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Grant Details

Grant Number: 5R21CA202417-02 Interpret this number
Primary Investigator: Lin, Hui-Yi
Organization: Lsu Health Sciences Center
Project Title: Gene-Gene Interactions and Their Functional Roles in Prostate Cancer Aggressiveness
Fiscal Year: 2017


Project Summary/Abstract Prostate cancer with substantial clinical heterogeneity is the most common cancer and the second leading cause of cancer-related death in American men. It remains unclear why some prostate tumors are more aggressive than others. Existing clinical features (such as prostate specific antigen (PSA), clinical stage and Gleason score) are not sufficient for classifying high- and low-risk prostate cancer patients. It has been shown that approximately 20% of low-risk prostate cancer patients died due to conservative treatment. Thus, there is an urgent need for identifying additional biomarkers in order to improve prediction accuracy of prostate cancer aggressiveness. The majority of current studies focus on evaluating individual genetic variants, which may not be sufficient to explain the complexity of disease causality. The objective of this study is to identify gene-gene interactions within the four candidate pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism) associated with prostate cancer aggressiveness and their impact on gene expression. The genetic variants (both individual effects and interactions) associated with prostate cancer aggressiveness will be performed using the existing genetic data from the large scale prostate cancer consortium, a collection of approximately 22,000 prostate cancer patients. The associations between genetic variants and gene expressions will be identified using public domain genetic data and will be validated using a cohort data set with 1065 prostate cancer patients. Evaluating genetic variants with gene expression levels helps to identify downstream genes which can guide further study and may lead to discovery of novel therapeutic targets. Our study findings can provide valuable information toward understanding pathogenesis of prostate cancer and identifying genotype combinations for predicting prostate cancer aggressiveness. As for the long-term impact, the study results may be applied in developing effective screening tools to predict prostate cancer aggressiveness.


SNPxE: SNP-environment interaction pattern identifier.
Authors: Lin H.Y. , Huang P.Y. , Tseng T.S. , Park J.Y. .
Source: BMC bioinformatics, 2021-09-07; 22(1), p. 425.
EPub date: 2021-09-07.
PMID: 34493206
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KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness.
Authors: Lin H.Y. , Huang P.Y. , Cheng C.H. , Tung H.Y. , Fang Z. , Berglund A.E. , Chen A. , French-Kwawu J. , Harris D. , Pow-Sang J. , et al. .
Source: Scientific reports, 2021-04-29; 11(1), p. 9264.
EPub date: 2021-04-29.
PMID: 33927218
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Alcohol Intake and Alcohol-SNP Interactions Associated with Prostate Cancer Aggressiveness.
Authors: Lin H.Y. , Wang X. , Tseng T.S. , Kao Y.H. , Fang Z. , Molina P.E. , Cheng C.H. , Berglund A.E. , Eeles R.A. , Muir K.R. , et al. .
Source: Journal of clinical medicine, 2021-02-02; 10(3), .
EPub date: 2021-02-02.
PMID: 33540941
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A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.
Authors: Huynh-Le M.P. , Fan C.C. , Karunamuni R. , Walsh E.I. , Turner E.L. , Lane J.A. , Martin R.M. , Neal D.E. , Donovan J.L. , Hamdy F.C. , et al. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2020 Sep; 29(9), p. 1731-1738.
EPub date: 2020-06-24.
PMID: 32581112
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Interactions of PVT1 and CASC11 on Prostate Cancer Risk in African Americans.
Authors: Lin H.Y. , Callan C.Y. , Fang Z. , Tung H.Y. , Park J.Y. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019 Jun; 28(6), p. 1067-1075.
EPub date: 2019-03-26.
PMID: 30914434
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Alcohol intake patterns for cancer and non-cancer individuals: a population study.
Authors: Lin H.Y. , Fisher P. , Harris D. , Tseng T.S. .
Source: Translational cancer research, 2019 Jan; 8(Suppl 4), p. S334-S345.
PMID: 31497514
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AA9int: SNP interaction pattern search using non-hierarchical additive model set.
Authors: Lin H.Y. , Huang P.Y. , Chen D.T. , Tung H.Y. , Sellers T.A. , Pow-Sang J.M. , Eeles R. , Easton D. , Kote-Jarai Z. , Amin Al Olama A. , et al. .
Source: Bioinformatics (Oxford, England), 2018-12-15; 34(24), p. 4141-4150.
PMID: 29878078
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SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.
Authors: Lin H.Y. , Chen D.T. , Huang P.Y. , Liu Y.H. , Ochoa A. , Zabaleta J. , Mercante D.E. , Fang Z. , Sellers T.A. , Pow-Sang J.M. , et al. .
Source: Bioinformatics (Oxford, England), 2017-03-15; 33(6), p. 822-833.
PMID: 28039167
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Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer.
Authors: Lin H.Y. , Cheng C.H. , Chen D.T. , Chen Y.A. , Park J.Y. .
Source: Translational cancer research, 2016 Oct; 5(Suppl 5), p. S951-S963.
PMID: 28664150
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