Abstract 4028: Therapeutic relevance of two novel cancer candidate genes, <i>RASAL2</i> and <i>NENF</i>, activated by DNA hypomethylation
Bibliographic record
Abstract
Abstract DNA hypomethylation, a process of losing DNA methylation marks, plays an important role in cancer, particularly through activating genes that promote metastasis. We used a whole genome approach of mapping promoters that are hypomethylated in hepatocellular carcinoma (HCC), in order to identify novel candidate genes that play a critical role in cancer metastasis. We focused on two genes that were not previously assigned a role in cancer or cancer metastasis: Ras-GTPase-activating protein (RASAL2) and neuron derived neurotrophic factor (NENF) genes. We tested whether these genes play a causal role in cellular transformation and cancer invasiveness and are therefore candidate targets for anticancer drugs. siRNA depletion of RASAL2 and NENF expression in HepG2 HCC, SkHep1 liver adenocarcinoma, and T24 bladder cancer cell lines was performed and confirmed by QPCR. Following siRNA knockdowns, the effects on cell viability, anchorage-independent growth, and invasive capacities were assessed as measured by trypan blue exclusion test, soft agar, and Boyden chamber assays, respectively. We also evaluated phosphorylation of several serine/threonine kinases by western blot. RASAL2 and NENF depletion effectively inhibits cancer cell growth and cell invasive capacities. siRNA knockdowns resulted in 70-90% reduction of cell viability compared with cells treated with control siRNA. Cell invasion through an extracellular matrix in vitro was impeded by 80-98% after depletion of these proteins with the most profound effect seen in SkHep1 cell line. Anchorage independent growth, an indicator of the transformed state of cancer cells, was almost completely suppressed (97-100%) in SkHep1 and T24 cells with siRNA depletion whereas control cells grew in soft agar forming approximately 300 colonies/well on a 6-well plate. Interestingly, the observed effects seem to be cancer cell-specific as no significant changes were found in normal hepatocytes after knockdowns of the tested genes. The analysis of phosphorylation level of a set of kinases demonstrated the implication of the tested genes in PI3K/AKT and MAPK signaling pathways, functionally linked to cancer. After RASAL2 and NENF depletion in SkHep1 cells, we observed a decrease in phosphorylation of AKT, JNK, MKK6, p70S6 kinases by 60%, 50%, 50-80% and 50-70%, respectively. The knockdown of RASAL2 and NENF also led to changes in expression of genes involved with the DNA methylation machinery, including DNMT1 and MBD2 down-regulation. Our results established for the first time the role of two novel candidate genes in cancer and defined the potential functional role of DNA hypomethylation in activation of these genes.This study was supported by a grant from the MDEIE program of the government of Quebec and the National Cancer Institute of Canada to MS. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4028. doi:1538-7445.AM2012-4028
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".