piRNAs and PIWI-Like Proteins in Renal Cell Carcinoma: A Systematic Review of Emerging Biomarkers and Potential Therapeutic Targets
Bibliographic record
Abstract
BACKGROUND: Renal cell carcinoma (RCC) is the most common kidney malignancy, often associated with poor prognosis due to late-stage diagnosis and metastasis. Recent research has identified PIWI-interacting RNAs (piRNAs) and PIWI-like proteins as potential biomarkers and therapeutic targets in cancer, including RCC. This systematic review aims to evaluate the role of piRNAs and PIWI-like proteins in RCC pathogenesis, prognosis, and treatment. METHODS: A systematic search of PubMed and ScienceDirect databases from 2014 to 2024 was conducted according to PRISMA guidelines. Eligible studies included randomized controlled trials, cohort studies, and case-control studies investigating piRNAs and PIWI-like proteins in RCC. A total of 13 studies were included, with the quality of studies assessed using the Newcastle-Ottawa Scale. RESULTS: Several piRNAs, such as piR-1742, piR-31115, and piR-57125, were found to be dysregulated in RCC, contributing to tumor proliferation, invasion, and metastasis through pathways like PI3K/AKT and epithelial-mesenchymal transition (EMT). PIWI-like proteins, particularly Piwi-like 1, were associated with advanced tumor stages and poor survival outcomes, making them significant prognostic markers. Mitochondrial piRNAs, including piR-34536 and piR-51810, were identified as novel biomarkers for RCC prognosis. CONCLUSION: piRNAs and PIWI-like proteins show great promise as emerging diagnostic and prognostic biomarkers, as well as therapeutic targets in RCC. While these molecules have demonstrated potential in RCC management, further research is needed to confirm their clinical relevance and mechanisms of action. Future studies should focus on larger, well-structured cohorts to validate these findings and explore therapeutic interventions targeting piRNAs and PIWI-like proteins.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".