Nuclear localized Akt enhances breast cancer stem-like cells through counter-regulation of p21<sup>Waf1/Cip1</sup>and p27<sup>kip1</sup>
Bibliographic record
Abstract
UNLABELLED: Cancer stem-like cells (CSCs) are a rare subpopulation of cancer cells capable of propagating the disease and causing cancer recurrence. In this study, we found that the cellular localization of PKB/Akt kinase affects the maintenance of CSCs. When Akt tagged with nuclear localization signal (Akt-NLS) was overexpressed in SKBR3 and MDA-MB468 cells, these cells showed a 10-15% increase in the number of cells with CSCs enhanced ALDH activity and demonstrated a CD44(+High)/CD24(-Low) phenotype. This effect was completely reversed in the presence of Akt-specific inhibitor, triciribine. Furthermore, cells overexpressing Akt or Akt-NLS were less likely to be in G0/G1 phase of the cell cycle by inactivating p21(Waf1/Cip1) and exhibited increased clonogenicity and proliferation as assayed by colony-forming assay (mammosphere formation). Thus, our data emphasize the importance the intracellular localization of Akt has on stemness in human breast cancer cells. It also indicates a new robust way for improving the enrichment and culture of CSCs for experimental purposes. Hence, it allows for the development of simpler protocols to study stemness, clonogenic potency, and screening of new chemotherapeutic agents that preferentially target cancer stem cells. SUMMARY: The presented data, (i) shows new, stemness-promoting role of nuclear Akt/PKB kinase, (ii) it underlines the effects of nuclear Akt on cell cycle regulation, and finally (iii) it suggests new ways to study cancer stem-like cells.
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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) | 0.003 | 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".