Effect of adipose-derived mesenchymal stem cells on hepatocellular carcinoma: <i>In vitro</i> inhibition of carcinogenesis
Bibliographic record
Abstract
AIM: To investigate the effect of adipose-derived mesenchymal stem cells (ADMSCs) and their conditioned media (CM) on hepatocellular carcinoma (HCC) cell tumorigenesis. METHODS: The proliferation rate of HepG2 and PLC-PRF-5 HCC cancer cells was measured using the trypan blue exclusion method and confirmed using the cell-counting kit 8 (commonly known as CCK-8) assay. Apoptosis was detected by flow cytometry using annexin V-FITC. Protein and mRNA expression was quantified by ELISA and real time PCR, respectively. Migration and invasion rates were performed by Transwell migration and invasion assays. Wound healing was examined to confirm the data obtained from the migration assays. RESULTS: Our data demonstrated that when co-culturing HCC cell lines with ADMSCs or treating them with ADMSC CM, the HCC cell proliferation rate was significantly inhibited and the apoptosis rate increased. The decreased proliferation rate was accompanied by an upregulation of P53 and Retinoblastoma mRNA and a downregulation of c-Myc and hTERT mRNA levels. More notably, ADMSCs and their CM suppressed the expression of the two important markers of HCC carcinogenicity, alpha-fetoprotein and Des-gamma-carboxyprothrombin. In addition, the migration and invasion levels of HepG2 and PLC-PRF-5 cells significantly decreased, potentially through increased expression of the tissue inhibitor metalloproteinases TIMP-1, TIMP-2 and TIMP-3. CONCLUSION: These findings shed new light on a protective and therapeutic role for ADMSCs and their CM in controlling HCC invasiveness and carcinogenesis.
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) | 0.000 | 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 | 0.001 |
| 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".