Inhibition of de novo Methyltransferase 3B is a Potential Therapy for Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Aberrant epigenetic patterns, including inactivation of tumor suppressor genes due to DNA methylation, have been described in many human cancers. Epigenetic therapeutic is a new and rapidly developing area of tumor treatment because DNA methyltransferase (DNMT) inhibitors can reverse its changes. We attempted to identify potential approach for epigenetic therapy of hepatocellular carcinoma. METHODS: We knocked down the expression of DNMT 1 or DNMT 3B by siRNA, and inhibited DNA methyltranferases by 5-Aza-2'-deoxycytidine. We used high-density oligonucleotide gene expression microarrays to examine the induced genes in human hepatocellular carcinoma cell line SMMC-7721 after suppressing DNA methyltranferases. The 5' ends of up-regulated genes were analyzed by BLAST database to determine whether they have promoter CpG islands, and then the identical induced genes were compared among different inhibition of DNA methyltranferases. RESULTS: Our results show that 9 genes were found to be over expressed by more than two-fold induced by DNMT1 siRNA and 5-Aza-CdR, and 30 genes were found to be over expressed by more than two-fold induced by DNMT3B siRNA and 5-Aza-CdR in SMMC-7721. Among them, 76.6% up-regulated genes conjectural contained 5' CpG islands. The DNMT3B siRNA could induce more genes identical to demethylation agent in SMMC-7721. CONCLUSIONS: DNMT3B might be a new potential target for therapy of hepatocellular carcinoma.
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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) | 0.003 | 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".