Fibroblasts activated by miRs-185-5p, miR-652-5p, and miR-1246 shape the tumor microenvironment in triple-negative breast cancer via PATZ1 downregulation
Bibliographic record
Abstract
The intricate interplay between epithelial and fibroblast cells within the tumor microenvironment plays a crucial role in driving triple-negative breast cancer progression. This crosstalk involves the exchange of various signaling molecules, including growth factors, cytokines, extracellular matrix components, and extracellular vesicles. Recently, we demonstrated that triple-negative breast cancer extracellular vesicles carry and release a specific combination of miRs, including miR-185-5p, miR-652-5p, and miR-1246 (from here on, referred as combo-miRs), into normal fibroblasts, effectively reprogramming them into cancer-associated fibroblasts. Here, we show that the conditioned medium from the fibroblasts activated by combo-miRs exerts a pro-tumorigenic effect on epithelial cells, enhancing the viability and migratory potential while driving increased invasiveness in patient-derived breast cancer organoids. A proteomic analysis of conditioned medium from combo-miRs activated fibroblasts revealed 76 significantly upregulated secreted proteins compared to control. Bioinformatic analysis identified the transcriptional factor PATZ1 as a potential regulator of the 12 most highly upregulated proteins. Consistently, in-silico predictions and in vitro experiments confirmed that PATZ1 is a direct target of miR-185-5p and miR-652-5p. The downregulation of PATZ1 by these miRNAs led to increased levels of the secreted proteins in the conditioned medium from combo-miRs activated fibroblasts. Furthermore, the conditioned medium from PATZ1-knockout mesenchymal embryonic fibroblasts and normal fibroblasts with silenced PATZ1 similarly enhanced the migratory potential of MCF10A cells, further supporting the critical role of PATZ1 in regulating tumor-promoting mechanisms. These findings provide valuable insights into the dynamics of the TME in TNBC, highlighting combo-miRs and PATZ1 as promising targets for future therapeutic interventions.
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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".