Involvement of miRNAs in the formation and maintenance of self-renewing kidney cancer spheres with stem cell properties.
Bibliographic record
Abstract
463 Background: Cancer cells may acquire stem cell (CSC) properties by activated TGFβ-epithelial-mesenchymal transition (EMT) axis resulting in formation of cancer stem cells. miRNAs are involved in CSC formation in solid tumors, but their role has not been investigated in renal cell carcinoma (RCC). Methods: RCC spheres were generated and propagated in serum-free defined medium (SFDM). mRNA expression was assessed by qRT-PCR. miRNA expression was screened on a qRT-PCR based panel. Tumorigenicity was assessed by subcutaneous injection of RCC sphere or parental cells into immunodeficient mice in different dilutions. TargetScan and miRPath was used for target prediction and clustering. Results: We isolated self-renewing cancer spheres from ACHN and CAKI-1 RCC cell lines in the stem cell supporting media, SFDM. Spheres were highly clonogenic and tumorigenic in xenograft tumor model and expressed high levels of stem cell-related markers and mesenchymal markers. These spheres were enriched in the mesenchymal marker CD44 and the kidney progenitor maker CD24 indicating that EMT contributed to their formation or maintenance. We compared miRNA expression between the spheres and the parental cells and identified differentially expressed miRNAs. Functional clustering of their predicted targets indicates that TGFβ signaling is a potential regulator of CSC self-renewal and is regulated by the candidate miRNAs. Further, we show that transfection of ACHN and CAKI-1 cells with the miR-17 inhibitor resulted in rapid and highly efficient formation of cancer spheres that were indistinguishable from the spheres formed in SFDM. These spheres were stable and could be propagated indefinitely. Histologic examination and immunohistochemistry of the sphere-derived xenografts confirmed the presence of clear cell RCC with large areas of sarcomatoid dedifferentiation. Finally, we prove that the TGFβ receptor II, and the co-Smad Smad4 are possible direct targets of miR-17. Conclusions: The TGFβ-EMT axis likely contributes to the self-renewing potential of RCC spheres. miRNAs are differentially expressed in RCC spheres and miR-17 inhibition transformed ccRCC cells to highly tumorigenic RCC spheres.
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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".