Centrioles play pivotal roles in the assembly of centrosomes, their dysfunction is associated with multiple inherited diseases or cancers. To date, few studies have focused on the associations between coding single nucleotide polymorphisms (SNPs) in the centriole duplication cycle genes and the risk of breast cancer in Chinese women.
Twenty-one SNPs were selected from the coding regions of 10 critical centriole genes. The associations between the selected SNPs and breast cancer susceptibility were assessed in a case-control study of Chinese women, which included 1032 cases and 1063 controls. Potential biological functions in the influence of protein stability and the profile of expression quantitative trait loci (eQTL) of the identified SNPs were further evaluated using in silico databases.
Multivariate logistic regression analyses revealed that a missense SNP rs7279204 in PCNT was significantly associated with an increased risk of breast cancer (additive model: adjusted OR=1.19, 95% CI: 1.02-1.38), while a missense SNP rs77922978 in CEP295 was significantly associated with a decreased risk of breast cancer (additive model: adjusted OR=0.74, 95% CI: 0.56-0.97). Stratification analyses suggested that rs7279204 and rs77922978 exhibited different effects among later first live birth, ER-negative and PR-negative women (P<0.05). Moreover, rs77922978 showed significant differences for ER and PR status strata (heterogeneity test P=0.028, P=0.046). In addition, bioinformatic analyses indicated that the two variants may possess potential functions of reducing the protein stability of their host genes. Further eQTL analysis showed that the rs7279204 was not only correlated with the expression of its host gene PCNT, but also correlated with the expression of its nearby genes, implying its potential roles in regulation of some cancer susceptibility genes.
The SNPs rs7279204 and rs77922978 within the coding region of the PCNT and CEP295 genes may contribute to the susceptibility of breast cancer in Han Chinese population.

Copyright © 2021. Published by Elsevier GmbH.

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