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In silico identification and in vitro expression analysis of breast cancer-related m 6 A-SNPs.

Tamara KleinbielenFelix OlasagastiDaniel AzcarateElena BeristainAmparo Viguri-DíazIsabel Guerra-MerinoÁfrica García-OradMarian M de Pancorbo
Published in: Epigenetics (2022)
Research on m 6 A-associated SNPs (m 6 A-SNPs) has emerged recently due to their possible critical roles in many key biological processes. In this sense, several investigations have identified m 6 A-SNPs in different diseases. In order to gain a more complete understanding of the role that m 6 A-SNPs can play in breast cancer, we performed an in silico analysis to identify the m 6 A-SNPs associated with breast cancer and to evaluate their possible effects. For this purpose, we downloaded SNPs related to breast cancer and a list of m 6 A-SNPs from public databases in order to identify which ones appear in both. Subsequently, we assessed the identified m 6 A-SNPs in silico by expression quantitative trait loci (eQTL) analysis and differential gene expression analysis. We genotyped the m 6 A-SNPs found in the in silico analysis in 35 patients with breast cancer, and we carried out a gene expression analysis experimentally on those that showed differences. Our results identified 981 m 6 A-SNPs related to breast cancer. Four m 6 A-SNPs showed an eQTL effect and only three were in genes that presented an altered gene expression. When the three m 6 A-SNPs were evaluated in the tissue sample of our breast cancer patients, only the m 6 A-SNP rs76563149 located in ZNF354A gene presented differences in allele frequencies and a low gene expression in breast cancer tissues, especially in luminal B HER2+ subtype. Future investigations of these m 6 A-SNPs should expand the study in different ethnic groups and increase the sample sizes to test their association with breast cancer and elucidate their molecular function.
Keyphrases
  • genome wide
  • dna methylation
  • gene expression
  • genome wide association
  • copy number
  • genome wide identification
  • emergency department
  • mass spectrometry
  • artificial intelligence