The effectiveness of miRNAs as potential non-invasive liquid biomarkers for the diagnosis and prognosis of prostate cancer: A systematic review
Bibliographic record
Abstract
Background: Prostate cancer (PCa) is one of the most prominent cancers worldwide. However, many limitations in its diagnostic and prognostic protocols lead to severe unreliability. This research aims to evaluate the current literature to better understand the effectiveness of miRNAs as potential diagnostic and prognostic non-invasive liquid biomarkers of prostate cancer. Methods: A systematic review was conducted by thorough searches on the Omni and PubMed database for articles in the past five year, that met specific eligibility criteria. Characteristics of the 18 chosen studies varied, as the goal was to include diverse populations and methodologies to encompass all the current literature. Key differentially expressed miRNAs were extracted from the research, including their dysregulation signatures and associated statistical values. Results: MiRNA panels, in conjunction with current diagnostic protocols and clinicopathological factors, display the most promise as a future diagnostic tool for PCa. More specifically, a 2-miRNA and 5-cs-miRPs panel show remarkable potential for future use in prostate cancer screening and diagnosis. Some limitations of the supporting evidence include heterogeneity between studies’ methodology and analysis, lack of standardization in the current protocols of miRNA collection and quantification, and the influence of genetic and environmental factors on the expression of these biomarkers. Conclusion: Future research should validate which miRNAs to include in a panel, how to standardize their storage, collection, and quantification, and how to incorporate them into the current protocols. Clinical applications of miRNAs as non-invasive liquid biomarkers can contribute to early cancer detection and prevention, thus improving outcomes for prostate cancer patients.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".