Highly tumorigenic hepatocellular carcinoma cell line with cancer stem cell-like properties
Bibliographic record
Abstract
There are limited numbers of models to study hepatocellular carcinoma (HCC) in vivo in immunocompetent hosts. In an effort to develop a cell line with improved tumorigenicity, we derived a new cell line from Hepa1-6 cells through an in vivo passage in C57BL/6 mice. The resulting Dt81Hepa1-6 cell line showed enhanced tumorigenicity compared to Hepa1-6 with more frequent (28±12 vs. 0±0 lesions at 21 days) and more rapid tumor development (21 (100%) vs. 70 days (10%)) in C57BL/6 mice. The minimal Dt81Hepa1-6 cell number required to obtain visible tumors was 100,000 cells. The Dt81Hepa1-6 cell line showed high hepatotropism with subcutaneous injection leading to liver tumors without development of tumors in lungs or spleen. In vitro, Dt81Hepa1-6 cells showed increased anchorage-independent growth (34.7±6.8 vs. 12.3±3.3 colonies; P<0.05) and increased EpCAM (8.7±1.1 folds; P<0.01) and β-catenin (5.4±1.0 folds; P<0.01) expression. A significant proportion of Dt81Hepa1-6 cells expressed EpCAM compared to Hepa1-6 (34.8±1.1% vs 0.9±0.13%; P<0.001). Enriched EpCAM+ Dt81Hepa1-6 cells led to higher tumor load than EpCAM- Dt81Hepa1-6 cells (1093±74 vs 473±100 tumors; P<0.01). The in vivo selected Dt81Hepa1-6 cell line shows high liver specificity and increased tumorigenicity compared to Hepa1-6 cells. These properties are associated with increased expression of EpCAM and β-catenin confirming that EpCAM+ HCC cells comprise a subset with characteristics of tumor-initiating cells with stem/progenitor cell features. The Dt81Hepa1-6 cell line with its cancer stem cell-like properties will be a useful tool for the study of hepatocellular carcinoma in vivo.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".