Gram-negative bacterial infection enhances the potential of gastric adenocarcinoma peritoneal metastasis via TNFR1 dependent manner
Bibliographic record
Abstract
Adenocarcinoma of the proximal stomach is the fastest rising malignancy in North America,and is associated with a high rate of recurrence to the peritoneum. As part of cancer treatment,the majority of patients undergo at least one invasive surgical procedure. Recent clinicaldata has linked postoperative infection complications with adverse oncologic outcomes;however, the underlying mechanisms are unclear. Emerging evidence suggests that duringinfection the release of TNFα, a key inflammatory cytokine facilitates cancer progression.The role and mechanisms of gram-negative bacterial infections in facilitating the metastaticpotential of gastric cancer to the peritoneum is unknown. We hypothesized that incubation ofgastric cancer cells or peritoneal MCs with heat-inactivated Escherichia coli or lipolysaccharide(LPS) enhance gastric cancer cell adhesion and invasion via TNFR1 signaling and increasethe potential of peritoneal metastasis. We found that incubation of human gastric cancercells or mesothelial cells with heat inactivated E. coli, LPS or TNFα significantly increaseby 3-4 fold in vitro adhesion 3-4 fold to mesothelial cells and enhance in vitro invasion. Theseresults were attenuated by inhibition of TLR4 (Eritoran), inhibition of TNFR1 (anti-TNFR1/isotype control antibodies) or p38 MAPK inhibitor (BIRB0796). TNFα treatment alsoincreased CD54 and CD106 expression on MC and cancer cells. To further validate the in vitroresults, a novel ex vivo murine peritoneal metastasis model was developed. We reportedthat ex vivo gastric cancer cells adhesion to murine peritoneum is augmented by overnightLPS, HI E.coli and TNFα treatments and this effect was abrogated by using TNFR1-/- mouseperitoneum. These findings implicate TNFα in the process of gastric cancer metastasis to theperitoneum in the context of systemic infection and identify TNFα as potential therapeutictarget.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".