Kidney injury molecule-1 inhibits metastasis of renal cell carcinoma
Bibliographic record
Abstract
Abstract Metastasis is present in approximately 30% of patients diagnosed with renal cell carcinoma (RCC) and is associated with a 5-year survival rate of < 15%. Kidney injury molecule 1 (KIM-1), encoded by the HAVCR1 gene, is a proximal tubule cell-surface glycoprotein and a biomarker for early detection of RCC, but its pathophysiological significance in RCC remains unclear. We generated human and murine RCC cell lines either expressing or lacking KIM-1, respectively, and compared their growth and metastatic properties using validated methods. Surprisingly, KIM-1 expression had no effect on cell proliferation or subcutaneous tumour growth in immune deficient (Rag1−/−) Balb/c mice, but inhibited cell invasion and formation of lung metastasis in the same model. Further, we show that the inhibitory effect of KIM-1 on metastases was observed in both immune deficient and immune competent mice. Transcriptomic profiling identified the mRNA for the pro-metastatic GTPase, Rab27b, to be downregulated significantly in KIM-1 expressing human and murine RCC cells. Finally, analysis of The Cancer Genome Atlas (TCGA) data revealed that elevated HAVCR1 mRNA expression in the two most common types of RCC, clear cell and papillary RCC, tumours correlated with significantly improved overall patient survival. Our findings reveal a novel role for KIM-1 in inhibiting metastasis of RCC and suggests that tumour-associated KIM-1 expression may be a favourable prognostic factor.
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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 | 0.000 |
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".