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Background <p>Understanding the role of causal genes of prostate cancer (PrCa) can reveal key biological pathways and identify potential targets for treatment.</p> Methods <p>We investigated associations between genetically predicted gene expression levels and PrCa risk using cis-eQTL summary-based Mendelian randomization (SMR) and colocalization analysis. Findings were replicated using two independent PrCa GWAS. We then intersected the identified genes with differentially expressed genes (DEGs) identified from TCGA-PRAD dataset to obtain key genes. Furthermore, enrichment, protein-molecule network, immune infiltration, and epigenetic analyses were conducted to explore their biological pathways. Lastly, phenome-wide association study (PheWAS), drug prediction, and molecular docking simulation analysis were utilized to identify potential drugs.</p> Results <p>We identified 15 genes in blood whose expression levels are putatively associated with PrCa, validated in at least one replication GWAS dataset. Using open-access mRNA-sequencing data, we found that <i>ZNF217</i> and <i>BNIP2</i> were key genes potentially important in PrCa pathogenesis. Single-cell RNA-sequencing analysis revealed that <i>BNIP2</i> was predominantly expressed in a subset of endothelial cells, whereas <i>ZNF217</i> was mainly enriched in epithelial cells. Downstream analysis revealed their involvement in epigenetic modulation-related pathways, while upstream analysis showed that upregulation of <i>ZNF217</i> notably correlated with increased CpG methylation. Molecular docking simulation suggested doxorubicin, alsterpaullone, and camptothecin as potential drugs targeting these key genes.</p> Conclusions <p>These findings provide robust leads for understanding pathogenic mechanisms and developing therapeutic interventions for PrCa.</p>

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Uncovering Novel Susceptible Genes and Therapeutic Targets of Prostate Cancer: a Multi-omics Study Integrating Summary-based Mendelian Randomization Analysis and Molecular Docking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Xuemeng Qiu,
  • Yifei Zhang,
  • Jiyue Wu,
  • Zihao Gao,
  • Xinyi Chai,
  • Xihao Shen,
  • Zejia Sun,
  • Wei Wang

摘要

Background

Understanding the role of causal genes of prostate cancer (PrCa) can reveal key biological pathways and identify potential targets for treatment.

Methods

We investigated associations between genetically predicted gene expression levels and PrCa risk using cis-eQTL summary-based Mendelian randomization (SMR) and colocalization analysis. Findings were replicated using two independent PrCa GWAS. We then intersected the identified genes with differentially expressed genes (DEGs) identified from TCGA-PRAD dataset to obtain key genes. Furthermore, enrichment, protein-molecule network, immune infiltration, and epigenetic analyses were conducted to explore their biological pathways. Lastly, phenome-wide association study (PheWAS), drug prediction, and molecular docking simulation analysis were utilized to identify potential drugs.

Results

We identified 15 genes in blood whose expression levels are putatively associated with PrCa, validated in at least one replication GWAS dataset. Using open-access mRNA-sequencing data, we found that ZNF217 and BNIP2 were key genes potentially important in PrCa pathogenesis. Single-cell RNA-sequencing analysis revealed that BNIP2 was predominantly expressed in a subset of endothelial cells, whereas ZNF217 was mainly enriched in epithelial cells. Downstream analysis revealed their involvement in epigenetic modulation-related pathways, while upstream analysis showed that upregulation of ZNF217 notably correlated with increased CpG methylation. Molecular docking simulation suggested doxorubicin, alsterpaullone, and camptothecin as potential drugs targeting these key genes.

Conclusions

These findings provide robust leads for understanding pathogenic mechanisms and developing therapeutic interventions for PrCa.