Exposure of Tumor-Associated Macrophages to Apoptotic MCF-7 Cells Promotes Breast Cancer Growth and Metastasis
Bibliographic record
Abstract
Tumor-associated macrophages (TAMs) have been found to be associated with the progression and metastasis of breast cancer. To clarify the mechanisms underlying the crosstalk between TAMs and cancer stem cells (CSCs) in breast cancer recurrence and metastasis, we used a co-culture model of macrophages and apoptotic human breast cancer cell line MCF-7 cells to investigate the effects of TAMs on MCF-7 in vitro and in vivo. Macrophages co-cultured with apoptotic MCF-7 had increased tumor growth and metastatic ability in a nude mouse transplantation assay. The macrophages exposed to apoptotic cells also induce an increase in the proportion of CD44+/CD24- cancer stem-like cells, as well as their proliferative ability accompanied with an increase in mucin1 (MUC1) expression. During this process, macrophages secreted increased amounts of interleukin 6 (IL-6) leading to increased phosphorylation of signal transducers and activators of transcription 3 (STAT3), which likely explains the increased transcription of STAT3 target genes such as TGF-β1 and HIF-1α. Our results indicate that when cancer cells endure chemotherapy induced apoptosis, macrophages in their microenvironment can then activate cancer stem cells to promote cancer growth and metastasis by secreting IL-6, which activates STAT3 phosphorylation to regulate the transcription of its downstream target genes.
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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 | 0.000 |
| 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".