Expression of Galectin-7 Is Induced in Breast Cancer Cells by Mutant p53
Bibliographic record
Abstract
Galectin-7 was initially described as a marker of epithelial differentiation expressed in the stratified epithelium of various tissues. Like other members of the galectin family, its expression level is often significantly altered in cancer cells. In breast cancer, its expression is significantly augmented in aggressive molecular subtypes, most notably in estrogen receptor-negative tumors and in cell lines with a basal-like phenotype. Studies using experimental mouse models have further shown high expression of galectin-7 was sufficient to increase the metastatic behavior of poorly metastatic breast cancer cells, rendering them more resistant to apoptosis. This expression pattern in breast cancer cells is unexpected because galectin-7 was originally identified as a p53-induced gene. To address this paradox, we have examined the molecular mechanisms regulating galectin-7 in breast cancer cells. Our results showed that transfection of breast cancer cells with expression vectors encoding mutant p53 was sufficient to induce galectin-7 at both mRNA and protein levels. Doxorubicin treatment of breast cancer cells harboring a mutant p53 also induced galectin-7. This induction was specific since knockdown of endogenous mutant p53 inhibited doxorubicin-induced galectin-7 expression. The p53-induced galectin-7 expression in breast cancer cells correlated with increased NF-κB activity and was inhibited by NF-κB inhibitors, indicating that the ability of mutant p53 to induce galectin-7 was dependent on NF-κB activity. The implication of NF-κB was further supported by data showing that NF-κB bound to the endogenous galectin-7 promoter and that TNFα-induced galectin-7 expression was abolished by NF-κB inhibitors. Taken together, our data provide an explanation to the observed high galectin-7 expression levels in cancer cells and suggest that galectin-7 could be part of a common pathway used by mutant p53 to promote cancer progression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".