Stage-specific tumor microenvironment dynamics and cancer-associated fibroblast profiling in MBL-6 mouse models of breast cancer
Bibliographic record
Abstract
Breast cancer remains the most prevalent malignancy among women, necessitating the development of novel therapeutic strategies. Experimental animal models that closely mimic human breast cancer are crucial for advancing these therapies. This study utilized the criteria of the tumour, node, metastasis (TNM) staging system and variations in metabolic rates to develop models representing stages II and IV of human breast cancer, using the MBL-6 mouse breast cancer cell line. We assessed tumor growth curves in vivo and investigated distant metastasis to organs such as the liver, lungs, lymph nodes, and spleen. Carcinoma-associated fibroblasts (CAFs) were isolated, and their proliferation rates, inflammatory enzyme expression, and matrix metalloproteinase levels were compared between stages II and IV. By analyzing tumor kinetics and metabolic differences, we were able to predict tumor size and progression at each stage. Our results revealed that CAFs isolated from both stages exhibited similar phenotypic characteristics. However, CAFs from stage II tumors showed higher expression of indoleamine 2,3-dioxygenase 1 (IDO1), while those from stage IV tumors had higher levels of inducible nitric oxide synthase (iNOS). These distinct expression patterns suggest unique microenvironmental features at different stages of tumor progression. Further investigation of the cancer microenvironment may provide valuable insights for selecting targeted therapies and improving disease management.
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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 | 0.000 | 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) | 0.001 | 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".