CLEC18A interacts with sulfated GAGs and controls clear cell renal cell carcinoma progression
Bibliographic record
Abstract
Abstract C-type lectins are a large family of proteins with essential functions in both health and disease. In cancer, some C-type lectins have been found to both promote and inhibit tumor growth, but many of the C-type lectins still remain uncharacterised in a tumor context. Therefore, there is growing interst in further elucidating the mechanisms with which C-type lectins control tumor growth. Here, we report a key role of the CLEC18 family of C-type lectins in the progression of clear cell renal cell carcinoma (ccRCC). The CLEC18 family is conserved across the entire Chordata phylum with recent gene duplication events in humans. We found that CLEC18A is exclusively expressed in the proximal tubule of the kidney and the medial habenula of the brain. We further identified sulfated glycosaminoglycans (GAGs) of proteoglycans as the main CLEC18A ligand, making them unique among C-type lectins. In ccRCC patients, high expression of the CLEC18 family lectins in the tumor are associated with improved survival. In mouse models of ccRCC, deletion of the mouse ortholog Clec18a resulted in enhanced tumor growth. Our results establishes CLEC18A as a novel and critical regulators of ccRCC tumor growth and highlights the potential benefit of modulating CLEC18 expression in the renal tumor microenvironment.
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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) | 0.001 | 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".