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SNPs within microRNA binding sites and the prognosis of breast cancer.

ABSTRACT: Single nucleotide polymorphisms (SNPs) within microRNA binding sites can affect the binding of microRNA to mRNA and regulate gene expression, thereby contributing to cancer prognosis. Here we performed a two-stage study of 2647 breast cancer patients to explore the association between SNPs within microRNA binding sites and breast cancer prognosis. In stage I, we genotyped 192 SNPs within microRNA binding sites using the Illumina Goldengate platform. In stage II, we validated SNPs associated with breast cancer prognosis in another dataset using the TaqMan platform. We identified 8 SNPs significantly associated with breast cancer prognosis in stage I (P<0.05), and only rs10878441 was statistically significant in stage II (AA vs CC, HR=2.21, 95% CI: 1.11-4.42, P=0.024). We combined the data from stage I and stage II, and found that, compared with rs10878441 AA genotype, CC genotype was associated with poor survival of breast cancer (HR=2.19, 95% CI: 1.30-3.70, P=0.003). Stratified analyses demonstrated that rs10878441 was related to breast cancer prognosis in grade II and lymph node-negative patients (P<0.05). The Leucine-rich repeat kinase 2 (LRRK2) rs10878441 CC genotype is associated with poor prognosis of breast cancer in a Chinese population and may be used as a potential prognostic biomarker for breast cancer. • The LRRK2 rs10878441 CC genotype is associated with poor prognosis of breast cancer in a Chinese population. • Stratified analyses demonstrated that rs10878441 was related to breast cancer prognosis in grade II patients and lymph node-negative patients.

SUBMITTER: Zhang L

PROVIDER: S-EPMC7993692 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAssociation between several single-nucleotide polymorphisms (SNPs) and breast cancer risk has been identified through genome-wide association studies (GWAS), but little is known about their significance in patients' prognosis. We screened SNPs which were related to the prognosis of breast cancer in Henan Han population, analyzed relevant genes by bioinformatics in database, and further constructed the genetic regulatory network involved in the pathogenesis of breast cancer.MethodsWe evaluated five SNPs in 232 cases of breast cancer at the Affiliated Tumor Hospital of Zhengzhou University. Relationships between five SNPs, clinical prognostic indicators, and disease-free survival (DFS) were evaluated by Kaplan-Meier analysis and Cox proportional hazards model. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of genes and Genome (KEGG) analysis were carried out to preliminarily establish genetic regulation network model of breast cancer. Bayesian algorithm was used to optimize the model.ResultsThe multivariate Cox proportional hazards model confirmed that SNP rs3803662 (TOX3/TNRC9) had correlation with DFS independently. In the multivariate Cox proportional hazards model, compared with GA/AA, GG increased the recurrent risk of breast cancer (p = .021, hazard ratio [HR] = 2.914). GO analysis showed that the function of TOX3/TNRC9 included biological_process, molecular_function, and cellular_component. According to KEGG signaling pathway database, the map of breast cancer-related gene regulatory network was obtained. IGF-IGF1R-PI3K-Akt-mTOR-S6K was the best possible pathway for the differentiation of breast cancer cells in this network and ER-TOX3/TNRC9 was the best possible pathway for the survival of tumor cells in this network by Bayesian theorem optimization.ConclusionsSNP rs3803662 (TOX3/TNRC9) is an independent prognostic factor for breast cancer in Henan Han Population. ER-TOX3/TNRC9 is the best possible pathway involved in the pathogenesis of breast cancer.

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SNPs within microRNA binding sites and the prognosis of breast cancer.

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