Statins as Potential Therapeutics for Lung Cancer
Bibliographic record
Abstract
Lung cancer is the most common cancer worldwide. It also has the highest malignancy-associated mortality rate. Treatment options are limited by cancer and tumor heterogeneity, resistance to treatment options, and an advanced stage at time of diagnosis, all of which are common. Statins are a class of lipid-lowering medications that have been studied for their antitumor effects in various types of cancers. Multiple mechanisms have been proposed to explain their observed off-target effects. Most of these hypotheses focus largely on statin-induced upregulation of proapoptotic signaling pathways and mediators, and the downregulation of antineoplastic factors secondary to statin use. Preclinical and clinical studies support their use for conferring a mortality benefit and improving treatment effect in some chemotherapy-resistant subtypes of lung cancer. However, their exact mechanism of action, class-dependent effect, dose-dependent effect, potential use as adjuvant chemotherapeutics, and markers of statin-sensitivity in specific lung cancer subtypes remain areas of ongoing investigation. Herein, we review the latest literature pertinent to the role statins can play in the management of lung cancers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".