» Articles » PMID: 35571988

Mitochondrial 1555 G>A Variant As a Potential Risk Factor for Childhood Glioblastoma

Overview
Journal Neurooncol Adv
Date 2022 May 16
PMID 35571988
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Childhood glioblastoma multiforme (GBM) is a highly aggressive disease with low survival, and its etiology, especially concerning germline genetic risk, is poorly understood. Mitochondria play a key role in putative tumorigenic processes relating to cellular oxidative metabolism, and mitochondrial DNA variants were not previously assessed for association with pediatric brain tumor risk.

Methods: We conducted an analysis of 675 mitochondrial DNA variants in 90 childhood GBM cases and 2789 controls to identify enrichment of mitochondrial variant associated with GBM risk. We also performed this analysis for other glioma subtypes including pilocytic astrocytoma. Nuclear-encoded mitochondrial gene variants were also analyzed.

Results: We identified m1555 A>G was significantly associated with GBM risk (adjusted OR 29.30, 95% CI 5.25-163.4, value 9.5 X 10). No association was detected for other subtypes. Haplotype analysis further supported the independent risk contributed by m1555 G>A, instead of a haplogroup joint effect. Nuclear-encoded mitochondrial gene variants identified significant associations in European (rs62036057 in , adjusted OR = 2.99, 95% CI 1.88-4.75, -value = 3.42 X 10) and Hispanic (rs111709726 in , adjusted OR = 3.57, 95% CI 1.99-6.40, -value = 1.41 X 10) populations in ethnicity-stratified analyses.

Conclusion: We report for the first time a potential role played by a functional mitochondrial ribosomal RNA variant in childhood GBM risk, and a potential role for both mitochondrial and nuclear-mitochondrial DNA polymorphisms in GBM tumorigenesis. These data implicate cellular oxidative metabolic capacity as a contributor to the etiology of pediatric glioblastoma.

Citing Articles

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning.

Zheng H, Han X, Liu Q, Zhou L, Zhu Y, Wang J Heliyon. 2024; 10(2):e24854.

PMID: 38312556 PMC: 10835357. DOI: 10.1016/j.heliyon.2024.e24854.


The Comparative Experimental Study of Sodium and Magnesium Dichloroacetate Effects on Pediatric PBT24 and SF8628 Cell Glioblastoma Tumors Using a Chicken Embryo Chorioallantoic Membrane Model and on Cells In Vitro.

Damanskiene E, Balnyte I, Valanciute A, Lesauskaite V, Alonso M, Stakisaitis D Int J Mol Sci. 2022; 23(18).

PMID: 36142368 PMC: 9499689. DOI: 10.3390/ijms231810455.

References
1.
Hou T, Jian C, Xu J, Huang A, Xi J, Hu K . Identification of EFHD1 as a novel Ca(2+) sensor for mitoflash activation. Cell Calcium. 2016; 59(5):262-70. DOI: 10.1016/j.ceca.2016.03.002. View

2.
Bramer G . International statistical classification of diseases and related health problems. Tenth revision. World Health Stat Q. 1988; 41(1):32-6. View

3.
Maeda Y, Sasaki A, Kasai S, Goto S, Nishio S, Sawada K . Prevalence of the mitochondrial 1555 A>G and 1494 C>T mutations in a community-dwelling population in Japan. Hum Genome Var. 2020; 7:27. PMC: 7501278. DOI: 10.1038/s41439-020-00115-9. View

4.
Chang C, Chow C, Tellier L, Vattikuti S, Purcell S, Lee J . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4:7. PMC: 4342193. DOI: 10.1186/s13742-015-0047-8. View

5.
Ostrom Q, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan J . CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013-2017. Neuro Oncol. 2020; 22(12 Suppl 2):iv1-iv96. PMC: 7596247. DOI: 10.1093/neuonc/noaa200. View