SMOC2 promotes an epithelial-mesenchymal transition and a pro-metastatic phenotype in epithelial cells of renal cell carcinoma origin
Bibliographic record
Abstract
Renal Cell Carcinoma (RCC) is the most common form of all renal cancer cases, and well-known for its highly aggressive metastatic behavior. SMOC2 is a recently described non-structural component of the extracellular matrix (ECM) that is highly expressed during tissue remodeling processes with emerging roles in cancers, yet its role in RCC remains elusive. Using gene expression profiles from patient samples, we identified SMOC2 as being significantly expressed in RCC tissue compared to normal renal tissue, which correlated with shorter RCC patient survival. Specifically, de novo protein synthesis of SMOC2 was shown to be much higher in the tubular epithelial cells of patients with biopsy-proven RCC. More importantly, we provide evidence of SMOC2 triggering kidney epithelial cells into an epithelial-to-mesenchymal transition (EMT), a phenotype known to promote metastasis. We found that SMOC2 induced mesenchymal-like morphology and activities in both RCC and non-RCC kidney epithelial cell lines. Mechanistically, treatment of RCC cell lines ACHN and 786-O with SMOC2 (recombinant and enforced expression) caused a significant increase in EMT-markers, -matrix production, -proliferation, and -migration, which were inhibited by targeting SMOC2 by siRNA. We further characterized SMOC2 activation of EMT to occur through the integrin β3, FAK and paxillin pathway. The proliferation and metastatic potential of SMOC2 overexpressing ACHN and 786-O cell lines were validated in vivo by their significantly higher tumor growth in kidneys and systemic dissemination into other organs when compared to their respective controls. In principle, understanding the impact that SMOC2 has on EMT may lead to more evidence-based treatments and biomarkers for RCC metastasis.
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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) | 0.001 | 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".