A Brief Review of MicroRNA Profiling in Human Prostate Cancer Tissues and Plasma
Bibliographic record
Abstract
(1) Background: The gold standard, prostate-specific antigen (PSA) screening lacks the sensitivity and specificity required for confident, early prostate-cancer detection. MicroRNAs (miRNAs) are small, highly stable, non-coding RNAs whose expression changes reproducibly in malignancy and therefore offer promise as minimally invasive biomarkers. Although prostate cancer biopsies are the gold standard for prostate cancer diagnosis, limitations in the field continue to persist. Since circulating fluids can also be a source of miRNA biomarkers, we investigated the overlap between miRNAs enriched in prostate cancer tissue and those isolated from the plasma of patients with prostate cancer. (2) Methods: We synthesized the published literature (PubMed, Google Scholar, ResearchGate, 2005–April 2025) and re-analyzed three Gene Expression Omnibus (GEO) datasets (GSE54516, GSE21032—tissue; GSE206793—plasma) to identify miRNAs consistently dysregulated in prostate cancer tissue and circulation. (3) Results: Of the 318 screened full-text articles, 24 met the inclusion criteria. From the GEO reanalysis (false-discovery-rate < 0.05, |log2FC| ≥ 1), 219 and 326 miRNAs were differentially expressed in tissue, whereas 12 were altered in plasma. Two miRNAs—miR-449b and miR-455-3p—were common in both compartments, highlighting their translational potential as liquid biopsy surrogates of tumor biology. (4) Conclusions: We summarize functional evidence for leading tumor-suppressive (e.g., miR-205, miR-23b, miR-455-3p) and oncogenic (e.g., miR-21, miR-182, miR-449b) candidates, discuss their intersection with the androgen-receptor, TGF-β, WNT/β-catenin, and PI3K-AKT signaling, and outline outstanding requirements for the clinical qualification of miRNA panels in prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".