<i>In vivo</i> evidence supporting a metastasis suppressor role for <i>Stard13</i> (<i>Dlc2</i>) in <i>ErbB2</i> (<i>Neu</i>) oncogene induced mouse mammary tumors
Bibliographic record
Abstract
Overexpression of dominant oncogenes and the loss of tumor suppressor genes are basic genetic events in the acquisition of the malignant phenotype. The erb-b2 receptor tyrosine kinase 2 (ERBB-2) proto-oncogene is overexpressed in 20-30% of human breast cancers. The StAR related lipid transfer domain containing 13 gene (STARD13), also known as Deleted in Liver Cancer-2 (DLC-2), maps to chromosome band 13q12.3 and is frequently downregulated in human cancers, including 72% of breast cancers. It encodes a RhoGAP protein with sterile α motif (SAM) and StAR-related lipid transfer (START) domains. The objective of this study was to determine if loss of Stard13 plays a role in mammary tumor progression using transgenic mice expressing the activated ErbB-2 (Neu) oncogene and Cre recombinase (NIC) in mammary epithelium under transcriptional control of the murine mammary tumor virus (MMTV) promoter (MMTV-NIC). These mice were crossed with a conditional Stard13 knockout mouse (floxed exon 3), resulting in simultaneous Neu expression and Stard13 deletion, specifically in the mammary epithelium. We found that loss of Stard13 did not alter tumor growth nor significantly modify overall survival and tumor free survival. However, there was an increase in the total number of lung metastases in the Stard13 heterozygous or homozygous mice compared with the parental MMTV-NIC strain. Altogether our results indicate that Stard13 acts as a metastasis suppressor rather than a tumor suppressor gene, in Neu oncogene induced mammary tumorigenesis.
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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".