Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

BACKGROUND

The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer.

METHODS

Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets.

RESULTS

Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer.

CONCLUSIONS

The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.