MicroRNA-related genetic variants associated with overall survival of head and neck squamous cell carcinoma
Menée auprès de 847 patients atteints d'un carcinome épidermoïde de la tête et du cou puis validée auprès de 1 236 patients complémentaires, cette étude évalue l'association entre 40 286 micro-ARNs, liés à des polymorphismes à simple nucléotide, et la survie globale des patients
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. MicroRNA-related single nucleotide polymorphisms (miR-SNPs) are a class of genetic variants with gene regulatory potential. 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 = 1236). Further, we leveraged miRNA interaction databases and miRNA expression data from The Cancer Genome Atlas (TCGA), to provide functional insight for the identified and replicated associations. Joint population analyses identified novel miR-SNPs associated with overall survival in oral and laryngeal cancers. rs1816158, located within long-non-coding RNA MIR100HG, was associated with overall survival in oral cavity cancer (HR; 1.56, 95% CI; 1.21-2.00). In addition, expression of MIR100HG-embedded microRNA, 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). Our findings reveal novel miR-SNPs associated with HNSCC survival, and extend our understanding of how genetic variation contributes to HNSCC prognosis.
Cancer Epidemiology Biomarkers & Prevention , article en libre accès, 2017