Background and Aim: The long noncoding RNAs (lncRNAs) is an important type of RNAs that can regulate gene expression and, therefore, are involved in the development of various cancers. The genome-wide association study (GWAS) is used to identify phenotype-related loci within non-coding regions. However, the biological functions and exact relationships between phenotype-related loci and lncRNAs have not fully been identified. No study was found on the relationship between rs8506C>T polymorphisms in the lincRNA-NR_024015 exon and breast cancer susceptibility and clinical factors. Therefore, the present study aimed to evaluate the effect of polymorphism rs8506C>T on the breast cancer risk.
Methods & Materials: In this case-control study, participants were 120 patients with breast cancer, 120 healthy controls. The genetic variant was genotyped by using the Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR–RFLP) method. Interactions between the polymorphism and clinical factors were further evaluated, and Odds Ratio (OR) was measured for risk assessment.
Ethical Considerations: This study has been approved by the Research Ethics Committee at Shahrekord University of Medical Sciences (code:????) .
Results: There was a correlation between rs8506 C>T polymorphism and breast cancer risk in the dominant model (CC and CT+TT genotypes; P=0.027; OR=1.84; 95% CI: 1.067‐3.201). In the co-dominant model, CT genotype had a statistically significant association with breast cancer risk (P=0.038). Subjects with T allele in the rs8506 polymorphism had an increased risk of breast cancer (OR=1.69; 95% CI: 1.047-2.736; P=0.031). No relationship between rs8506 polymorphism and clinical factors including metastasis, tumor grade, and Human Epidermal Growth Factor Receptor 2 (HER2) status was observed.
Conclusion: Genetic variant rs8506 C>T polymorphism in the lincRNA-NR_024015 exon may contribute to the breast cancer risk. Allele T in this variant confers an increased risk of breast cancer. Further functional analyses are required to detect the detailed mechanism underlying the observed association.