SET7/9 promotes hepatocellular carcinoma progression through regulation of E2F1
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide. Histone‑lysine N‑methyltransferase SET7/9 is a protein lysine monomethylase that methylates histone H3K4 as well as various non‑histone proteins. Deregulation of SET7/9 is frequently detected in human cancers. However, the role of SET7/9 in HCC development remains unclear. In the present study, upregulation of SET7/9 and E2F transcription factor 1 (E2F1) expression was detected in 68 samples of HCC tissues compared with these levels noted in the paired healthy liver samples. The expression levels of SET7/9 and E2F1 were significantly correlated with pathological stage and tumor size. Subcellular fractionation and co‑immunoprecipitation analyses revealed protein‑protein interaction between SET7/9 and E2F1 in the cytoplasm of HCC cells. Silencing of SET7/9, as well as treatment with 5'‑deoxy‑5'‑methylthioadenosine (MTA), a protein methylation inhibitor, led to reduced E2F1 protein abundance in HCC cells. Using Cell Counting Kit‑8 (CCK‑8) assay, Transwell migration assay and wound healing assay, significantly decreased cell proliferation, migration and invasion were observed in cells exhibiting downregulation of SET7/9 and E2F1 expression, as well as in wild‑type HCC cells treated with MTA. Furthermore, SET7/9 downregulation and MTA treatment resulted in reduced expression of downstream targets of E2F1, including cyclin A2, cyclin E1 and CDK2. In conclusion, the present study revealed an oncogenic function of SET7/9 in HCC and demonstrated that SET7/9 may be responsible for alterations in the proliferative ability, aggressiveness and invasive/metastatic potential of HCC cells through post‑translational regulation of E2F1.
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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) | 0.003 | 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".