Claudin-2 Promotes Breast Cancer Liver Metastasis by Facilitating Tumor Cell Interactions with Hepatocytes
Bibliographic record
Abstract
We previously identified claudin-2 as a functional mediator of breast cancer liver metastasis. We now confirm that claudin-2 levels are elevated in liver metastases, but not in skin metastases, compared to levels in their matched primary tumors in patients with breast cancer. Moreover, claudin-2 is specifically expressed in liver-metastatic breast cancer cells compared to populations derived from bone or lung metastases. The increased liver tropism exhibited by claudin-2-expressing breast cancer cells requires claudin-2-mediated interactions between breast cancer cells and primary hepatocytes. Furthermore, the reduction of the claudin-2 expression level, either in cancer cells or in primary hepatocytes, diminishes these heterotypic cell-cell interactions. Finally, we demonstrate that the first claudin-2 extracellular loop is essential for mediating tumor cell-hepatocyte interactions and the ability of breast cancer cells to form liver metastases in vivo. Thus, during breast cancer liver metastasis, claudin-2 shifts from acting within tight-junctional complexes to functioning as an adhesion molecule between breast cancer cells and hepatocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".