Identifying causal genes for prostatitis through drug-targeted Mendelian randomization

Prostatitis is a common condition in andrology and urology that significantly impacts the quality of life of affected individuals. Current treatments often fail to provide lasting benefits. To identify novel therapeutic targets, we conducted a drug-targeted Mendelian randomization (MR) study. Using cis-expression quantitative trait loci (cis-eQTL) data from the eQTLGen Consortium combined with Genome-Wide Association Studies (GWAS) data on prostatitis from FinnGen, we performed a two-sample MR analysis. This analysis identified nine potential causal genes: ANXA1, CRY2, DSTYK, FKBP1A, LAMA5, NENF, PTGIR, STK39, and TGFA. Following heterogeneity testing, horizontal pleiotropy assessment, and bidirectional MR, CRY2 and PTGIR were validated in the Genotype-Tissue Expression (GTEx) portal replication phase. Bayesian colocalization analysis and genetic correlation analysis investigations provided strong evidence of shared causal variants with prostatitis and negative genetic correlations for these genes. PheWAS indicated negligible horizontal pleiotropy, and drug prediction analysis identified potential targeting agents for CRY2 and PTGIR. This study highlights CRY2 and PTGIR as promising therapeutic targets for prostatitis, providing new insights into its genetic underpinnings and offering potential pathways for developing effective treatments.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-30
DOI
https://doi.org/10.1038/s41598-025-03510-w
Citations
1
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
1.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifying causal genes for prostatitis through drug-targeted Mendelian randomization

Caogang Li, Yifang Tao, Zihao Li, Peng Zhang et al.
1 citations
Scientific Reports
Genetic Associations and Epidemiology
1.82
article

Identifying causal genes for prostatitis through drug-targeted Mendelian randomization

Caogang Li, Yifang Tao, Zihao Li, Peng Zhang, Dong Zhang, Bohong Chen, Kun Yan
article en
1 citations

Abstract

Prostatitis is a common condition in andrology and urology that significantly impacts the quality of life of affected individuals. Current treatments often fail to provide lasting benefits. To identify novel therapeutic targets, we conducted a drug-targeted Mendelian randomization (MR) study. Using cis-expression quantitative trait loci (cis-eQTL) data from the eQTLGen Consortium combined with Genome-Wide Association Studies (GWAS) data on prostatitis from FinnGen, we performed a two-sample MR analysis. This analysis identified nine potential causal genes: ANXA1, CRY2, DSTYK, FKBP1A, LAMA5, NENF, PTGIR, STK39, and TGFA. Following heterogeneity testing, horizontal pleiotropy assessment, and bidirectional MR, CRY2 and PTGIR were validated in the Genotype-Tissue Expression (GTEx) portal replication phase. Bayesian colocalization analysis and genetic correlation analysis investigations provided strong evidence of shared causal variants with prostatitis and negative genetic correlations for these genes. PheWAS indicated negligible horizontal pleiotropy, and drug prediction analysis identified potential targeting agents for CRY2 and PTGIR. This study highlights CRY2 and PTGIR as promising therapeutic targets for prostatitis, providing new insights into its genetic underpinnings and offering potential pathways for developing effective treatments.

Scientific ReportsVol. 15(1)
First Affiliated Hospital of Xi'an Jiaotong University (CN), Second Affiliated Hospital of Xi'an Jiaotong University (CN)
Good health and well-being
Openalex Percentile: Top 15%
Genetic Associations and Epidemiology
1.82
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.