<i>Fusobacterium nucleatum</i>‐induced exosomal <scp>HOTTIP</scp> promotes gastric cancer progression through the <scp>microRNA</scp>‐885‐3p/<scp>EphB2</scp> axis
Bibliographic record
Abstract
Recent studies have reported that Fusobacterium nucleatum (Fn) is associated with gastric cancer (GC). Cancer-derived exosomes contain key regulatory noncoding RNAs and are a crucial medium of intercellular communication. However, the function and regulatory mechanism of exosomes (Fn-GCEx) secreted from Fn-infected GC cells remains unclear. In this study, Fn-GCEx enhanced the proliferation, migration, and invasion capacity of GC cells in vitro, as well as tumor growth and metastasis in vivo. HOTTIP was also upregulated in GC cells treated with Fn-GCEx. Moreover, knockdown of HOTTIP weakened the effects of Fn-GCEx in recipient GC cells. Mechanistically, HOTTIP promoted EphB2 expression by sponging microRNA (miR)-885-3p, thus activating the PI3K/AKT pathway in Fn-GCEx treated GC cells. Overall, Fn infection induced the upregulation of exosomal HOTTIP from GC cells that subsequently promoted GC progression through the miR-885-3p/EphB2/PI3K/AKT axis. Herein, we identify a potential molecular pathway and therapeutic target for GC.
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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".