Back

Systems-Informed prioritization of Exosomal Protein Candidates in TNBC Identifies an ECM Invasion Module and Nominates Agrin as a High-Priority Target

Nguyen, T. M.

2026-05-19 cancer biology
10.64898/2026.05.14.725271 bioRxiv
Show abstract

BackgroundTriple-negative breast cancer (TNBC) remains the most clinically challenging breast cancer subtype, in part due to the absence of validated molecular targets and the limited availability of non-invasive early detection strategies. Tumor-derived exosomes have emerged as promising liquid biopsy analytes, yet the functional organization of their protein cargo and the identification of biologically meaningful candidates remain incompletely characterized. MethodsWe present a Composite Driver Score (CDS) framework that integrates differential expression magnitude with protein-protein interaction network topology and Analytic Hierarchy Process (AHP)-based multi-criteria weighting to prioritize exosomal protein candidates in a systems-informed manner. The framework was applied to publicly available label-free quantitative proteomic datasets comparing MDA-MB-231 (TNBC) and MCF-10A (non-tumorigenic) exosomal fractions, with cross-dataset validation performed on an independent proteomic dataset. ResultsCDS prioritization demonstrated robustness to variations in proteome depth and parameter weighting, consistently recovering a functionally coherent set of extracellular matrix (ECM) and adhesion-associated proteins. Network and pathway analyses revealed coordinated co-enrichment of integrin receptors, cognate ECM ligands, and associated co-receptors -- consistent with selective packaging of a functionally integrated invasion module. Agrin (AGRN), a heparan sulfate proteoglycan with virtually limited prior characterization in TNBC exosome biology, emerged as a high-priority candidate through its network integration within this ECM program. ConclusionsThese findings support a model in which TNBC-derived exosomes carry coordinated molecular programs capable of modulating extracellular matrix architecture. The CDS framework offers a transferable strategy for integrative exosomal biomarker prioritization and a systems-level foundation for targeted liquid biopsy panel development.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PROTEOMICS
35 papers in training set
Top 0.1%
12.6%
2
Molecular & Cellular Proteomics
158 papers in training set
Top 0.2%
12.4%
3
Journal of Proteome Research
215 papers in training set
Top 0.4%
7.2%
4
Breast Cancer Research
32 papers in training set
Top 0.1%
6.4%
5
PLOS Computational Biology
1633 papers in training set
Top 8%
4.3%
6
PLOS ONE
4510 papers in training set
Top 35%
4.0%
7
Computational and Structural Biotechnology Journal
216 papers in training set
Top 2%
3.6%
50% of probability mass above
8
Scientific Reports
3102 papers in training set
Top 36%
3.6%
9
Clinical Proteomics
10 papers in training set
Top 0.1%
3.3%
10
Nature Communications
4913 papers in training set
Top 43%
2.9%
11
Analytical Chemistry
205 papers in training set
Top 1%
2.5%
12
Bioinformatics
1061 papers in training set
Top 7%
1.7%
13
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 4%
1.7%
14
npj Biofilms and Microbiomes
56 papers in training set
Top 1%
1.2%
15
Advanced Science
249 papers in training set
Top 15%
1.1%
16
Cell Communication and Signaling
35 papers in training set
Top 0.8%
1.0%
17
Genome Medicine
154 papers in training set
Top 7%
1.0%
18
iScience
1063 papers in training set
Top 24%
1.0%
19
Cancer Research Communications
46 papers in training set
Top 0.8%
1.0%
20
Frontiers in Oncology
95 papers in training set
Top 3%
0.9%
21
Cancers
200 papers in training set
Top 4%
0.9%
22
npj Systems Biology and Applications
99 papers in training set
Top 2%
0.9%
23
eBioMedicine
130 papers in training set
Top 3%
0.9%
24
BMC Medicine
163 papers in training set
Top 6%
0.8%
25
Endocrinology
38 papers in training set
Top 0.5%
0.8%
26
Journal of Clinical Medicine
91 papers in training set
Top 6%
0.7%
27
Journal of Translational Medicine
46 papers in training set
Top 3%
0.7%
28
Communications Biology
886 papers in training set
Top 23%
0.7%
29
Cell Reports Medicine
140 papers in training set
Top 8%
0.7%
30
Scientific Data
174 papers in training set
Top 3%
0.7%