Proteome of breast cancer derived microvesicles
Bibliographic record
Abstract
The aim of the research. To examine the proteomic profi le of breast cancer exosomes. Material and methods. Cell lines used for this study were MDA-MB-231 female epithelial breast cancer cells (ATCC HTB-26) and MCF10A non-tumorigenic epithelial breast tissue cells. MVs were isolated using diff erential ultracentrifugation. Samples were lysed, reduced, alkylated, digested, and analyzed by an Orbitrap Fusion mass spectrometer. MS raw fi les were analyzed using MaxQuant version 1.6.12.0. Peptides were searched against the human UniProt FASTA database using the Andromeda search engine, integrated into MaxQuant. Results. MVs derived from MCF10A and MDA-MB-231 cell lines were analyzed, and 1427 and 547 proteins were identifi ed in the MDA-MB-231 and MCF10A-derived MVs, respectively. In total, 455 proteins were common to both MDA-MB-231 and MCF10A MVs. MVs derived from MCF10A and MDAMB-231 cell lines were analyzed, and 1427 and 547 proteins were identifi ed in the MDA-MB-231 and MCF10A-derived MVs, respectively. In total, 455 proteins were common to both MDA-MB-231 and MCF10A MVs. Th e unique MDA-MB-231 MV proteins were searched against the DisGeNET human diseases database. Out of 972 MDA-MB-231 MV proteins, 112 were cancer-related while 32 were specifi cally associated with BC. In the MDA-MB-231 MV proteome, 23 Wnt signaling pathway proteins were identifi ed based on their GO biological process. Proteomic analysis identifi ed enzymes OAT, TALDO1, and BLMH were only in MVs from metastatic MDA-MB-231 cell line. The specific activity of OAT and TALDO1 was higher in MV fractions of MDA-MB-231 in comparison to the non-cancerous MCF10A cell line-derived MVs. Th ese fi ndings might suggest that these enzymes might play a role in BC. In our present study, we found that some enzymes identifi ed from MV fractions were already proposed to play a role in cancer therapy as therapeutic targets (OAT, TALDO1) and resistance against chemotherapy agents (BLMH).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".