Glioblastoma in the setting of prior lower grade gliomas - insights from SEER database

Identification of thioredoxin domain containing family members' expression pattern and prognostic value in diffuse gliomas via in silico analysis

BACKGROUND

Gliomas are the most prevalent primary tumors of the central nervous system. Their aggressive nature and the obstacles arising during therapy highlights the importance of finding new prognostic markers and therapy targets for gliomas. TXNDC genes are members of the thioredoxin superfamily and were shown to play a role in redox homeostasis, protein folding, electron transfer and also acting as cellular adapters. The well known contribution of these processes in cancer progression prompted us to investigate if TXNDC family members may also play a role in carcinogenesis, in particular diffuse gliomas.

METHODS

The present study used in silico analysis tools GEPIA, UCSC Xena, Gliovis, cBioPortal, and Ivy GAP to evaluate the expression pattern, prognostic value and clinical significance of TXNDC family members in diffuse gliomas.

RESULTS

Our analysis showed that TXNDC family members' expression pattern differ between tumors and healthy tissues and among tumors with different grades. The detailed analysis of TXNDC5 in glioma pathogenesis revealed that TXNDC5 expression is associated with more aggressive clinical and molecular features and poor therapy success both in LGG and GBM samples. Kaplan-Meier survival curves represented a worse prognosis for patients with leveated TXNDC5 levels in LGG and all grade glioma patients. The levels of TXNDC5 was shown to be possibly regulated by hypoxia-ER stress axis and a potential mechanism for TXNDC5-driven glioma progression was found to be extracellular matrix (ECM) production which is known to promote tumor aggressiveness.

CONCLUSIONS

Our results uncovered the previously unknown role of TXNDC family members in glioma pathogenesis and showed that TXNDC5 levels could serve as a predictor of clinical outcome and therapy success and may very well be used for targeted therapy.

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.