miRNome targeting NF-κB signaling orchestrates macrophage-triggered cancer metastasis and recurrence
Bibliographic record
Abstract
Whether and how tumor intrinsic signature determines macrophage-elicited metastasis remain elusive. Here, we show, in detailed studies of data regarding 7,477 patients of 20 types of human cancers, that only 13.8% ± 2.6%/27.9% ± 3.03% of patients with high macrophage infiltration index exhibit early recurrence/vascular invasion. In parallel, although macrophages enhance the motility of various hepatoma cells, their enhancement intensity is significantly heterogeneous. We identify that the expression of malignant Dicer, a ribonuclease that cleaves miRNA precursors into mature miRNAs, determines macrophage-elicited metastasis. Mechanistically, the downregulation of Dicer in cancer cells leads to defects in miRNome targeting NF-κB signaling, which in turn enhances the ability of cancer cells to respond to macrophage-related inflammatory signals and ultimately promotes metastasis. Importantly, transporting miR-26b-5p, the most potential miRNA targeting NF-κB signaling in hepatocellular carcinoma, can effectively reverse macrophage-elicited metastasis of hepatoma in vivo. Our results provide insights into the crosstalk between Dicer-elicited miRNome and cancer immune microenvironments and suggest that strategies to remodel malignant cell miRNome may overcome pro-tumorigenic activities of inflammatory cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".