ERRα metabolic nuclear receptor controls growth of colon cancer cells
Bibliographic record
Abstract
The estrogen-related receptor alpha (ERRα) is a nuclear receptor that acts primarily as a regulator of metabolic processes, particularly in tissues subjected to high-energy demand. In addition to its control of energy metabolism and mitochondrial biogenesis, ERRα has recently been associated with cancer progression. Notably, increased expression of ERRα has been shown in several cancerous tissues, including breast, ovary and colon. However, additional studies are required to gain insight into the action of ERRα in cancer biology, particularly in non-endocrine-related cancers. Therefore, using a short hairpin RNA-mediated approach, we investigated whether ERRα is required for the rapid growth of colon cancer cells and to maintain their neoplastic metabolic state. Results show that silencing ERRα significantly impaired colon cancer cell proliferation and colony formation in vitro as well as their in vivo tumorigenic capacity. A pronounced delay in G1-to-S cell cycle phase transition was observed in ERRα-depleted cells in association with reduced cyclin-dependent kinase 2 activity and hyperphosphorylated state of the retinoblastoma protein along with disturbed expression of several cell cycle regulators, including p15 and p27. Interestingly, ERRα-depleted HCT116 cells also displayed significant reduction in expression of a large set of key genes to glycolysis, tricarboxylic acid cycle and lipid synthesis. Furthermore, using (14)C isotope tracer analysis, ERRα depletion in colon cancer cells resulted in reduced glucose incorporation and glucose-mediated lipogenesis in these cells. These findings suggest that ERRα coordinates colon cancer cell proliferation and tumorigenic capacity with energy metabolism. Thus, ERRα could represent a promising therapeutic target in colon cancer.
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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".