Current issue
Archive
Manuscripts accepted
About the Journal
Editorial office
Editorial board
Section Editors
Abstracting and indexing
Subscription
Contact
Ethical standards and procedures
Most read articles
Instructions for authors
Article Processing Charge (APC)
Regulations of paying article processing charge (APC)
BREAST CANCER / BASIC RESEARCH
Plasma proteome–genome integration reveals novel protein biomarkers and therapeutic targets linked to breast cancer survival
1
Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
2
Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
3
Institute of Clinical Pharmacy, Central South University, Changsha, Hunan, China
These authors had equal contribution to this work
Submission date: 2025-04-14
Final revision date: 2025-07-06
Acceptance date: 2025-07-14
Online publication date: 2025-08-04
Publication date: 2026-06-30
Corresponding author
Shenglan Tan
Department of Pharmacy The Second Xiangya Hospital Central South University 410011, Changsha, China
Department of Pharmacy The Second Xiangya Hospital Central South University 410011, Changsha, China
Arch Med Sci 2026;22(3):1678-1687
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Identifying new drug targets is essential for improving breast cancer survival. The proteome provides a rich source for potential therapeutic targets. This study aimed to identify protein markers and therapeutic targets for breast cancer by using proteome-wide Mendelian randomization (MR).
Material and methods:
Protein quantitative trait loci (pQTL) data were obtained from four large-scale proteomic studies, including 17,267 circulating protein markers. Genetic associations with breast cancer survival were derived from a large-scale GWAS meta-analysis (37,954 cases, 2,900 deaths). Proteome-wide MR was performed to estimate causal effects of proteins on breast cancer survival, complemented by single-cell expression analysis to identify enriched cell types. Protein-protein interactions (PPI) and druggability assessments were also conducted to prioritize therapeutic targets.
Results:
Genetically predicted circulating levels of 27 proteins were found to be associated with breast cancer survival. Among these, eight proteins (ADAM15, CD83, SH3BGRL3, SNCG, ANXA1, GRHPR, ALDH2, and MTHFD2) showed the strongest evidence of association, while four proteins (ARG2, RPL14, NFU1, and TXNL4B) demonstrated a strong but slightly weaker correlation. Notably, SH3BGRL3, GRHPR, ARG2, RPL14, NFU1, and TXNL4B were newly identified as circulating protein markers significantly associated with breast cancer prognosis. Druggability analysis revealed that 13 of these proteins were already targeted by existing drugs, offering potential for breast cancer treatment.
Conclusions:
We identified 27 genes encoding proteins associated with overall and subtype-specific breast cancer survival, providing potential prognostic biomarkers and therapeutic targets, and offering new avenues for improving breast cancer management.
Identifying new drug targets is essential for improving breast cancer survival. The proteome provides a rich source for potential therapeutic targets. This study aimed to identify protein markers and therapeutic targets for breast cancer by using proteome-wide Mendelian randomization (MR).
Material and methods:
Protein quantitative trait loci (pQTL) data were obtained from four large-scale proteomic studies, including 17,267 circulating protein markers. Genetic associations with breast cancer survival were derived from a large-scale GWAS meta-analysis (37,954 cases, 2,900 deaths). Proteome-wide MR was performed to estimate causal effects of proteins on breast cancer survival, complemented by single-cell expression analysis to identify enriched cell types. Protein-protein interactions (PPI) and druggability assessments were also conducted to prioritize therapeutic targets.
Results:
Genetically predicted circulating levels of 27 proteins were found to be associated with breast cancer survival. Among these, eight proteins (ADAM15, CD83, SH3BGRL3, SNCG, ANXA1, GRHPR, ALDH2, and MTHFD2) showed the strongest evidence of association, while four proteins (ARG2, RPL14, NFU1, and TXNL4B) demonstrated a strong but slightly weaker correlation. Notably, SH3BGRL3, GRHPR, ARG2, RPL14, NFU1, and TXNL4B were newly identified as circulating protein markers significantly associated with breast cancer prognosis. Druggability analysis revealed that 13 of these proteins were already targeted by existing drugs, offering potential for breast cancer treatment.
Conclusions:
We identified 27 genes encoding proteins associated with overall and subtype-specific breast cancer survival, providing potential prognostic biomarkers and therapeutic targets, and offering new avenues for improving breast cancer management.
REFERENCES (35)
1.
Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024; 74: 229-63.
2.
Hong Z, Fang Z, Lei J, et al. The significance of Runx2 mediating alcohol-induced Brf1 expression and RNA Pol III gene transcription. Chem Biol Interact 2020; 323: 109057.
3.
Li H, Ju X, Zeng C, et al. Development and validation of a pathological model predicting the efficacy of neoadjuvant therapy for breast cancer based on RCB scoring. Arch Med Sci 2025; 21: 92-101.
4.
Rehman SU, Asel U, Abdullah M, et al. The development of predictive biomarkers and immunologic markers for breast cancer: current status and future perspectives. Braz J Biol 2025; 85: e292947.
5.
Nalejska E, Mączyńska E, Lewandowska MA. Prognostic and predictive biomarkers: tools in personalized oncology. Mol Diagn Ther 2014; 18: 273-84.
6.
Wang SE, Tan VY, Yarmolinsky J, et al. The effect of circulating proteins and their role in mediating adiposity’s effect on endometrial cancer risk: Mendelian randomisation and colocalization analyses. Cancer Epidemiol Biomarkers Prev 2025; 34: 1534-43.
7.
Fan KC, Chen SC, Yen IW, et al. Plasma angiopoietin-like protein 4 as a novel biomarker predicting 10-year mortality in a community-based population: a longitudinal cohort study. Arch Med Sci 2025; 21: 51-9.
8.
Krug K, Jaehnig EJ, Satpathy S, et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 2020; 183: 1436-56.e31.
9.
Morra A, Escala-Garcia M, Beesley J, et al. Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment. Breast Cancer Res 2021; 23: 86.
10.
Chen J, Xu F, Ruan X, et al. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses. EBioMedicine 2023; 89: 104494.
11.
Zheng J, Haberland V, Baird D, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet 2020; 52: 1122-31.
12.
Sun J, Zhao J, Jiang F, et al. Identification of novel protein biomarkers and drug targets for colorectal cancer by integrating human plasma proteome with genome. Genome Med 2023; 15: 75.
13.
Gudjonsson A, Gudmundsdottir V, Axelsson GT, et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat Commun 2022; 13: 480.
14.
Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 2021; 53: 1712-21.
15.
Pietzner M, Wheeler E, Carrasco-Zanini J, et al. Mapping the proteo-genomic convergence of human diseases. Science 2021; 374: eabj1541.
16.
Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature 2018; 558: 73-9.
17.
Guo Q, Schmidt MK, Kraft P, et al. Identification of novel genetic markers of breast cancer survival. J Natl Cancer Inst 2015; 107: djv081.
18.
Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017; 77: e108-10.
19.
Finan C, Gaulton A, Kruger FA, et al. The druggable genome and support for target identification and validation in drug development. Sci Transl Med 2017; 9: eaag1166.
20.
Shu L, Sun L, Yu C, Ren D, Zhang Y, Zheng P. Bidirectional two-sample Mendelian randomization analysis identifies protein C rather than protein S or antithrombin-III as associated with deep venous thrombosis. Arch Med Sci 2025; 21: 215-23.
21.
Zhang C, Qin F, Li X, Du X, Li T. Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes. BMC Med 2022; 20: 211.
22.
Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 2014; 10: e1004383.
23.
Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 2019; 20: 163-72.
24.
Zhong JL, Poghosyan Z, Pennington CJ, et al. Distinct functions of natural ADAM-15 cytoplasmic domain variants in human mammary carcinoma. Mol Cancer Res 2008; 6: 383-94.
25.
López C, Bosch R, Korzynska A, et al. CD68 and CD83 immune populations in non-metastatic axillary lymph nodes are of prognostic value for the survival and relapse of breast cancer patients. Breast Cancer 2022; 29: 618-35.
26.
Prechtel AT, Steinkasserer A. CD83: an update on functions and prospects of the maturation marker of dendritic cells. Arch Dermatol Res 2007; 299: 59-69.
27.
Araújo TG, Mota STS, Ferreira HSV, Ribeiro MA, Goulart LR, Vecchi L. Annexin A1 as a regulator of immune response in cancer. Cells 2021; 10: 2245.
28.
Hu J, Yang L, Peng X, et al. ALDH2 hampers immune escape in liver hepatocellular carcinoma through ROS/Nrf2-mediated autophagy. Inflammation 2022; 45: 2309-24.
29.
Xu T, Guo J, Wei M, et al. Aldehyde dehydrogenase 2 protects against acute kidney injury by regulating autophagy via the Beclin-1 pathway. JCI Insight 2021; 6: e138183.
30.
Wang P, Fang Z, Pei W, et al. Senescence reprogramming by MTHFD2 deficiency facilitates tumor progression. J Cancer 2024; 15: 6577-93.
31.
Bellou V, Belbasis L, Tzoulaki I, Evangelou E. Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses. PLoS One 2018; 13: e0194127.
32.
Zhao Y, Zhao L, Wang T, et al. The herbal combination shu gan jie yu regulates the SNCG/ER-a/AKT-ERK pathway in DMBA-induced breast cancer and breast cancer cell lines based on RNA-Seq and IPA analysis. Integr Cancer Ther 2024; 23: 15347354241233258.
33.
Chiang CY, Pan CC, Chang HY, et al. SH3BGRL3 protein as a potential prognostic biomarker for urothelial carcinoma: a novel binding partner of epidermal growth factor receptor. Clin Cancer Res 2015; 21: 5601-11.
34.
Zong C, Nie X, Zhang D, et al. Up regulation of glyoxylate reductase/hydroxypyruvate reductase (GRHPR) is associated with intestinal epithelial cells apoptosis in TNBS-induced experimental colitis. Pathol Res Pract 2016; 212: 365-71.
35.
Genolet R, Kersten S, Braissant O, et al. Promoter rearrangements cause species-specific hepatic regulation of the glyoxylate reductase/hydroxypyruvate reductase gene by the peroxisome proliferator-activated receptor alpha. J Biol Chem 2005; 280: 24143-52.
Share
RELATED ARTICLE
| eISSN: | 1896-9151 |
| ISSN: | 1734-1922 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
