Potentially functional variants of ERAP1, PSMF1 and NCF2 in the MHC-I-related pathway predict non-small cell lung cancer survival.

BACKGROUND: Cellular immunity against tumor cells is highly dependent on antigen presentation by major histocompatibility complex class I (MHC-I) molecules. However, few published studies have investigated associations between functional variants of MHC-I-related genes and clinical outcomes of lung cancer patients. METHODS: We performed a two-phase Cox proportional hazards regression analysis by using two previously published genome-wide association studies to evaluate associations between genetic variants in the MHC-I-related gene set and the survival of non-small cell lung cancer (NSCLC) patients, followed by expression quantitative trait loci analysis. RESULTS: Of the 7811 single-nucleotide polymorphisms (SNPs) in 89 genes of 1185 NSCLC patients in the discovery dataset of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, 24 SNPs remained statistically significant after validation in additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility Study. In a multivariate stepwise Cox model, three independent functional SNPs (ERAP1 rs469783 T > C, PSMF1 rs13040574 C > A and NCF2 rs36071574 G > A) remained significant with an adjusted hazards ratio (HR) of 0.83 [95% confidence interval (CI) = 0.77-0.89, P = 8.0 × 10-7], 0.86 (0.80-0.93, P = 9.4 × 10-5) and 1.31 (1.11-1.54, P = 0.001) for overall survival (OS), respectively. Further combined genotypes revealed a poor survival in a dose-response manner in association with the number of unfavorable genotypes (Ptrend < 0.0001 and 0.0002 for OS and disease-specific survival, respectively). Also, ERAP1 rs469783C and PSMF1 rs13040574A alleles were associated with higher mRNA expression levels of their genes. CONCLUSION: These potentially functional SNPs of the MHC-I-related genes may be biomarkers for NSCLC survival, possibly through modulating the expression of corresponding genes.

Duke Scholars

Author Sheng Luo Biostatistics & Bioinformatics, Division of Biostatistics

Author Carolyn Glass Pathology

Author Qingyi Wei Population Health Sciences