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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Cancer Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1449668
This article is part of the Research Topic Advances in Targeted Therapy and Biomarker Research for Endocrine-Related Cancers View all 9 articles

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

Provisionally accepted
Jingshuang Song Jingshuang Song 1,2Huawei Yang Huawei Yang 3,4*
  • 1 Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Region, China
  • 2 Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Region, China
  • 3 Guangxi Medical University Cancer Hospital, Nanning, China
  • 4 Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi Department of Education, Nanning, Guangxi Zhuang Region, China

The final, formatted version of the article will be published soon.

    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.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.

    Keywords: breast cancer, Proteomics, biomarkers, Drug Targets, Mendelian randomization, Colocalization analysis

    Received: 15 Jun 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Song and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Huawei Yang, Guangxi Medical University Cancer Hospital, Nanning, China

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