EHMT2/G9a-Inhibition Reprograms Cancer-Associated Fibroblasts (CAFs) to a More Differentiated, Less Proliferative and Invasive State
Bibliographic record
Abstract
Abstract Cancer-associated fibroblasts (CAFs) have previously been shown to play a pivotal role in multiple cancer dynamics, including mediating tumor cell invasion: their pro-invasive secretory profile and ability to remodel the extracellular matrix (ECM) architecture particularly promote tumor progression through tumor cell invasion into surrounding tissue areas and beyond. Given that reduced CAF abundance in tumors correlates with improved outcomes in various cancers, we set out to identify epigenetic targets involved in CAF activation in the tumor-stromal margin to reduce overall tumor aggressiveness. Using the GLAnCE (Gels for Live Analysis of Compartmentalized Environments) co-culture platform, we performed an image-based, phenotypic screen and identified EHMT2 (also known as G9a), an epigenetic enzyme that targets the methylation of histone 3 lysine 9 (H3K9), as the most potent modulator of CAF abundance and CAF-mediated tumor cell invasion. Transcriptomic and functional analysis of EHMT2-inhibited CAFs revealed the involvement of EHMT2 in driving CAFs towards a pro-invasive phenotype. Further, EHMT2 signaling mediated CAF hyperproliferation, a feature that is typically associated with activated fibroblasts present in tumors, but the molecular basis for which has not thus far been identified. This study suggests a role for EHMT2 as a regulator of CAF hyperproliferation within the tumor mass, which in turn magnifies CAF-induced pro-invasive effects on tumor 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 | 0.000 | 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".