Abstract 5253: Regulation of estrogen related receptor α by ING4 and its potential role in breast cancer pathogenesis
Bibliographic record
Abstract
Abstract Breast cancer is one of the most frequently diagnosed tumors in the United States that affects one in eight women during their lifetime. It is second only to lung cancer as cause of cancer death in women. The orphan members of the nuclear receptor superfamily, estrogen-related receptors ERRα and γ (ERRs) isoforms are considered the master regulators of energy metabolism and their transcriptional pathways are highly associated with the cancer phenotype. Realizing the important function of co-regulators on ERRs transcriptional activity and their impact on cancer development and progression, we have identified the tumor growth family member 4 (ING4), a protein that has been implicated in several solid tumors and physically interacts with ERRα in breast cancer cellular models and modulates its transcriptional activity. Examination of the Oncomine database revealed that both ING4 and ERRα gene expression were significantly higher in invasive ductal and lobular breast carcinomas compared to normal ones, and their increased expression is inversely correlated with the overall patient survival rate in The Cancer Genome Atlas (TCGA) database. Knocking down ING4 in a breast cancer cell line using siRNA technology significantly decreased the expression of ERRα, and overexpression of ING4 upregulates the endogenous expression of ERRα. Well-defined ERRα target genes were also regulated by ING4 in the same manner as ERRα. Also, depleting ING4 expression significantly increased breast cancer cell migration in in vitro scratch assays, while both ING4 and ERRα knockdown decreased breast cancer cell proliferation. Collectively, these findings provide evidence for positive regulation of ERRα by ING4. Understanding the mechanism by which ING4/ERRα interaction regulates cellular pathways in breast cancer, underscores the impact of such regulation on biological and pharmacological actions. Thus, they can be used for the development of improved novel pharmaceuticals that target ERRα/ING4 complex or its downstream pathways. Citation Format: Aymen A. Shatnawi, Vincent Giguere. Regulation of estrogen related receptor α by ING4 and its potential role in breast cancer pathogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5253.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".