MAPK4 silencing in gastric cancer drives liver metastasis by positive feedback between cancer cells and macrophages
Bibliographic record
Abstract
Liver metastasis is a major cause of death in gastric cancer patients, but the underlying mechanisms are poorly understood. Through a combination of in vivo screening and transcriptome profiling followed by quantitative RT-PCR and tissue array analyses, we found that mitogen-activated protein kinase 4 (MAPK4) downregulation in gastric cancer tissues from patients is significantly associated with liver metastasis and poor prognosis. The knockdown of MAPK4 in gastric cancer cells promotes liver metastasis in orthotopic mouse models. MAPK4 depletion in gastric cancer cells induces the secretion of macrophage migration inhibitory factor (MIF) to polarize tumor-associated macrophages (TAMs) in orthotopic xenograft tumors. Moreover, TAMs activate epithelial-mesenchymal transition of gastric cancer cells to suppress MAPK4 expression, which further increases MIF secretion to polarize TAMs. Taken together, our results suggest a previously undescribed positive feedback loop between cancer cells and macrophages mediated by MAPK4 silencing that facilitates gastric cancer liver metastasis.
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 | 0.000 | 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) | 0.001 | 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".