Identification of MFGE8 and KLK5/7 as mediators of breast tumorigenesis and resistance to COX-2 inhibition
Bibliographic record
Abstract
BACKGROUND: Cyclooxygenase 2 (COX-2) promotes stemness in triple negative breast cancer (TNBC), highlighting COX-2 as a promising therapeutic target in these tumors. However, to date, clinical trials using COX-2 inhibitors in breast cancer only showed variable patient responses with no clear significant clinical benefits, suggesting underlying molecular mechanisms contributing to resistance to COX-2 inhibitors. METHODS: By combining in silico analysis of human breast cancer RNA-seq data with interrogation of public patient databases and their associated transcriptomic, genomic, and clinical profiles, we identified COX-2 associated genes whose expression correlate with aggressive TNBC features and resistance to COX-2 inhibitors. We then assessed their individual contributions to TNBC metastasis and resistance to COX-2 inhibitors, using CRISPR gene knockout approaches in both in vitro and in vivo preclinical models of TNBC. RESULTS: We identified multiple COX-2 associated genes (TPM4, RGS2, LAMC2, SERPINB5, KLK7, MFGE8, KLK5, ID4, RBP1, SLC2A1) that regulate tumor lung colonization in TNBC. Furthermore, we found that silencing MFGE8 and KLK5/7 gene expression in TNBC cells markedly restored sensitivity to COX-2 selective inhibitor both in vitro and in vivo. CONCLUSIONS: Together, our study supports the establishment and use of novel COX-2 inhibitor-based combination therapies as future strategies for TNBC treatment.
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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) | 0.001 | 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".