Genome-Wide Study of Hypomethylated and Induced Genes in Patients with Liver Cancer Unravels Novel Anticancer Targets
Bibliographic record
Abstract
PURPOSE: We utilized whole-genome mapping of promoters that are activated by DNA hypomethylation in hepatocellular carcinoma (HCC) clinical samples to shortlist novel targets for anticancer therapeutics. We provide a proof of principle of this approach by testing six genes short-listed in our screen for their essential role in cancer growth and invasiveness. EXPERIMENTAL DESIGN: We used siRNA- or shRNA-mediated depletion to determine whether inhibition of these genes would reduce human tumor xenograft growth in mice as well as cell viability, anchorage-independent growth, invasive capacities, and state of activity of nodal signaling pathways in liver, breast, and bladder cancer cell lines. RESULTS: Depletion of EXOSC4, RNMT, SENP6, WBSCR22, RASAL2, and NENF effectively and specifically inhibits cancer cell growth and cell invasive capacities in different types of cancer, but, remarkably, there is no effect on normal cell growth, suggesting a ubiquitous causal role for these genes in driving cancer growth and metastasis. Depletion of RASAL2 and NENF in vitro reduces their growth as explants in vivo in mice. RASAL2 and NENF depletion interferes with AKT, WNT, and MAPK signaling pathways as well as regulation of epigenetic proteins that were previously demonstrated to drive cancer growth and metastasis. CONCLUSION: Our results prove that genes that are hypomethylated and induced in tumors are candidate targets for anticancer therapeutics in multiple cancer cell types. Because these genes are particularly activated in cancer, they constitute a group of targets for specific pharmacologic inhibitors of cancer and cancer metastasis. Clin Cancer Res; 20(12); 3118-32. ©2014 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".