Synaptotagmin 13 Is Highly Expressed in Estrogen Receptor-Positive Breast Cancer
Bibliographic record
Abstract
Background: Accumulating evidence indicates tumor-promoting roles of synaptotagmin 13 (SYT13) in several cancers; however, no studies have investigated its expression in breast cancer (BC). This study aimed to clarify the significance of SYT13 in BC. Methods: SYT13 mRNA expression levels were evaluated in BC cell lines. Polymerase chain reaction (PCR) array analysis was conducted to determine the correlation between expression levels of SYT13 and other tumor-associated genes. Then, the association of SYT13 expression levels in the clinical BC specimens with patients’ clinicopathological factors was evaluated. These findings were subsequently validated using The Cancer Genome Atlas (TCGA) database. Results: Among 13 BC cell lines, estrogen receptor (ER)-positive cells showed higher SYT13 mRNA levels than ER-negative cells. PCR array analysis revealed positive correlations between SYT13 and several oncogenes predominantly expressed in ER-positive BC, such as estrogen receptor 1, AKT serine/threonine kinase 1, and cyclin-dependent kinases 4. In 165 patients, ER-positive specimens exhibited higher SYT13 mRNA expression levels than ER-negative specimens. The TCGA database analysis confirmed that patients with ER-positive BC expressed higher SYT13 levels than ER-negative patients. Conclusion: This study suggests that SYT13 is highly expressed in ER-positive BC cells and clinical specimens, and there is a positive association of SYT13 with the ER signaling pathways.
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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 | 0.001 |
| 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".