Disruption of the <i>Lcn2</i> gene in mice suppresses primary mammary tumor formation but does not decrease lung metastasis
Bibliographic record
Abstract
Based largely on studies in xenograft models, lipocalin-2 (Lcn2) has been implicated in the progression of multiple types of human tumors, including breast cancer. Here we examine the role of Lcn2 in mammary tumorigenesis and lung metastasis using an in vivo molecular genetics approach. We crossed a well-characterized transgenic mouse model of breast cancer, the MMTV-PyMT (mouse mammary tumor virus-polyoma middle T antigen) mouse, with two independent gene-targeted Lcn2(-/-) mouse strains of the 129/Ola or C57BL/6 genetic background. The onset and progression of mammary tumor development and lung metastasis in the female progeny of these crosses were monitored over a 20-week period. Female Lcn2(-/-)MMTV-PyMT mice of the 129/Ola background (Lcn2(-/-)PyMT(129)) showed delayed onset of mammary tumors, and both Lcn2(-/-)PyMT(129) mice and Lcn2(-/-)MMTV-PyMT mice of the C57BL/6 background (Lcn2(-/-)PyMT(B6)) exhibited significant decreases in multiplicity and tumor burden (approximately 2- to 3-fold), as measured by total tumor weight and volume. At the molecular level, mammary tumors derived from Lcn2(-/-)PyMT(B6) females showed reduced matrix metalloproteinase-9 (MMP-9) activity and a lack of high molecular weight MMP activity. However, although increased MMP-9 activity has been linked to tumor progression, neither Lcn2(-/-)PyMT(B6) nor Lcn2(-/-)PyMT(129) female mice showed a reduction in lung metastases compared to Lcn2(+/+)PyMT controls. Our results demonstrate, using an in vivo animal model approach, that Lcn2 is a potent inducer of mammary tumor growth but not a significant promoter of lung metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".