Association between SNP rs527616 in lncRNA AQP4-AS1 and susceptibility to breast cancer in a southern Brazilian population.
Rafael D MarchiCarolina MathiasGabriel A K ReiterRubens Silveira de LimaFlávia KurodaCícero de Andrade UrbanRicardo Lehtonen Rodrigues de SouzaDaniela Fiori GradiaEnilze Maria de Souza Fonseca RibeiroIglenir J CavalliJaqueline Carvalho de OliveiraPublished in: Genetics and molecular biology (2021)
Breast cancer (BC) is the leading cause of death by this disease in women worldwide. Among the factors involved in tumorigenesis, long non-coding RNAs (lncRNAs) and their differential expression have been associated. Differences in gene expression may be triggered by variations in DNA sequence, including single nucleotide polymorphisms (SNPs). In the present study, we analyzed the rs527616 (C>G), located in the lncRNA AQP4-AS1, using PCR-SSP in 306 BC patients and 312 controls, from a Brazilian population. In the BC group, the frequency found for CG heterozygotes was above the expected and the overdominant model is the best one to explain our results (OR: 1.70, IC 95%: 1.23-2.34, P<0.001). Furthermore, the SNP were associated with age at BC diagnosis and the risk genotype more frequent in the older age group. According to TCGA data, AQP4-AS1 is down-regulated in BC tissue, and the overexpression is associated with better prognoses, including Luminal A, HER2-, stage 1 of disease and smaller tumor. In conclusion, the CG genotype is associated with increased susceptibility in the southern Brazilian population. This SNP is mapped in the lncRNA AQP4-AS1, showing differential expression in BC samples. Based on these results, we emphasize the potential of the role of AQP4-AS1 in cancer.
Keyphrases
- long non coding rna
- genome wide
- gene expression
- poor prognosis
- dna methylation
- end stage renal disease
- newly diagnosed
- ejection fraction
- transcription factor
- chronic kidney disease
- long noncoding rna
- breast cancer risk
- squamous cell carcinoma
- physical activity
- adipose tissue
- young adults
- papillary thyroid
- electronic health record
- genetic diversity
- risk assessment
- big data
- artificial intelligence
- machine learning
- patient reported
- childhood cancer
- human health