Identification of the Regulatory Targets of miR-3687 and miR-4417 in Prostate Cancer Cells Using a Proteomics Approach
Bibliographic record
Abstract
MicroRNAs (miRNA) are ubiquitous non-coding RNAs that have a prominent role in cellular regulation. The expression of many miRNAs is often found deregulated in prostate cancer (PCa) and castration-resistant prostate cancer (CRPC). Although their expression can be associated with PCa and CRPC, their functions and regulatory activity in cancer development are poorly understood. In this study, we used different proteomics tools to analyze the activity of hsa-miR-3687-3p (miR-3687) and hsa-miR-4417-3p (miR-4417), two miRNAs upregulated in CRPC. PCa and CRPC cell lines were transfected with miR-3687 or miR-4417 to overexpress the miRNAs. Cell lysates were analyzed using 2D gel electrophoresis and proteins were subsequently identified using mass spectrometry (Maldi-MS/MS). A whole cell lysate, without 2D-gel separation, was analyzed by ESI-MS/MS. The expression of deregulated proteins found across both methods was further investigated using Western blotting. Gene ontology and cellular process network analysis determined that miR-3687 and miR-4417 are involved in diverse regulatory mechanisms that support the CRPC phenotype, including metabolism and inflammation. Moreover, both miRNAs are associated with extracellular vesicles, which point toward a secretory mechanism. The tumor protein D52 isoform 1 (TD52-IF1), which regulates neuroendocrine trans-differentiation, was found to be substantially deregulated in androgen-insensitive cells by both miR-3687 and miR-4417. These findings show that these miRNAs potentially support the CRPC by truncating the TD52-IF1 expression after the onset of androgen resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".