LYVE-1 enhances the adhesion of HS-578T cells to COS-7 cells via hyaluronan
Bibliographic record
Abstract
PURPOSE: Lymphatic vessel endothelial hyaluronan receptor (LYVE-1), a specific molecular marker for lymph systems, has only one known ligand, hyaluronan (HA). Many studies have reported that HA, on the surface of tumor cells, is associated with the metastatic behavior of cancer cells. The interaction of LYVE-1 with HA may facilitate tumor cell attachment and enhance dissemination of tumor cells to lymph nodes. The aim of this study was to explore the biological function of LYVE-1 and to determine whether the interaction between LYVE-1 and HA was directly involved in the adhesion of tumor cells to lymphatic vessels. METHODS: COS-7 cells were transfected with cDNA encoding LYVE-1 and expressed LYVE-1 assembled exogenously added HA. A high HA-expressing breast cancer cell line, HS-578T, was chosen to be the upper layer of cells that adhered to a lower layer of COS-7(LYVE-1(+)), COS-7(pEGFP-N1), or COS-7 cells for the adhesion analyses. The mechanism of adhesion was investigated by an experiment in which the HA on the surface of HS-578T cells was digested by Streptomyces hyaluronidase before the HS-578T cells were allowed to adhere to COS-7(LYVE-1(+)) cells. RESULTS: Results showed that more adhesion was observed between HS-578T and COS-7(LYVE-1(+)) cells, while less adhesion was observed between HS-578T cells and either COS-7(pEGFP-N1) or COS-7 cells (p < 0.01). Decreased HA on the HS-578T cell surface could reduce the adhesion of HA-578T cells to COS-7(LYVE-1(+)) cells suggesting that this adhesion might be mediated through HA. CONCLUSION: Our results suggest that LYVE-1 allows the adhesion of tumor cells through the interaction of HA on the tumor cell membrane with LYVE-1.
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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) | 0.002 | 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".