Expression and Functions of Galectin-7 in Human and Murine Melanomas
Bibliographic record
Abstract
The identification of galectin-7 as a p53-induced gene and its ability to induce apoptosis in many cell types support the hypothesis that galectin-7 has strong antitumor activity. This has been well documented in colon cancer. However, in some cases, such as breast cancer and lymphoma, its high expression level correlates with aggressive subtypes of cancer, suggesting that galectin-7 may have a dual role in cancer progression. In fact, in breast cancer, overexpression of galectin-7 alone is sufficient to promote metastasis to the bone and lung. In the present work, we investigated the expression and function of galectin-7 in melanoma. An analysis of datasets obtained from whole-genome profiling of human melanoma tissues revealed that galectin-7 mRNA was detected in more than 90% of biopsies of patients with nevi while its expression was more rarely found in biopsies collected from patients with malignant melanoma. This frequency, however, was likely due to the presence of normal epidermis tissues in biopsies, as shown our studies at the protein level by immunohistochemical analysis. Using the experimental melanoma B16F1 cell line, we found that melanoma cells can express galectin-7 at the primary tumor site and in lung metastasis. Moreover, we found that overexpression of galectin-7 increased the resistance of melanoma cells to apoptosis while inducing de novo egr-1 expression. Overexpression of galectin-7, however, was insufficient to modulate the growth of tumors induced by the subcutaneous injection of B16F1 cells. It also failed to modulate the dissemination of B16F1 cells to the lung.
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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 | 0.001 | 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".