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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2021
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oai:doaj.org-article:e04a68afd0214bb2aaaa455915e1fc0c2021-12-02T17:20:11ZKLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness10.1038/s41598-021-85169-72045-2322https://doaj.org/article/e04a68afd0214bb2aaaa455915e1fc0c2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85169-7https://doaj.org/toc/2045-2322Abstract 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.Hui-Yi LinPo-Yu HuangChia-Ho ChengHeng-Yuan TungZhide FangAnders E. BerglundAnn ChenJennifer French-KwawuDarian HarrisJulio Pow-SangKosj YamoahJohn L. ClevelandShivanshu AwasthiRobert J. RounbehlerTravis GerkeJasreman DhillonRosalind EelesZsofia Kote-JaraiKenneth MuirUKGPCS collaboratorsJohanna SchleutkerNora PashayanAPCB (Australian Prostate Cancer BioResource)David E. NealSune F. NielsenBørge G. NordestgaardHenrik GronbergFredrik WiklundGraham G. GilesChristopher A. HaimanRuth C. TravisJanet L. StanfordAdam S. KibelCezary CybulskiKay-Tee KhawChristiane MaierStephen N. ThibodeauManuel R. TeixeiraLisa Cannon-AlbrightHermann BrennerRadka KanevaHardev PandhaThe PRACTICAL consortiumSrilakshmi SrinivasanJudith ClementsJyotsna BatraJong Y. ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
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DOAJ |
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Medicine R Science Q 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 KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| description |
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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| 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 |
| author_facet |
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 |
| author_sort |
Hui-Yi Lin |
| title |
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| title_short |
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| title_full |
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| title_fullStr |
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| title_full_unstemmed |
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness |
| title_sort |
klk3 snp–snp interactions for prediction of prostate cancer aggressiveness |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/e04a68afd0214bb2aaaa455915e1fc0c |
| work_keys_str_mv |
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