MicroRNAs as Regulators of Signal Transduction in Urological Tumors
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are short noncoding RNAs that have been shown to play pivotal roles in carcinogenesis. In the past decade, miRNAs have been the focus of much research in oncology, and there are great expectations for their utility as cancer biomarkers and therapeutic targets. CONTENT: In this review we examine how miRNAs can regulate signal transduction pathways in urological tumors. We performed in silico target prediction using TargetScan 5.1 to identify the signal transduction targets of miRNA, and we summarize the experimental evidence detailing miRNA regulation of pathways analyzed herein. SUMMARY: miRNAs, which have been shown to be dysregulated in bladder, prostate, and renal cell cancer, are predicted to target key proteins in signal transduction. Because androgen receptor signaling is a major regulator of prostate cancer growth, its regulation by miRNAs has been well described. In addition, members of the phosphatidylinositol 3-kinase/Akt (RAC-alpha serine/threonine-protein kinase) signaling pathway have been shown to be susceptible to miRNA regulation. In contrast, there are very few studies on the impact of miRNA regulation on signaling by VHL (von Hippel-Lindau tumor suppressor) and vascular endothelial growth factor in renal cell carcinoma or by fibroblast growth factor receptor 3 and p53 in bladder cancer. Many miRNAs are predicted to target important signaling pathways in urological tumors and are dysregulated in their respective cancer types; a systematic overview of miRNA regulation of signal transduction in urological tumors is pending. The identification of these regulatory networks might lead to novel targeted cancer therapies. In general, the targeting of miRNAs is a valuable approach to cancer therapy, as has been shown recently for various types of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".