Comprehensive Proteomic Profiling of Triple-Negative Breast Cancer-Derived Small Extracellular Vesicles Unveiled PXDN and GGT5 as Novel Protein Markers Implicated in Oncogenic Signaling Networks
Bibliographic record
Abstract
Breast cancer (BC) is the most prevalent cancer and the second leading cause of cancer-related mortality among women. Early detection and treatment can significantly improve survival rates. The potential application of small extracellular vesicles (sEVs) as biomarkers for early BC diagnosis has gained increasing attention, primarily due to their promise as a minimally invasive detection method. However, the specific protein signatures of sEVs are still not well understood. This study compared the proteomes of MDA-MB-231 and MCF-10A cells with their respective sEVs and conducted cross-comparisons between the two cell types and their sEV populations. Bioinformatic analyses revealed that MDA-MB-231 cell-derived sEVs are enriched with proteins involved in cancer growth and proliferation pathways. The proteins from these pathways can offer a valuable resource for triple-negative BC (TNBC) biomarkers. In this study, three proteins were selected based on their unique presence in MDA-MB-231 cell-derived sEVs and their association with pathways related to BC: peroxidasin homolog (PXDN), glutathione hydrolase 5 proenzyme (GGT5), and plasminogen activator inhibitor 1 (SERPINE1). These proteins were validated using synthetic heavy-labeled peptides and mass spectrometry-based parallel reaction monitoring, as well as Western blot analysis. This study highlights the potential of sEV-based proteins as noninvasive biomarkers for early TNBC detection, laying the groundwork for future diagnostic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".