An integrative approach to identify YB‐1‐interacting proteins required for cisplatin resistance in MCF7 and MDA‐MB‐231 breast cancer cells
Bibliographic record
Abstract
The Y-box binding protein 1 (YB-1) is a multifunctional protein that affects transcription, splicing, and translation. Overexpression of YB-1 in breast cancers causes cisplatin resistance. The exact mechanism by which YB-1 confers cisplatin resistance is unknown. The aim of the present study was to identify, using mass spectrometry, proteins that interact with YB-1 that are important for cisplatin resistance in two breast cancer cell lines, namely MCF7 and MDA-MB-231. A tagged YB-1 construct was used to identify proteins interacting directly with YB-1 in breast cancer cells. We then focused on proteins that are potentially involved in breast cancer progression based on the ONCOMINE public microarray database. Genes encoding for these YB-1-interacting proteins were examined in the public NCBI comparative genomic hybridization database to determine whether they are localized to regions of chromosomes that are rearranged in breast cancer tissues. From these analyses, we generated a list of proteins potentially involved in cisplatin resistance. Cisplatin dose-response curves were constructed in MCF7 and MDA-MB-231 transfected with four siRNA corresponding to each of these YB-1 interactors to identify proteins significantly affecting cisplatin sensitivity upon gene silencing. Depletion of only the X-linked ribosomal protein S4 (RPS4X) resulted in consistent resistance to cisplatin in both cell lines with at least three different siRNA sequences against RPS4X. Further analyses indicated that the knock down of RPS4X decreased DNA synthesis, induced cisplatin resistance, and is equivalent to the overexpression of YB-1 in both MCF7 and MDA-MB-231 cells. These results suggest that the RPS4X/YB-1 complex is a significant potential target to counteract cisplatin resistance in breast cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".