xCT knockdown in human breast cancer cells delays onset of cancer-induced bone pain
Bibliographic record
Abstract
Cancers in the bone produce a number of severe symptoms including pain that compromises patient functional status, quality of life, and survival. The source of this pain is multifaceted and includes factors secreted from tumor cells. Malignant cells release the neurotransmitter and cell-signaling molecule glutamate via the oxidative stress-related cystine/glutamate antiporter, system x C − , which reciprocally imports cystine for synthesis of glutathione and the cystine/cysteine redox cycle. Pharmacological inhibition of system x C − has shown success in reducing and delaying the onset of cancer pain-related behavior in mouse models. This investigation describes the development of a stable siRNA-induced knockdown of the functional trans-membrane system x C − subunit xCT ( SLC7A11) in the human breast cancer cell line MDA-MB-231. Clones were verified for xCT knockdown at the transcript, protein, and functional levels. RNAseq was performed on a representative clone to comprehensively examine the transcriptional cellular signature in response to xCT knockdown, identifying multiple differentially regulated factors relevant to cancer pain including nerve growth factor, interleukin-1, and colony-stimulating factor-1. Mice were inoculated intrafemorally and recordings of pain-related behaviors including weight bearing, mechanical withdrawal, and limb use were performed. Animals implanted with xCT knockdown cancer cells displayed a delay until the onset of nociceptive behaviors relative to control cells. These results add to the body of evidence suggesting that a reduction in glutamate release from cancers in bone by inhibition of the system x C − transporter may decrease the severe and intractable pain associated with bone metastases.
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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) | 0.002 | 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".