Differential SIRT1 expression in hepatocellular carcinomas and cholangiocarcinoma of the liver.
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) are two major liver malignancies. Although some phenotypic overlap is known, HCC and CCA are usually different with regard to etiology, histology, and prognosis. Gene expression and deacetylase activity of the class III histone deacetylase SIRT1 are up-regulated in cancer cells due to oncogene overexpression or loss of function of tumor suppressor genes. SIRT1 may play a critical role in tumor initiation, progression, and drug resistance by blocking senescence and apoptosis, and promoting cell growth and angiogenesis, but pleiotropic effects (synchronous or metachronous anti-proliferation and anti-apoptotic mechanisms) have been suggested in some cancers. Our aim was to investigate the expression of SIRT1 in liver epithelial malignancies. Thirty carcinomas of the liver, including 16 HCC and 14 CCA cases, were investigated by immunohistochemistry using monoclonal antibodies against SIRT1 and p53. Western blot analysis (WBA) was carried out for expression of SIRT1 in three CCA cell lines, one HCC cell line, and one cell line of Papova-immortalized normal hepatocytes. An expression of SIRT1 was found in 11 of 16 (68.75%) HCC and in 5 of 14 (35.71%) CCA. Moreover, we found an expression of p53 in 8 out of 16 (50%) HCC and 13 out of 14 (92.86%) CCA. WBA showed expression of SIRT1 in all cell lines studied, although a stronger signal was seen in the HCC cell line. Immunohistochemical data did not correlate to clinical stage or other clinical or histopathological parameters. Sirtuin 1 is a phylogenetically-conserved family of deacetylases and our data seem to indicate that (1) pleiotropic effects may be present in hepatic epithelial malignancies, and (2) there is no specificity of SIRT1 for either HCC or CCA.
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 | 0.001 | 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 | 0.000 |
| 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".