Modulating Three-Dimensional Microenvironment with Hyaluronan of Different Molecular Weights Alters Breast Cancer Cell Invasion Behavior
Bibliographic record
Abstract
Hyaluronan (HA), a polymer with various molecular weights (MW) found in tumor microenvironments, is associated with malignant progression of breast cancer. Reducing the amount of high-MW HA in the microenvironment by hyaluronidase is a promising approach for breast cancer treatment. However, whether the generation of HA fragments negatively affects breast cancer cells remains to be determined. Furthermore, HA forms three-dimensional (3D) networks by cross-linking with other extracellular molecules to function. Therefore, a model mimicking the cross-linked HA network is required to determine the effect of HA fragments on breast cancer cells. To clarify the differential roles of low (HA35) versus high (HA117) MW HA on cancer cell phenotype, a 3D culture system was set up by covalently cross-linking HA with alginate and investigating the behavior of 4T-1 and SKBR3 breast cancer cells alongside a two-dimensional (2D) control. The results show the invasion and migration abilities of 4T-1 and SKBR3 cells are significantly enhanced by the presence of HA35 but inhibited by HA117 in both 2D monolayers and 3D spheroids. The differential effects of HA35 and HA117 on cancer cell epithelial-mesenchymal transition (EMT) phenotype were further confirmed in terms of differential regulation of E-cadherin and vimentin as important EMT markers at both the cellular and mRNA levels. Additional experiments show the CD44-Twist signaling pathway might be involved in the differential effects of HA35 and HA117. These results have important implications with respect to understanding the role of HA in breast cancer development and for the design of therapeutic approaches based on the eradication of HA with hyaluronidase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".