Grant Details
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 |
Abstract
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.
Publications
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
Related Citations
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; , .
EPub date: 2024-09-30 00:00:00.0.
PMID: 39349773
Related Citations
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; , .
EPub date: 2024-08-18 00:00:00.0.
PMID: 39185205
Related Citations
Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study.
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; , .
EPub date: 2024-08-15 00:00:00.0.
PMID: 39148211
Related Citations
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; , .
EPub date: 2024-06-21 00:00:00.0.
PMID: 38948854
Related Citations
Regional analysis to delineate intrasample heterogeneity with RegionalST.
Authors: Lyu Y.
, Wu C.
, Sun W.
, Li Z.
.
Source: Bioinformatics (oxford, England), 2024-03-29 00:00:00.0; 40(4), .
PMID: 38579257
Related Citations
Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.
Authors: Meng Z.
, Wang J.
, Lin L.
, Wu C.
.
Source: Statistics In Medicine, 2024-02-06 00:00:00.0; , .
EPub date: 2024-02-06 00:00:00.0.
PMID: 38318993
Related Citations
Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.
Authors: Zhu J.
, Wu K.
, Liu S.
, Masca A.
, Zhong H.
, Yang T.
, Ghoneim D.H.
, Surendran P.
, Liu T.
, Yao Q.
, et al.
.
Source: Gigascience, 2024-01-02 00:00:00.0; 13, .
PMID: 38608280
Related Citations
Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.
Authors: Zhong H.
, Zhu J.
, Liu S.
, Ghoneim D.H.
, Surendran P.
, Liu T.
, Fahle S.
, Butterworth A.
, Ashad Alam M.
, Deng H.W.
, et al.
.
Source: Human Molecular Genetics, 2023-11-03 00:00:00.0; 32(22), p. 3181-3193.
PMID: 37622920
Related Citations
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.
PMID: 37541187
Related Citations
THE EFFECT DIRECTION SHOULD BE TAKEN INTO ACCOUNT WHEN ASSESSING SMALL-STUDY EFFECTS.
Authors: Meng Z.
, Wu C.
, Lin L.
.
Source: The Journal Of Evidence-based Dental Practice, 2023 Mar; 23(1), p. 101830.
EPub date: 2022-12-24 00:00:00.0.
PMID: 36914304
Related Citations
Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.
Authors: Wei W.
, Petersen M.
, van der Laan M.J.
, Zheng Z.
, Wu C.
, Wang J.
.
Source: Biometrics, 2022-11-22 00:00:00.0; , .
EPub date: 2022-11-22 00:00:00.0.
PMID: 36416173
Related Citations
Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease.
Authors: King A.
, Wu L.
, Deng H.W.
, Shen H.
, Wu C.
.
Source: Bmc Medicine, 2022-11-07 00:00:00.0; 20(1), p. 385.
EPub date: 2022-11-07 00:00:00.0.
PMID: 36336692
Related Citations