The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma
Bibliographic record
Abstract
Comprehensive N6-methyladenosine (m6A) epitranscriptomic profiling of primary tumors remains largely uncharted. Here, we profiled the m6A epitranscriptome of 10 nonneoplastic lung tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating the corresponding transcriptomic, proteomic, and extensive clinical annotations. We identified distinct clusters and genes that were exclusively linked to disease progression through m6A modifications. In comparison with nonneoplastic lung tissues, we identified 430 transcripts to be hypo-methylated and 222 to be hyper-methylated in tumors. Among these genes, EML4 emerged as a novel metastatic driver, displaying significant hypermethylation in tumors. m6A modification promoted the translation of EML4, leading to its widespread overexpression in primary tumors. Functionally, EML4 modulated cytoskeleton dynamics by interacting with ARPC1A, enhancing lamellipodia formation, cellular motility, local invasion, and metastasis. Clinically, high EML4 protein abundance correlated with features of metastasis. METTL3 small-molecule inhibitor markedly diminished both EML4 m6A and protein abundance and efficiently suppressed lung metastases in vivo. Significance: Our study reveals a dynamic and functional epitranscriptomic landscape in LUAD, offering a valuable resource for further research in the field. We identified EML4 hypermethylation as a key driver of tumor metastasis, highlighting a novel therapeutic strategy of targeting EML4 to prevent LUAD metastasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".