Molecular mechanisms of metastatic peritoneal dissemination in gastric adenocarcinoma
Bibliographic record
Abstract
Peritoneal dissemination portends a dismal prognosis in patients with gastric adenocarcinoma in the context of limited effective treatments. The underlying cellular processes that drive gastric peritoneal carcinomatosis remain unclear, limiting the application of novel targeted therapies. In this comprehensive review, we aimed to identify and summarize all existing context-dependent molecular mechanisms that have been implicated in peritoneal dissemination and peritoneal carcinomatosis establishment from primary gastric adenocarcinoma. We applied a multilevel examination including data from in vivo murine models using human gastric cancer cell lines, in vitro technique-based studies, ex vivo models, and genomic/proteomic and molecular profiling analyses to report on various aspects of gastric cancer peritoneal metastasis biology. Mechanisms promoting peritoneal dissemination were grouped into three main functional categories: (1) intrinsic cancer cell biology, (2) cancer cell-peritoneal surface adhesion, and (3) peritoneal tumor microenvironment. We identified significant overlap among the three categories, indicating a complex interplay between multiple molecular mechanisms. By interrupting these pathways, peritoneal-directed therapies have the potential to improve quality and length of life in patients with high-risk primary gastric cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".