Control of the Human Osteopontin Promoter by ERRα in Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer is the second leading cause of death from cancer. Osteopontin (OPN) is a component of tumor extracellular matrix identified as a key marker of cancer progression. The estrogen-related receptor α (ERRα) has been implicated in endocrine-related cancer development and progression, possibly through modulation of cellular energy metabolism. Previous reports that ERRα regulates OPN expression in bone prompted us to investigate whether ERRα controls OPN expression in human colorectal cancer. Using a tissue microarray containing 83 tumor-normal tissue pairs of colorectal cancer samples, we found that tumor epithelial cells displayed higher staining for ERRα than normal mucosa, in correlation with elevated OPN expression. In addition, knocking down endogenous ERRα led to reduced OPN expression in HT29 colon cancer cells. Promoter analysis, inhibition of ERRα activity, and expression and mutation of potential ERRα response elements in the proximal promoter of human OPN showed that ERRα and its obligate co-activator, peroxisome proliferator-activated receptor γ co-activator-1 α, positively control human OPN promoter activity. Furthermore, chromatin immunoprecipitation experiments confirmed in vivo occupancy of the OPN promoter by ERRα in HT29 cells, suggesting that OPN is a direct target of ERRα in colorectal cancer. These findings suggest an additional mechanism by which ERRα participates in the development and progression of colorectal cancer, further supporting the relevance of targeting ERRα with antagonists as anticancer agents.
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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) | 0.002 | 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".