학술논문
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness
Document Type
article
Author
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
Source
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Subject
Language
English
ISSN
2045-2322
Abstract
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