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MicroRNA-Related Genetic Variants Associated with Survival of Head and Neck Squamous Cell Carcinoma.

Cancer Epidemiol. Biomarkers Prev.2019 Jan;28(1):127-136. Epub 2018 Jun 07
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摘要


BACKGROUND:Head and neck squamous cell carcinoma (HNSCC) is commonly diagnosed at an advanced stage, and prognosis for such patients is poor. There remains a gap in our understanding of genetic variants related with HNSCC prognosis. miRNA-related single nucleotide polymorphisms (miR-SNPs) are a class of genetic variants with gene-regulatory potential. METHODS:We used a genome-scale approach and independent patient populations in a two-stage approach to test 40,286 common miR-SNPs for association with HNSCC survival in the discovery population (n = 847), and selected the strongest associations for replication in validation phase cases (n = 1,236). Furthermore, we leveraged miRNA interaction databases and miRNA expression data from The Cancer Genome Atlas, to provide functional insight for the identified and replicated associations. RESULTS:Joint population analyses identified novel miR-SNPs associated with overall survival in oral and laryngeal cancers. rs1816158, located within long noncoding RNA MIR100HG, was associated with overall survival in oral cavity cancer (HR, 1.56; 95% confidence interval (CI), 1.21-2.00). In addition, expression of MIR100HG-embedded miRNA, miR-100, was significantly associated with overall survival in an independent cohort of HNSCC cases (HR, 1.25; 95% CI, 1.06-1.49). A SNP in the 3'UTR of SH3BP4 (rs56161233) that overlaps predicted miRNA-binding sites and is predicted to disrupt several miRNA-mRNA interactions was associated with overall survival of laryngeal cancer (HR, 2.57; 95% CI, 1.71-3.86). CONCLUSIONS:This work reveals novel miR-SNPs associated with HNSCC survival, and utilizes miRNA-mRNA interaction and expression data to provide functional support for these associations. IMPACT:These findings extend our understanding of how genetic variation contributes to HNSCC survival, and may contribute to future prognostic models for improved risk stratification.

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