Junctional adhesion molecule-A is co-expressed with HER2 in breast tumors and acts as a novel regulator of HER2 protein degradation and signaling
Bibliographic record
Abstract
Junctional adhesion molecule-A (JAM-A) is a membranous cell-cell adhesion protein involved in tight-junction formation in epithelial and endothelial cells. Its overexpression in breast tumors has recently been linked with increased risk of metastasis. We sought to identify if JAM-A overexpression was associated with specific subtypes of breast cancer as defined by the expression of human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER) and progesterone receptor. To this end, JAM-A immunohistochemistry was performed in two breast cancer tissue microarrays. In parallel, cross-talk between JAM-A, HER2 and ER was examined in several breast cell lines, using complementary genetic and pharmacological approaches. High JAM-A expression correlated significantly with HER2 protein expression, ER negativity, lower patient age, high-grade breast cancers, and aggressive luminal B, HER2 and basal subtypes of breast cancer. JAM-A and HER2 were co-expressed at high levels in vitro in SKBR3, UACC-812, UACC-893 and MCF7-HER2 cells. Knockdown or functional antagonism of HER2 did not alter JAM-A expression in any cell line tested. Interestingly, however, JAM-A knockdown decreased HER2 and ER-α expression, resulting in reduced levels of phospho-(active) AKT without an effect on the extracellular signal-related kinase phosphorylation. The downstream effects of JAM-A knockdown on HER2 and phospho-AKT were partially reversed upon treatment with the proteasomal inhibitor MG132. We conclude that JAM-A is co-expressed with HER2 and associates with aggressive breast cancer phenotypes. Furthermore, we speculate that JAM-A may regulate HER2 proteasomal degradation and activity, potentially offering a promise as a therapeutic target in HER2-positive breast cancers.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".