RETRACTED: Role of Autophagy as a Survival Mechanism for Hypoxic Cells in Tumors
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Enhanced autophagy has been observed in hypoxic regions of solid tumors. Here we address the hypothesis that autophagy is required for survival of hypoxic cells. We evaluated sensitivity to hypoxia of three human tumor cell lines (MCF7, PC3, and LNCaP) and their autophagy-deficient variants with shRNA knockdown of the genes ATG7 and BECLIN1. Hypoxia-induced cell death was more rapid for autophagy-deficient cells and was increased in the presence of the proton pump inhibitor pantoprazole that inhibits autophagy. Autophagy-deficient cells had a lower rate of oxygen consumption than wild-type cells. In xenografts derived from the three cell lines, autophagy (as determined by increased LC3 and reduced p62/SQSTM1) colocalized with hypoxic regions (identified by EF5). Xenografts derived from autophagy-deficient cells grew more slowly than wild-type tumors. Both LC3 expression and hypoxia were decreased in xenografts generated from single-knockdown cells and absent in double-knockdown tumors. Our results are consistent with the hypothesis that autophagy facilitates the survival of hypoxic cells, although reduced oxygen consumption of autophagy-deficient cells may contribute to lack of hypoxia in tumors derived from them. Because hypoxia is associated with resistance to anticancer therapy, inhibition of autophagy has potential to enhance the effectiveness of cancer treatment.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".