miRNAs in bladder cancer: Its functional contributions and therapeutic potential.
Bibliographic record
Abstract
Background: Bladder cancer (BC) is one of the most prevalent malignancies worldwide, with a significant burden on healthcare systems due to its high recurrence rates and invasive diagnostic procedures. While cystoscopy and urine cytology remain the gold standards for BC diagnosis, they have notable limitations in sensitivity and invasiveness. MicroRNAs (miRNAs), small non-coding RNAs involved in post-transcriptional gene regulation, have emerged as promising biomarkers and therapeutic targets in BC. This systematic review explores the role of miRNAs in BC pathogenesis, diagnosis, prognosis, and therapeutic applications, with a focus on their molecular mechanisms and clinical potential. Methodology: A comprehensive literature review was conducted using PubMed, covering studies from January 2020 to August 2023. Inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses on miRNAs in BC. Exclusion criteria included non-human studies, non-urothelial malignancies, and studies with insufficient mechanistic insights. Data were extracted regarding miRNA functional roles, diagnostic potential, therapeutic implications, and interactions within the tumor microenvironment. Results: Multiple miRNAs were identified as key regulators in BC progression. Exosomal miR-217 inhibits ferroptosis, promoting tumor survival, while miR-3960 and miR-490-3p act as tumor suppressors by targeting oncogenic proteins. Cuproptosis-related miRNAs were linked to BC prognosis and immune modulation. Additionally, miR-146a-5p, derived from cancer-associated fibroblasts, enhances chemoresistance and tumor stemness. Several miRNA-based diagnostic panels demonstrated high sensitivity and specificity in distinguishing BC from benign conditions. Therapeutically, restoring tumor-suppressive miRNAs or inhibiting oncogenic miRNAs holds potential for personalized medicine in BC treatment. Conclusion: MiRNAs play a crucial role in BC pathogenesis, serving as both oncogenic drivers and tumor suppressors. Their stability in body fluids positions them as promising non-invasive biomarkers for early detection and disease monitoring. Furthermore, miRNA-based therapeutic strategies offer new avenues for targeted BC treatments.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".