KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness

KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness

Abstract Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP–SNP interactions in the four pathways (the angiogenesis-, mitochon...

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Autores principales: Hui-Yi Lin, Po-Yu Huang, Chia-Ho Cheng, Heng-Yuan Tung, Zhide Fang, Anders E. Berglund, Ann Chen, Jennifer French-Kwawu, Darian Harris, Julio Pow-Sang, Kosj Yamoah, John L. Cleveland, Shivanshu Awasthi, Robert J. Rounbehler, Travis Gerke, Jasreman Dhillon, Rosalind Eeles, Zsofia Kote-Jarai, Kenneth Muir, UKGPCS collaborators, Johanna Schleutker, Nora Pashayan, APCB (Australian Prostate Cancer BioResource), David E. Neal, Sune F. Nielsen, Børge G. Nordestgaard, Henrik Gronberg, Fredrik Wiklund, Graham G. Giles, Christopher A. Haiman, Ruth C. Travis, Janet L. Stanford, Adam S. Kibel, Cezary Cybulski, Kay-Tee Khaw, Christiane Maier, Stephen N. Thibodeau, Manuel R. Teixeira, Lisa Cannon-Albright, Hermann Brenner, Radka Kaneva, Hardev Pandha, The PRACTICAL consortium, Srilakshmi Srinivasan, Judith Clements, Jyotsna Batra, Jong Y. Park
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/e04a68afd0214bb2aaaa455915e1fc0c
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Sumario:Abstract Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP–SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10–9) and 3145 (P < 1 × 10–5) SNP–SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene–gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP–SNP interactions were supported by gene expression and protein–protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

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