Regulation of the proline regulatory axis and autophagy modulates stemness in TP73/p73 deficient cancer stem-like cells
Bibliographic record
Abstract
Cancer stem-like cells (CSLCs) reside as a small population within tumors, which mostly contain a larger population of differentiated cells. With their unique self-renewing abilities, CSLCs remain refractory to various therapeutic interventions, which otherwise kill differentiated cancer cells, and thus are a major culprit behind cancer treatment failures and cancer relapse. Recently, the process of macroautophagy/autophagy has emerged as a potential therapeutic target for eliminating CSLCs, as autophagic homeostasis has been discovered to play an important role in the growth of cancer and normal stem cells, and is required for the maintenance of the non-differentiated state of CSLCs. Our current work now shows that the so-called 'tumor suppressor' TP73/p73 plays an unconventional role in CSLC biology, and positively regulates the growth and stemness of CSLCs through the modulation of autophagy. Our data show that TP73/p73 deficiency, promotes autophagy in CSLCs by activating the autophagy machinery involving AMPK-TSC-MTOR signaling. Mechanistically, TP73/p73 deficiency-induced autophagy occurs as a result of reduced ATP levels resulting from the metabolic perturbations within the proline regulatory axis. Collectively, these findings unveil novel therapeutically-relevant implications for autophagy in the TP73/p73-dependent regulation of stemness within CSLCs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".