Proteomic profiling of MCF-7 breast cancer cells with chemoresistance to different types of anti-cancer drugs
Bibliographic record
Abstract
Chemoresistance is a poor prognostic factor in breast cancer and, thus, presents a significant clinical challenge. The mechanisms of chemoresistance involve multiple complex biological processes. This study aims to identify common contributory factors to chemoresistance in breast cancer by comparing protein expression profiles of chemosensitive MCF-7 breast cancer cells and cells resistant to two different commonly used anti-cancer drugs (adriamycin and paclitaxel). Expression of the ATP binding cassette transporter, P-glycoprotein (P-gp), in breast tumours has previously been found to correlate with poor prognosis in vivo and, accordingly, we confirmed overexpression of P-gp in both adriamycin- and paclitaxel-resistant MCF-7 cells. Using two-dimensional gel electrophoresis and MALDI-TOF peptide mass fingerprinting, we identified 20 proteins differentially expressed between chemosensitive, adriamycin-resistant and paclitaxel-resistant MCF-7 cells. Cytokeratin-8, keratin-19, Hsp-27, 14-3-3 epsilon, annexin-A2 and phosphoglycerate kinase-1 showed altered expression in both adriamycin- and paclitaxel-resistant cells. Validation of a number of these changes was confirmed by Western blotting. Our findings provide further insights into the complex mechanisms of chemoresistance, as well as representing an attractive starting point for the identification of potential protein biomarkers to predict response to chemotherapy in breast cancer in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".