Small interfering RNA-mediated reduction in heterogeneous nuclear ribonucleoparticule A1/A2 proteins induces apoptosis in human cancer cells but not in normal mortal cell lines.
Bibliographic record
Abstract
To prevent their recognition as DNA breaks, the ends of linear chromosomes are organized into telomeres, which are made of proteins bound to telomere-specific, double-stranded repeats and to single-stranded DNA extensions, the G-tails. The mammalian heterogeneous nuclear ribonucleoparticule A1 and A2 proteins can bind with high affinity to such G-tails. Moreover, previous work established that in certain mouse cells a severe reduction in the level of A1 is associated with shortened telomeric repeat tracts, and restoring A1 expression increases telomere length. Here, we document that the expression of A1/A2 proteins is elevated in a variety of human cancers, whereas A1/A2 expression is lower or absent in normal tissues. To determine whether the status of A1/A2 proteins could be improved from cancer markers to cancer targets, we used small interfering RNA-mediated RNA interference to elicit a reduction in A1/A2 proteins in a variety of human cell lines. We show that this treatment provoked specific and rapid cell death by apoptosis in cell lines derived from cervical, colon, breast, ovarian, and brain cancers. Cancer cell lines that lack p53 or express a defective p53 protein were equally sensitive to a small interfering RNA-mediated decrease in A1/A2 expression. The reduction in A1/A2 levels in HeLa cells was associated with a change in the distribution of the lengths of G-tails, an event not observed when apoptosis was induced with staurosporine. Remarkably, comparable decreases in the expression of A1/A2 in several mortal human fibroblastic and epithelial cell lines did not promote cell death. Thus, manipulating the level and activity of A1/A2 proteins may constitute a potent and specific approach in the treatment of human cancers of various origins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".