Induction of epithelial-mesenchymal transition (EMT) and Gli1 expression in head and neck squamous cell carcinoma (HNSCC) spheroid cultures
Bibliographic record
Abstract
Tumor microenvironment provides a specialized niche in which a population of stem-like cells is enriched and contributes to cancer progression. Moreover, cancer stem cell (CSC) phenotype has been associated with epithelial-mesenchymal transition (EMT). Here we investigated the effect of tumor microenvironment on the phenotypic characteristics of head and neck cancer cells and expression of CSC markers using a three-dimensional (3D), spheroid, culture system of CAL33 cell line from human tongue squamous cell carcinoma. CAL33 cells derived from 2D monolayer cultures were grown in spheroid cultures containing serum-free medium (epidermal growth factor [EGF], fibroblast growth factor [FGF], and insulin). Adherent CAL33 cells from spheroids or standard control cultures were grown in the presence/absence of serum in combination with hypoxia/normoxia. Markers of EMT, CSC, and hypoxia were analyzed either by Western blotting, immunofluorescence, or reverse transcription quantitative PCR. Spheroid cultures showed hypoxic microenvironment (high carbonic anhydrase IX [CAIX] expression), mesenchymal-like characteristics (reduced E-cadherin and increased vimentin and N-cadherin expression, presence of larger colonies comprised of larger, spread cells with lower density), and increased expression of the CSC marker glioma-associated oncogene homolog 1 (Gli1). These effects were recapitulated in serum-free adherent CAL33 cells maintained for prolonged periods in hypoxia (1% O2) but, in contrast, were completely abolished by the presence of serum. Overall, we found that a combination of hypoxia, EGF and FGF was essential to induce the EMT in adherent CAL33 cell cultures. The addition of serum rapidly reverts the EMT of cells, affects CSC phenotype and, thus, prevents the detection of such cells in tumor cell lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".