Genetic variants in PDSS1 and SLC16A6 of the ketone body metabolic pathway predict cutaneous melanoma-specific survival.

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A few single-nucleotide polymorphisms (SNPs) have been identified to be associated with cutaneous melanoma (CM) survival through genome-wide association studies, but stringent multiple testing corrections required for the hypothesis-free testing may have masked some true associations. Using a hypothesis-driven analysis approach, we sought to evaluate associations between SNPs in ketone body metabolic pathway genes and CM survival. We comprehensively assessed associations between 4196 (538 genotyped and 3658 imputed) common SNPs in 44 ketone body metabolic pathway genes and CM survival, using a dataset of 858 patients of a case-control study from The University of Texas M.D. Anderson Cancer Center as the discovery set and another dataset of 409 patients from the Nurses' Health Study and the Health Professionals Follow-up Study as the replication set. There were 95/858 (11.1%) and 48/409 (11.7%) patients who died of CM, respectively. We identified two independent SNPs (ie, PDSS1 rs12254548 G>C and SLC16A6 rs71387392 G>A) that were associated with CM survival, with allelic hazards ratios of 0.58 (95% confidence interval [CI] = 0.44-0.76, P = 9.00 × 10-5 ) and 1.98 (95% CI = 1.34-2.94, P = 6.30 × 10-4 ), respectively. Additionally, associations between genotypes of the SNPs and messenger RNA expression levels of their corresponding genes support the biologic plausibility of a role for these two variants in CM tumor progression and survival. Once validated by other larger studies, PDSS1 rs12254548 and SLC16A6 rs71387392 may be valuable biomarkers for CM survival.





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Dai, Wei, Hongliang Liu, Ka Chen, Xinyuan Xu, Danwen Qian, Sheng Luo, Christopher I Amos, Jeffrey E Lee, et al. (2020). Genetic variants in PDSS1 and SLC16A6 of the ketone body metabolic pathway predict cutaneous melanoma-specific survival. Molecular carcinogenesis, 59(6). pp. 640–650. 10.1002/mc.23191 Retrieved from

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Sheng Luo

Professor of Biostatistics & Bioinformatics

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