The m6A reader IGF2BP2 promotes the progression of esophageal squamous cell carcinoma cells by increasing the stability of OCT4 mRNA
Bibliographic record
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common malignancy with high morbidity and mortality. Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) serves as a reader of RNA m6A (N6 methyladenosine) modification to regulate gene expression at the post-transcriptional level. Emerging evidence suggests that IGF2BP2 plays critical roles in tumorigenesis and malignant development. However, the biological function and molecular mechanism of IGF2BP2 in ESCC are not well understood. Here, we found that IGF2BP2 expression was upregulated in esophageal cancer tissues and ESCC cells, and IGF2BP2 overexpression enhanced proliferation, migration, invasion, and stem cell-like properties of ESCC cells. Conversely, the knockdown of IGF2BP2 expression inhibited malignant phenotype of ESCC cells. Mechanistically, IGF2BP2 upregulated octomer-binding transcription factor 4 (OCT4) mRNA expression, and RNA immunoprecipitation (RIP) assay proved that IGF2BP2 could interact with OCT4 mRNA. Moreover, OCT4 was modified at m6A confirmed by methylated m6A RNA immunoprecipitation (Me-RIP)-qPCR assay, and IGF2BP2 knockdown reduced OCT4 mRNA stability. These results suggested that IGF2BP2 served as a reader for m6A-modified OCT4, thus increased OCT4 mRNA expression by regulating its stability. Furthermore, the knockdown of OCT4 could reverse the effects of IGF2BP2 on ESCC cells. In conclusion, these data indicate that IGF2BP2, as a reader for m6A, plays an oncogenic role by regulating OCT4 expression in ESCC, which provides new insights into targeting IGF2BP2/OCT4 axis for the therapy of ESCC.
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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 | 0.001 |
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".