Genetic variants in ELOVL2 and HSD17B12 predict melanoma-specific survival.

Abstract

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single-nucleotide polymorphisms (SNPs) in 149 genes of the fatty-acid synthesis pathway with cutaneous melanoma disease-specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses' Health and Health Professionals Follow-up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51-0.84 and p = 8.34 × 10-4 ) and 2.29 (1.55-3.39 and p = 3.61 × 10-5 ), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies.

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Published Version (Please cite this version)

10.1002/ijc.32194

Publication Info

Dai, Wei, Hongliang Liu, Xinyuan Xu, Jie Ge, Sheng Luo, Dakai Zhu, Christopher I Amos, Shenying Fang, et al. (2019). Genetic variants in ELOVL2 and HSD17B12 predict melanoma-specific survival. International journal of cancer. 10.1002/ijc.32194 Retrieved from https://hdl.handle.net/10161/18098.

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Luo

Sheng Luo

Professor of Biostatistics & Bioinformatics

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