and CCS. Radical resection may improve the OS and CCS of glioblastoma patients aged ≥ 75 years and decrease early mortality.

Pediatric Glioma Outcomes: Predictors of Early Mortality

OBJECTIVE

To assess the early mortality in pediatric glioma and identify predictors of early mortality, which may provide insight into the therapeutic strategies for children with a high risk of early mortality.

METHODS

We used SEER∗Stat 8.3.5 software to extract data of pediatric glioma from the Surveillance, Epidemiology, and End Results database. Logistical regression to identify the independent factors in predicting early mortality.

RESULTS

A total of 3035 male and 2741 female patients were enrolled in the present study. The death rates within 1 month and 3 months after diagnosis were 1.32% and 2.44%, respectively. Early mortality decreased significantly during the past 40 years. Our results showed that glioblastoma, anaplastic glioma, and oligodendroglioma were risk factors of early mortality for children diagnosed with glioma, whereas advanced age, gross total resection, radiation, and chemotherapy were associated with decreased early mortality.

CONCLUSIONS

We found a decrease in early mortality during the past 40 years. The death rates within 1 month and 3 months after diagnosis were 1.32% and 2.44%, respectively. Age at diagnosis, histologic subtype, the extent of resection, chemotherapy, and radiation were associated with early mortality in pediatric glioma.