Pluripotent Transcription Factors Possess Distinct Roles in Normal versus Transformed Human Stem Cells
Bibliographic record
Abstract
BACKGROUND: Cancer and normal stem cells (SCs) share proliferative properties of self-renewal and expression of key transcription factors (TFs). Despite similar TF identities, the functional role of specific TFs responsible for retaining SC state has yet to be examined in cancer. METHODOLOGY/PRINCIPAL FINDINGS: Here, we compare the role of Oct4 and Nanog, two-core pluripotent TFs, in transformed (t-hPSCs), and normal human pluripotent stem cells (hPSCs). Unlike normal SCs, self-renewal and survival of t-hPSCs were found to be independent of Oct4. In contrast, t-hPSCs exhibit hypersensitivity to reduction in Nanog and demonstrate complete loss of self-renewal coupled with apoptosis. Dual and sequential knockdown of Oct4 and Nanog revealed that sensitivity of t-hPSCs to Nanog was Oct4 dependent. CONCLUSIONS/SIGNIFICANCE: Our study indicates a bifurcation for the role of two-core SC and cancer related TFs in self-renewal and survival processes. We suggest that the divergent roles of these TFs establish a paradigm to develop novel therapeutics towards selective destruction of aggressive tumors harboring cancer stem cells (CSCs) with similar molecular signatures.
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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".