@article{ATM9193,
author = {Padmanaban S. Suresh and Thejaswini Venkatesh and Rie Tsutsumi},
title = {In silico analysis of polymorphisms in microRNAs that target genes affecting aerobic glycolysis},
journal = {Annals of Translational Medicine},
volume = {4},
number = {4},
year = {2016},
keywords = {},
abstract = {Background: Cancer cells preferentially metabolize glucose through aerobic glycolysis, an observation known as the Warburg effect. Recently, studies have deciphered the role of oncogenes and tumor suppressor genes in regulating the Warburg effect. Furthermore, mutations in glycolytic enzymes identified in various cancers highlight the importance of the Warburg effect at the molecular and cellular level. MicroRNAs (miRNAs) are non-coding RNAs that posttranscriptionally regulate gene expression and are dysregulated in the pathogenesis of various types of human cancers. Single nucleotide polymorphisms (SNPs) in miRNA genes may affect miRNA biogenesis, processing, function, and stability and provide additional complexity in the pathogenesis of cancer. Moreover, mutations in miRNA target sequences in target mRNAs can affect expression.
Methods: In silico analysis and cataloguing polymorphisms in miRNA genes that target genes directly or indirectly controlling aerobic glycolysis was carried out using different publically available databases.
Results: miRNA SNP2.0 database revealed several SNPs in miR-126 and miR-25 in the upstream and downstream pre-miRNA flanking regions respectively should be inserted after flanking regions and miR-504 and miR-451 had the fewest. These miRNAs target genes that control aerobic glycolysis indirectly. SNPs in premiRNA genes were found in miR-96, miR-155, miR-25 and miR34a by miRNASNP. Dragon database of polymorphic regulation of miRNA genes (dPORE-miRNA) database revealed several SNPs that modify transcription factor binding sites (TFBS) or creating new TFBS in promoter regions of selected miRNA genes as analyzed by dPORE-miRNA.
Conclusions: Our results raise the possibility that integration of SNP analysis in miRNA genes with studies of metabolic adaptations in cancer cells could provide greater understanding of oncogenic mechanisms.},
issn = {2305-5847}, url = {https://atm.amegroups.org/article/view/9193}
}