Tumor promotion through the mesenchymal stem cell compartment in human hepatocellular carcinoma
Bibliographic record
Abstract
Although the infiltration of mesenchymal stem (stromal) cells (MSCs) into different tumors is widely recognized in animal models, the question whether these MSCs have a positive or negative effect on disease progression remains unanswered. The aim of this study is to investigate whether human hepatocellular carcinoma (HCC) harbors MSCs and whether these MSCs affect tumor growth. We observed that cells capable of differentiation into both adipocyte and osteocyte lineages and expressing MSC markers can be cultured from surgically resected HCC tissues. In situ staining of human HCC tissues with a STRO-1 antibody showed that the tumor and tumor-stromal region are significantly enriched with candidate MSCs compared with adjacent tissue (n = 12, P < 0.01). In mice, coengraftment of a human HCC cell line (Huh7) with MSCs resulted in substantially larger tumors compared with paired engraftment of Huh7 alone (n = 8, P < 0.01). Consistently, coculturing Huh7 with irradiated MSCs significantly increased the number and the size of colonies formed. This enhancement of Huh7 colony formation was also observed by treatment of MSC-conditioned medium (MSC-CM), suggesting that secreted trophic factors contribute to the growth-promoting effects. Genome-wide gene expression array and pathway analysis confirmed the upregulation of cell growth and proliferation-related processes and downregulation of cell death-related pathways by treatment of MSC-CM in Huh7 cells. In conclusion, these results show that MSCs are enriched in human HCC tumor compartment and could exert trophic effects on tumor cells. Thus, targeting of HCC tumor MSCs may represent a new avenue for therapeutic intervention.
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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 | 0.000 | 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 | 0.000 |
| 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".