Expression of somatostatin receptors in normal and cirrhotic human liver and in hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Somatostatin analogues have been used with conflicting results to treat advanced hepatocellular carcinoma (HCC). The aim of this study was to investigate expression of somatostatin receptor (SSTR) subtypes in human liver, and to examine the effect of selective SSTR agonists on proliferation, apoptosis, and migration of hepatoma cells (HepG2, HuH7) and hepatic stellate cells (HSCs). METHODS: Expression of SSTRs in cell lines, normal and cirrhotic liver, and HCC was examined by immunohistochemistry and reverse transcription-polymerase chain reaction. Effects of SSTR agonists on proliferation and apoptosis of tumour cells and HSCs were assessed by the 5-bromo-2' deoxyuridine and TUNEL methods, respectively. The influence of SSTR agonists on migration was investigated using Boyden chambers. RESULTS: In normal liver, both hepatocytes and HSCs were negative for all five SSTRs. Cirrhotic liver and HCC as well as cultured hepatoma cells and HSCs expressed all five SSTRs, both at the protein and mRNA levels, except for HuH7 cells which did not immunoreact with SSTR3. None of the agonists influenced proliferation or apoptosis. However, compared with untreated cells, L-797,591, an SSTR1 agonist, reduced migration of HepG2, HuH7, and HSCs significantly to 88 (7)% (p<0.05), 83 (11)% (p<0.05), and 67 (13)% (p<0.01), respectively. CONCLUSIONS: Cirrhotic liver and HCC express SSTRs. Although the somatostatin analogues used in this study did not affect proliferation and apoptosis, stimulation of SSTR1 may decrease invasiveness of HCC by reducing migration of hepatoma cells and/or HSCs. Clinical trials evaluating somatostatin analogues for the treatment of HCC should take these findings into account.
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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 | 0.001 | 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".