Bidirectional cross talk between uveal melanoma cells and hepatic myofibroblasts promotes inflammation‐induced chemokines expression
Bibliographic record
Abstract
Purpose Uveal melanoma (UM) is the most common primary ocular neoplasm in adults. Its cause is largely unknown, and no risk factors have yet been identified. The metastatic disease develops in up to 50% of patients, usually involving the liver. Treatment only rarely prolongs survival, because metastases are highly resistant to most chemotherapeutic agents. Tumor cells may modulate the functions of surrounding cells to facilitate their own growth, survival, invasion, and metastasis. This study was conducted to investigate the role of hepatic microenvironment on UM cells (UMC). Methods Here, we utilized metastatic (Omm2.3) and non‐metastatic (Mel270) UMC in coculture with hepatocyte‐stellate cells (HSC) LX2. The transcriptomic study was performed by microarray assay. Expression of relevant genes was measured by qPCR. Cytokines were quantified by Elisa test. Cell proliferation was assessed by MTT staining. Extracellular matrix components were evaluated by quantitative cell adhesion assay. Results Hepatic microenvironment increased the expression of numerous genes. However, the number of genes overexpressed in metastatic co‐cultures is three‐times higher than in non‐metastatic cocultures, demonstrating that hepatic microenvironment has more impact on metastatic UMC. Over‐expressed genes in coculture were linked to inflammation and included several interleukins. In addition, UMC‐HSC crosstalk generated expression of cell adhesion receptors, particularly by increasing fibronectin. In contrast, hepatic microenvironment had no effect on cell proliferation. Conclusions Our results provide evidence for an important role of inflammation in the progression of metastatic UM. Therefore, the inflammatory characteristics of the tumor microenvironment might offer therapeutic opportunities.
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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) | 0.002 | 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".