Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis
Bibliographic record
Abstract
Carcinogenesis consists of oncogenesis and metastasis, and intriguingly microRNAs (miRNAs) are involved in both processes. Although aberrant miRNA activities are prevalent in diverse tumor types, the exact mechanisms for how they regulate cancerous processes are not always clear. To this end, we performed a large-scale pan-cancer analysis via a novel probabilistic approach to infer recurrent miRNA-target interactions implicated in 12 cancer types using data from The Cancer Genome Atlas. We discovered ~20,000 recurrent miRNA regulations, which are enriched for cancer-related miRNAs/genes. Notably, miRNA 200 family (miR-200/141/429) is among the most prominent miRNA regulators, which is known to be involved in metastasis. Importantly, the recurrent miRNA regulatory network is not only enriched for cancer pathways but also for extracellular matrix (ECM) organization and ECM-receptor interactions. The results suggest an intriguing cancer mechanism involving miRNA-mediated cell-to-cell communication, which possibly involves delivery of tumorigenic miRNA messengers to adjacent cells via exosomes. Finally, survival analysis revealed 414 recurrent-prognostic associations, where both gene and miRNA involved in each interaction conferred significant prognostic power in one or more cancer types. Together, our comprehensive pan-cancer analysis provided not only biological insights into metastasis but also brought to bear the clinical relevance of the proposed recurrent miRNA-gene associations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".