Data from Digitoxin-Induced Cytotoxicity in Cancer Cells Is Mediated through Distinct Kinase and Interferon Signaling Networks
Bibliographic record
Abstract
Abstract Cardiac glycosides (e.g., digoxin, digitoxin) constitute a diverse family of plant-derived sodium pump inhibitors that have been in clinical use for the treatment of heart-related diseases (congestive heart failure, atrial arrhythmia) for many years. Recently though, accumulating in vitro and in vivo evidence highlight potential anticancer properties of these compounds. Despite the fact that members of this family have advanced to clinical trial testing in cancer therapeutics, their cytotoxic mechanism is not yet elucidated. In this study, we investigated the cytotoxic properties of cardiac glycosides against a panel of pancreatic cancer cell lines, explored their apoptotic mechanism, and characterized the kinetics of cell death induced by these drugs. Furthermore, we deployed a high-throughput kinome screening approach and identified several kinases of the Na-K-ATPase-mediated signal transduction circuitry (epidermal growth factor receptor, Src, pkC, and mitogen-activated protein kinases) as important mediators downstream of cardiac glycoside cytotoxic action. To further extend our knowledge on their mode of action, we used mass-spectrometry–based quantitative proteomics (stable isotope labeling of amino acids in cell culture) coupled with bioinformatics to capture large-scale protein perturbations induced by a physiological dose of digitoxin in BxPC-3 pancreatic cancer cells and identified members of the interferon family as key regulators of the main protein/protein interactions downstream of digitoxin action. Hence, our findings provide more in-depth information regarding the molecular mechanisms underlying cardiac glycoside-induced cytotoxicity. Mol Cancer Ther; 10(11); 2083–93. ©2011 AACR.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".