Grant Number:	5R01CA263494-03 Interpret this number
Primary Investigator:	Wu, Lang
Organization:	University Of Hawaii At Manoa
Project Title:	Uncovering Causal Protein Markers to Improve Prostate Cancer Etiology Understanding and Risk Prediction in Africans and Europeans
Fiscal Year:	2024

Prostate cancer (PCa) is the second most commonly diagnosed malignancy in men, with incidence and mortality rates varying across Africans and Europeans. The vast majority of deaths from PCa occur among the approximately 10-15% of patients diagnosed with aggressive PCa. The etiology of PCa is poorly understood. Basic research supports a crucial role of certain proteins in PCa development. Epidemiological studies also have identified multiple candidate protein biomarkers for PCa. However, conventional epidemiologic studies were conducted primarily in Europeans, and it is unclear which of the candidate protein biomarkers may be European-specific or pan-ethnic. Also, findings with many of these biomarkers have been inconsistent, potentially due to major methodological limitations, such as selection bias and uncontrolled confounding. Besides understanding etiology, identifying causal protein biomarkers can potentially contribute to improving risk prediction. For PCa, substantial efforts have been made to identify high-risk populations for improving PCa screening. However, the performance of available PCa risk prediction models remains unsatisfactory. There are critical needs to 1) apply a novel study design with reduced limitations of conventional biomarker studies for characterizing PCa causally related protein biomarkers across Africans and Europeans to improve the etiology understanding; and 2) develop improved prediction models that may effectively facilitate PCa risk/aggressiveness assessment across Africans and Europeans. One strategy to potentially decrease limitations of unmeasured confounding is to use genetic instruments for assessing the relationship between proteins and PCa. While our previous studies have utilized proteins measured in blood, it is also critical to study prostate tissue, the most relevant tissue for PCa development, as levels of many proteins show tissue-specific effects. The proposed project will apply a series of new studies to address these important knowledge gaps. Specifically, we will 1) conduct a study to identify putative causal protein biomarkers for PCa risk and aggressiveness across Africans and Europeans by applying novel methods (Aim 1); 2) functionally characterize top protein biomarkers for their roles in PCa biology (Aim 2); and 3) develop and validate ethnic-specific and pan-ethnic prediction models for PCa risk and aggressiveness, by incorporating newly identified candidate protein biomarkers and integrating results from multiple statistical methods (Aim 3). Our study will generate important new knowledge for PCa etiology, and develop improved PCa risk/aggressiveness prediction models across Africans and Europeans. The proposed new methods can also be applied to other complex diseases.




MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.
Authors: Melton H.J. , Zhang Z. , Deng H.W. , Wu L. , Wu C. .
Source: Epigenetics, 2024 Dec; 19(1), p. 2370542.
EPub date: 2024-07-04 00:00:00.0.
PMID: 38963888
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Integrating Multi-Omics Data to Uncover Prostate Tissue DNA Methylation Biomarkers and Target Genes for Prostate Cancer Risk.
Authors: Liu S. , Zhu J. , Green D. , Zhong H. , Long Q. , Wu C. , Wang L. , Deng Y. , Wu L. .
Source: Molecular Carcinogenesis, 2024-10-14 00:00:00.0; , .
EPub date: 2024-10-14 00:00:00.0.
PMID: 39400371
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Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.
Authors: Liu S. , Zhu J. , Zhong H. , Wu C. , Xue H. , Darst B.F. , Guo X. , Durda P. , Tracy R.P. , Liu Y. , et al. .
Source: Diabetologia, 2024-09-30 00:00:00.0; , .
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Benchmarking DNA Foundation Models for Genomic Sequence Classification.
Authors: Feng H. , Wu L. , Zhao B. , Huff C. , Zhang J. , Wu J. , Lin L. , Wei P. , Wu C. .
Source: Biorxiv : The Preprint Server For Biology, 2024-08-18 00:00:00.0; , .
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Authors: Liu T. , Joshu C.E. , Lu J. , Prizment A. , Chatterjee N. , Wu L. , Platz E.A. .
Source: The Prostate, 2024-08-15 00:00:00.0; , .
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Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells.
Authors: Tian Y. , Wu L. , Huang C.C. , Wang L. .
Source: Biorxiv : The Preprint Server For Biology, 2024-06-21 00:00:00.0; , .
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Authors: Lyu Y. , Wu C. , Sun W. , Li Z. .
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Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.
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Source: Human Molecular Genetics, 2023-11-03 00:00:00.0; 32(22), p. 3181-3193.
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Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression.
Authors: Tian Y. , Dong D. , Wang Z. , Wu L. , Park J.Y. , PRACTICAL consortium , Wei G.H. , Wang L. .
Source: American Journal Of Human Genetics, 2023-08-03 00:00:00.0; 110(8), p. 1289-1303.
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THE EFFECT DIRECTION SHOULD BE TAKEN INTO ACCOUNT WHEN ASSESSING SMALL-STUDY EFFECTS.
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Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.
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Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease.
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