An Atypical System for Studying Epithelial-Mesenchymal Transition in Hepatocellular Carcinoma
Bibliographic record
Abstract
Intrahepatic and extrahepatic metastases are frequently detected in hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is believed to drive metastasis. There are not many well-established model systems to study EMT in HCC. Here we identified an atypical EMT while characterizing a population of mesenchymal cells in Huh7.5 hepatoma cell cultures. Cells with distinct morphology appeared during geneticin treatment of Huh7.5 cultures. Molecular characterization of geneticin resistant Huh7.5M cells confirmed EMT. Huh7.5M cells expressed cancer stem cell markers. p38MAPK and ERK1/2 were substantially activated in Huh7.5M cells. Their Inhibition elevated E-Cadherin expression with concerted suppression of Vimentin and anchorage independent growth in Huh7.5M cells. TGFβ could not induce EMT in Huh7.5 cultures, but enriched mesenchymal populations, similar to geneticin. Huh7.5M cells formed more aggressive solid tumors, primarily comprising cells with epithelial morphology, in nude mice. Canonical EMT-TFs did not participate in this atypical EMT, indicating that the established canonical EMT-TFs do not drive every EMT and there is a dire need to identify additional factors. The system that we characterized is a unique model to study EMT, MET and biphasic TGFβ signaling in HCC and offers considerable potential to facilitate more insightful studies on deeper questions in tumor metastasis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".