Deciphering the m6A Epitranscriptomic Landscape of mRNAs in Breast Cancer Cells
Bibliographic record
Abstract
N6-methyladenosine (m6A), the most prevalent modification in mRNAs, influences mRNA stability, splicing, and translation. Dysregulation of m6A patterns has been linked to various diseases, including cancer, highlighting its significance in cellular homeostasis. However, accurate detection and precise quantification of m6A sites within individual transcripts remains challenging. In this study, we employed nanopore sequencing to achieve transcriptome-wide, base-resolution map of the m6A methylome in human breast cancer cells. By investigating m6A distribution across breast cancer cell lines and implementing a CRISPR/Cas9-based knockout of the major m6A eraser ALKBH5, we provide insights into the differential methylation levels and motif-specific characteristics of m6A transcriptomic sites. We elucidated the m6A epitranscriptome in five well-established breast cancer cell lines derived from distinct molecular subtypes of the disease and confirmed a DRACH-dependent activity of ALKBH5. Comparative methylation analysis with the non-cancerous MCF-10A cell line revealed that MCF-7 and BT-474 breast cancer cells are primarily hypomethylated, while BT-20, MDA-MB-231 and SK-BR-3 cells show widespread hypermethylation. These cell line-based patterns highlight the potential regulatory role of m6A in breast cancer heterogeneity. Overall, our findings enhance the understanding of m6A dynamics in breast cancer.
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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".