Investigating miRNA-related Pathways Contributing to Kidney Cancer Pathogenesis
Bibliographic record
Abstract
BACKGROUND/AIM: Renal cell carcinoma is one of the most common types of cancer worldwide. Understanding tumor pathogenesis is important in developing better treatment. Micro RNAs (miRNAs) are key players in controlling cancer behavior. Transcription factors (TFs) are potentially responsible for controlling miRNA expression and dysregulation in kidney cancer. The objective of this study was to better understand the TF-miRNA axis of interaction. MATERIALS AND METHODS: We utilized publicly available databases to investigate miRNA-TF interactions, including ChipBase database for TFs that binds to the promoters of miRNAs which are dysregulated in renal cell carcinoma. Renal cancer-specific TFs were extracted from the list using the GENT Database. We assessed the prognostic significance of these TFs using cBioPortal. RESULTS: We identified TFs which bind to miRNA promoters, including hepatocyte nuclear factor-4 alpha (HNF-4α), E2F transcription factor 4 (E2F4), signal transducer and activator of transcription 1 (STAT1), Sp1 transcription factor (SP1), GATA binding protein 6 (GATA6), and nuclear factor kappa B (NFκB). These TFs were positively correlated with their targeted miRNAs, including miR-200c, miR-15a, miR-146b, miR-155, and miR-223. We recognized unique patterns of interactions, including a divergent effect in which multiple miRNAs are simultaneously affected by the same TF. CONCLUSION: Our results show that miRNA-TF interaction is complex. Expression levels of these TFs were found to correlate with renal carcinoma prognosis and have potential utility as biomarkers for aggressive tumor behavior. Targeting these TFs may result in modulating the expression of their target genes and miRNAs, with subsequent therapeutic implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".