N6-Methyladenosine Reader YTHDF1 Promotes ARHGEF2 Translation and RhoA Signaling in Colorectal Cancer
Bibliographic record
Abstract
Background & Aims N6-methyladenosine (m 6 A) governs the fate of RNAs through m 6 A readers. Colorectal cancer (CRC) exhibits aberrant m 6 A modifications and expression of m 6 A regulators. However, how m 6 A readers interpret oncogenic m 6 A methylome to promote malignant transformation remains to be illustrated. Methods YTH N6-methyladenosine RNA binding protein 1 ( Ythdf1 ) knockout mouse was generated to determine the effect of Ythdf1 in CRC tumorigenesis in vivo. Multiomic analysis of RNA-sequencing, m 6 A methylated RNA immunoprecipitation sequencing, YTHDF1 RNA immunoprecipitation sequencing, and proteomics were performed to unravel targets of YTHDF1 in CRC. The therapeutic potential of targeting YTHDF1-m 6 A-Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) was evaluated using small interfering RNA (siRNA) encapsulated by lipid nanoparticles (LNP). Results DNA copy number gain of YTHDF1 is a frequent event in CRC and contributes to its overexpression. High expression of YTHDF1 is significantly associated with metastatic gene signature in patient tumors. Ythdf1 knockout in mice dampened tumor growth in an inflammatory CRC model. YTHDF1 promotes cell growth in CRC cell lines and primary organoids and lung and liver metastasis in vivo. Integrative multiomics analysis identified RhoA activator ARHGEF2 as a key downstream target of YTHDF1. YTHDF1 binds to m 6 A sites of ARHGEF2 messenger RNA, resulting in enhanced translation of ARHGEF2 . Ectopic expression of ARHGEF2 restored impaired RhoA signaling, cell growth, and metastatic ability both in vitro and in vivo caused by YTHDF1 loss, verifying that ARHGEF2 is a key target of YTHDF1. Finally, ARHGEF2 siRNA delivered by LNP significantly suppressed tumor growth and metastasis in vivo. Conclusions We identify a novel oncogenic epitranscriptome axis of YTHDF1-m 6 A-ARHGEF2, which regulates CRC tumorigenesis and metastasis. siRNA-delivering LNP drug validated the therapeutic potential of targeting this axis in CRC.
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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) | 0.002 | 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".