Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Prostate cancer (PCa) is a leading malignancy affecting men globally, contributing significantly to cancer-related morbidity and mortality. Our study aims to explore causal relationships between blood protein ratios (BPR) and PCa using a Mendelian randomization (MR) approach, potentially identifying new diagnostic and therapeutic targets. Methods: A two-sample MR method was employed, utilizing genetic variants as instrumental variables (IVs) to infer causality between circulating BPR and PCa. Data on BPR were obtained from a proteomics study of the UK Biobank, while PCa data were sourced from FinnGen, the PRACTICLE Consortium, and the GWAS Catalog. Stringent criteria were applied for IV selection, and statistical analyses included the inverse-variance weighted (IVW) method with sensitivity analyses to address pleiotropy and heterogeneity. Results: Significant causal associations were identified between several BPR and PCa. Notably, the ratios of CEBPB/PXN, APBB1IP/NCF2, APP/EGF, and CRKL/ EGF were found to be protective against PCa, while the ratios of ARHGAP1/RAD23B, EGF/ TNFSF14, and GOLM2/STC1 were identified as risk factors. Reverse MR analysis suggested that PCa might act as a protective factor for the GOLM2/STC1 ratio. Sensitivity analyses confirmed the robustness of these findings. Conclusions: This study elucidates significant causal relationships between 7 BPR and PCa, offering new insights for diagnosis, treatment evaluation, and personalized therapeutic strategies. Future research should focus on validating these findings and exploring the underlying biological mechanisms to improve PCa management.

Authors

Institutions

Publication Details

Journal
IETI Transactions on Data Analysis and Forecasting (iTDAF)
Published
2024-10-18
DOI
https://doi.org/10.3991/itdaf.v2i3.51637
Primary Topic
Cancer, Lipids, and Metabolism
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Jinghui Sung, Yuanpeng J. Huang, Yonglong Zhuang, Zikang Jiang et al.
IETI Transactions on Data Analysis and Forecasting (iTDAF)
Cancer, Lipids, and Metabolism
article

Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Jinghui Sung, Yuanpeng J. Huang, Yonglong Zhuang, Zikang Jiang, Weijie Li
article en

Abstract

Prostate cancer (PCa) is a leading malignancy affecting men globally, contributing significantly to cancer-related morbidity and mortality. Our study aims to explore causal relationships between blood protein ratios (BPR) and PCa using a Mendelian randomization (MR) approach, potentially identifying new diagnostic and therapeutic targets. Methods: A two-sample MR method was employed, utilizing genetic variants as instrumental variables (IVs) to infer causality between circulating BPR and PCa. Data on BPR were obtained from a proteomics study of the UK Biobank, while PCa data were sourced from FinnGen, the PRACTICLE Consortium, and the GWAS Catalog. Stringent criteria were applied for IV selection, and statistical analyses included the inverse-variance weighted (IVW) method with sensitivity analyses to address pleiotropy and heterogeneity. Results: Significant causal associations were identified between several BPR and PCa. Notably, the ratios of CEBPB/PXN, APBB1IP/NCF2, APP/EGF, and CRKL/ EGF were found to be protective against PCa, while the ratios of ARHGAP1/RAD23B, EGF/ TNFSF14, and GOLM2/STC1 were identified as risk factors. Reverse MR analysis suggested that PCa might act as a protective factor for the GOLM2/STC1 ratio. Sensitivity analyses confirmed the robustness of these findings. Conclusions: This study elucidates significant causal relationships between 7 BPR and PCa, offering new insights for diagnosis, treatment evaluation, and personalized therapeutic strategies. Future research should focus on validating these findings and exploring the underlying biological mechanisms to improve PCa management.

IETI Transactions on Data Analysis and Forecasting (iTDAF)Vol. 2(3)
Fujian University of Traditional Chinese Medicine (CN), Xiamen University (CN)
Good health and well-being
Openalex Percentile: Top 73%
Cancer, Lipids, and Metabolism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.