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Global changes in gene expression accompany the development of cancer. Thus, inherited variants in miRNA-binding sites are likely candidates for conferring inherited susceptibility. Using an in silico approach, we compiled a comprehensive list of SNPs predicted to modulate miRNA binding in genes from several key lung cancer pathways. We then investigated whether these SNPs were associated with lung cancer risk in two independent populations. In general, SNPs in miRNA-binding sites are rare. However, some allelic variation was observed. We found that rs1126579 in CXCR2 was associated with a reduced risk of lung cancer in both European American [ORTT vs. CC 0.56 (0.37–0.88); P = 0.008] and Japanese [ORTT vs. CC 0.62 (0.38–1.00); P = 0.049] populations. Furthermore, we found that the SNP disrupted a novel binding site for miR-516a-3p, led to a moderate increase in CXCR2 mRNA and protein expression, and increased MAPK signaling. Moreover, analysis of rs1126579 with serum levels of IL8, its endogenous ligand, supported an interaction whereby rs1126579-T and high serum IL8 conferred synergistic protection from lung cancer. Our findings demonstrate a function for a 3′UTR SNP in modulating CXCR2 expression, signaling, and susceptibility to lung cancer. Cancer Res; 75(3); 566–75. ©2014 AACR.

two cohorts by incorporating the resulting coefficients of multivariable Cox regression model into a score that utilizes linear gene expression values. This gene expression classifier was validated in 6 additional publicly available datasets of stage I/II lung SCC (N 1⁄4 358). The classifier identified high-risk patients in multiple large-scale and geographically diverse cohorts (N 1⁄4 570). The results appear to be independent of race and gene expression platform. Canonical pathways associated with this signature encompass proteins involved in signal transduction, tissue remodeling, and cell motility that would broadly lead to cancer cell migration, invasion and proliferation, suggesting that the gene signature identifies molecular subsets of patients with clinical relevance. This gene classifier could be used to guide clinical decisions after surgical resection. Thus, we would advocate that this classifier be incorporated into prospective trials for further evaluation of its clinical effectiveness.