Endoplasmic Reticulum Protein ERp46 in Renal Cell Carcinoma
Bibliographic record
Abstract
An established inverse clinical correlation between serum adiponectin levels and renal cell carcinoma (RCC) aggressiveness exists. We have recently demonstrated that adiponectin suppresses clear cell RCC (ccRCC) progression through interaction with its receptor, adiponectin receptor 1 (AdipoR1). ERp46 has been shown to inhibit adiponectin signaling via interaction with AdipoR1 in HeLa cells. However, the expression of ERp46 in RCC has not been described thus far. The objectives of this study were to investigate ERp46 in RCC, its expression, its effects on RCC growth in a mouse model and whether it interacts with AdipoR1. We demonstrated a higher ERp46/AdipoR1 expression ratio in metastatic compared to non-metastatic ccRCC, as determined by immunohistochemistry of tissue microarrays and subsequent image analysis. When ERp46 was stably knocked down using shRNA or overexpressed in murine RCC RAG cells, RCC growth after subcutaneous injection in BALB/c nude mice was inhibited and accelerated, respectively. In vitro analysis to determine the molecular interaction between AdipoR1 and ERp46 included co-immunoprecipitation using human ccRCC 786-O cells and a bacterial adenylate cyclase-based two hybrid system and demonstrated no sustained AdipoR1-ERp46 interaction. This is the first report to suggest a role for ERp46 as a potential therapeutic target in RCC given its expression profile in human RCC samples and its effect on in vivo RCC growth. Since a stable interaction with AdipoR1 could not be established, we suggest that the tumorigenic properties of ERp46 in RCC cells are not related to an inhibitory modulation of AdipoR1.
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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".