Autophagy: The Potential Link between SARS-CoV-2 and Cancer
Bibliographic record
Abstract
COVID-19 infection survivors suffer from a constellation of symptoms referred to as post-acute COVID-19 syndrome. However, in the wake of recent evidence highlighting the long-term persistence of SARS-CoV-2 antigens in tissues and emerging information regarding the interaction between SARS-CoV-2 proteins and various components of the host cell macroautophagy/autophagy machinery, the unforeseen long-term consequences of this infection, such as increased risk of malignancies, should be explored. Although SARS-CoV-2 is not considered an oncogenic virus, the possibility of increased risk of cancer among COVID-19 survivors cannot be ruled out. Herein, we provide an overview of the possible mechanisms leading to cancer development, particularly obesity-related cancers (e.g., colorectal cancer), resulting from defects in autophagy and the blockade of the autophagic flux, and also immune escape in COVID-19 survivors. We also highlight the potential long-term implications of COVID-19 infection in the prognosis of patients with cancer and their response to different cancer treatments. Finally, we consider future directions for further investigations on this matter.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".