Identifying therapeutic targets for breast cancer: insights from systematic Mendelian randomization analysis.

ABSTRACT

Background: Breast cancer (BC) exhibits a high incidence rate, imposing a substantial burden on healthcare systems. Novel drug targets are urgently needed for BC. Mendelian randomization (MR) has gained widespread application for identifying fresh therapeutic targets. Our endeavor was to pinpoint circulatory proteins causally linked to BC risk and proffer potential treatment targets for BC. Methods: Through amalgamating protein quantitative trait loci from 2,004 circulating proteins and comprehensive genome-wide association study data from the Breast Cancer Association Consortium, we conducted MR analyses. Employing Steiger filtering, bidirectional MR, Bayesian colocalization, phenotype scanning, and replication analyses, we further solidified MR study outcomes. Additionally, protein-protein interaction (PPI) network was harnessed to unveil latent associations between proteins and prevailing breast cancer medications. The phenome-wide MR (Phe-MR) was employed to assess potential side effects and indications for the druggable proteins of BC. Finally, we further affirmed the drugability of potential drug targets through mRNA expression analysis and molecular docking. Results: Through comprehensive analysis, we identified five potential drug targets, comprising four (TLR1, A4GALT, SNUPN, and CTSF) for BC and one (TLR1) for BC_estrogen receptor positive. None of these five potential drug targets displayed reverse causation. Bayesian colocalization suggested that these five latent drug targets shared variability with breast cancer. All drug targets were replicated within the deCODE cohort. TLR1 exhibited PPI with current breast cancer therapeutic targets. Furthermore, Phe-MR unveiled certain adverse effects solely for TLR1 and SNUPN. Conclusion: Our study uncovers five prospective drug targets for BC and its subtypes, warranting further clinical exploration.