Intracellular galectin-7 expression in cancer cells results from an autocrine transcriptional mechanism and endocytosis of extracellular galectin-7
Bibliographic record
Abstract
The β-galactoside binding protein galectin-7 (gal-7) is constitutively expressed at abnormally high levels in the outside milieu and intracellular compartments of many types of epithelial cancer cells, most notably in aggressive forms of ovarian and breast cancer. It is thus of utmost importance to understand how gal-7 traffics between both intracellular and extracellular compartments to develop novel drugs that target the protumorigenic functions of galectin-7. In the present work, we report that extracellular gal-7 plays a central role in controlling intracellular gal-7 in cells. It does so via two distinct yet complementary mechanisms: firstly by increasing the transcriptional activation of lgals7 gene transcription, and secondly via re-entry into the cells. Increased mRNA levels were dose- and time-dependent and occur in all cell lines tested, including ovarian and breast cancer cell lines. Addition of recombinant gal-7 to MDA-MB-231 transfected with a luciferase reporter vector containing response elements of the lgals7 promoter indicated that increased mRNA level of lgals7 occurs via de novo gene transcription. Re-entry of extracellular gal-7 inside cells was rapid, and reached cytosolic and mitochondrial compartments. Taken together, these findings reveal the existence of a positive self-amplification pathway that regulates intracellular gal-7 expression in breast and ovarian cancer cells.
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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".