MicroRNAs: exploring a new dimension in the pathogenesis of kidney cancer
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the most common neoplasm of the adult kidney. The role of the von-Hippel-Lindeau (VHL) tumour suppressor gene is well established in RCC with a loss of VHL protein leading to accumulated hypoxia-induced factor (HIF) and the subsequent transcriptional activation of multiple downstream targets. Recently, microRNAs (miRNAs) have been shown to be differentially expressed in RCC and their role in RCC pathogenesis is emerging. This month, in BMC Medicine, Gleadle and colleagues show that certain miRNAs are regulated by VHL in either a hypoxia-inducible factor (HIF)-dependent or HIF-independent manner in RCC. They also show that miRNA expression correlates with the survival of RCC patients.In this commentary, we discuss the current understanding of the role of miRNAs in RCC and the different possible scenarios of their involvement in RCC pathogenesis. We also address their clinical significance as tumour markers, together with the potential use of miRNAs as therapeutic targets. Finally, we discuss some of the challenges that face the fast-evolving field of miRNAs, including the identification and validation of miRNA targets and the difficulties associated with establishing a link between miRNA expression and biological effects. A more thorough understanding of the biological nature of miRNAs and careful experimental planning will help us to reveal the complex role that miRNAs play in RCC pathogenesis. See research article: http://www.biomedcentral.com/1741-7015/8/64.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".