Comparative proteomics study of proteins involved in induction of higher rates of cell death in mitoxantrone-resistant breast cancer cells MCF-7/MX exposed to TNF-α.
Bibliographic record
Abstract
OBJECTIVES: Resistance to medications is one of the main complications in chemotherapy of cancer. It has been shown that some multidrug resistant cancer cells indicate more sensitivity against cytotoxic effects of TNF-α compared to their parental cells. Our previous findings indicated vulnerability of the mitoxantrone-resistant breast cancer cells MCF-7/MX to cell death induced by TNF-α compared to the parent cells MCF-7. In this study, we performed a comparative proteomics analysis for identification of proteins involved in induction of higher susceptibility of MCF-7/MX cells to cytotoxic effect of TNF-α. MATERIALS AND METHODS: Intensity of protein spots in 2D gel electrophoresis profiles of MCF-7 and MCF-7/MX cells were compared with Image Master Platinum 6.0 software. Selected differential protein-spots were identified with MALDI-TOF/TOF mass spectrometry and database searching. Pathway analyses of identified proteins were performed using PANTHER, KEGG PATHWAY, Gene MANIA and STRING databases. Western blot was performed for confirmation of the proteomics results. RESULTS: Our results indicated that 48 hr exposure to TNF-α induced 87% death in MCF-7/MX cells compared to 19% death in MCF-7 cells. Forty landmarks per 2D gel electrophoresis were matched by Image Master Software. Six proteins were identified with mass spectrometry. Western blot showed that 14-3-3γ and p53 proteins were expressed higher in MCF-7/MX cells treated with TNF-α compared to MCF-7 cells treated with TNF-α. CONCLUSION: Our results showed that 14-3-3 γ, prohibitin, peroxiredoxin 2 and P53 proteins which were expressed differentially in MCF-7/MX cells treated with TNF-α may involve in the induction of higher rates of cell death in these cells compared to TNF-α-treated MCF-7 cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".