Furanoic Lipid F-6, A Novel Anti-Cancer Compound that Kills Cancer Cells by Suppressing Proliferation and Inducing Apoptosis
Bibliographic record
Abstract
Identifying novel anti-cancer drugs is important for devising better cancer treatment options. In a series of studies designed to identify novel therapeutic compounds, we recently showed that a C-20 fatty acid (12,15-epoxy-13,14-dimethyleicosa-12,14-dienoic acid, a furanoic acid or F-6) present in the lipid fraction of the secretions of the Arabian Gulf catfish skin (Arius bilineatus Val.; AGCS) robustly induces neutrophil extracellular trap formation. Here, we demonstrate that a lipid mix (Ft-3) extracted from AGCS and F-6, a component of Ft-3, dose dependently kill two cancer cell lines (leukemic K-562 and breast MDA MB-231). Pure F-6 is approximately 3.5 to 16 times more effective than Ft-3 in killing these cancer cells, respectively. Multiplex assays and network analyses show that F-6 promotes the activation of MAPKs such as Erk, JNK, and p38, and specifically suppresses JNK-mediated c-Jun activation necessary for AP-1-mediated cell survival pathways. In both cell lines, F-6 suppresses PI3K-Akt-mTOR pathway specific proteins, indicating that cell proliferation and Akt-mediated protection of mitochondrial stability are compromised by this treatment. Western blot analyses of cleaved caspase 3 (cCasp3) and poly ADP ribose polymerase (PARP) confirmed that F-6 dose-dependently induced apoptosis in both of these cell lines. In 14-day cell recovery experiments, cells treated with increasing doses of F-6 and Ft-3 fail to recover after subsequent drug washout. In summary, this study demonstrates that C-20 furanoic acid F-6, suppresses cancer cell proliferation and promotes apoptotic cell death in leukemic and breast cancer cells, and prevents cell recovery. Therefore, F-6 is a potential anti-cancer drug candidate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".