Analysis of 41 cancer cell lines reveals excessive allelic loss and novel mutations in the<i>SIRT1</i>gene
Bibliographic record
Abstract
SIRT1 is an evolutionarily conserved protein deacetylase that modulates stress response, cellular metabolism and aging in model organisms. While SIRT1 exerts beneficial effects in protecting against age-related diseases, the role of SIRT1 in cancer has been controversial. SIRT1 promotes cell survival by deacetylating, and thereby negatively regulating the activity of important tumor suppressors such as p53. In this regard, SIRT1 has been considered to be a potential oncogene, and SIRT1 inhibitors have been studied for possible anticancer therapeutic effects. In contrast, it has been shown that SIRT1 deficiency leads to increased genomic instability and tumorigenesis, and that overexpression of SIRT1 attenuates cancer formation in mice, suggesting it may also act as a tumor suppressor. Based on this evidence, SIRT1-activating molecules could act as candidate chemotherapeutic drugs. In order to gain insight into the role of SIRT1 in cancer, we performed a comprehensive resequencing analysis of the SIRT1 gene in 41 tumor cell lines and found an unusually excessive homozygosity, which was confirmed to be allelic loss by microsatellite analysis. Furthermore, we found two novel SIRT1 mutations (D739Y and R65_A72del) in addition to the known, rare non-synonymous variation resulting in I731V. In vitro assays using purified SIRT1 protein showed that these mutations do not alter SIRT1 deacetylase activity or telomerase activity, which was shown to be regulated by SIRT1. We conclude that allelic loss or mutations in the SIRT1 gene occur prevalently during tumorigenesis, supporting the assertion that SIRT1 may serve as a tumor suppressor.
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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".