Let-7b Inhibits Human Cancer Phenotype by Targeting Cytochrome P450 Epoxygenase 2J2
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are small, noncoding RNA molecules of 20 to 22 nucleotides that regulate gene expression by binding to their 3' untranslated region (3'UTR). Increasing data implicate altered miRNA participation in the progress of cancer. We previously reported that CYP2J2 epoxygenase promotes human cancer phenotypes. But whether and how CYP2J2 is regulated by miRNA is not understood. METHODS AND RESULTS: Using bioinformatics analysis, we found potential target sites for miRNA let-7b in 3'UTR of human CYP2J2. Luciferase and western blot assays revealed that CYP2J2 was regulated by let-7b. In addition, let-7b decreased the enzymatic activity of endogenous CYP2J2. Furthermore, let-7b may diminish cell proliferation and promote cell apoptosis of tumor cells via posttranscriptional repression of CYP2J2. Tumor xenografts were induced in nude mice by subcutaneous injection of MDA-MB-435 cells. The let-7b expression vector, pSilencer-let-7b, was injected through tail vein every 3 weeks. Let-7b significantly inhibited the tumor phenotype by targeting CYP2J2. Moreover, quantitative real-time polymerase chain reaction and western blotting were used to determine the expression levels of let-7b and CYP2J2 protein from 18 matched lung squamous cell cancer and adjacent normal lung tissues; the expression level of CYP2J2 was inversely proportional to that of let-7b. CONCLUSIONS: Our results demonstrated that the decreased expression of let-7b could lead to the high expression of CYP2J2 protein in cancerous tissues. These findings suggest that miRNA let-7b reduces CYP2J2 expression, which may contribute to inhibiting tumor phenotypes.
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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) | 0.001 | 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".