In silico Meta-Analysis of Circulatory microRNAs in Prostate Cancer
Bibliographic record
Abstract
Circulatory microRNAs (miRNAs) have emerged as a new class of non coding RNA molecules which regulate many crucial molecular and biological processes. We have aimed to shed light on the roles of circulatory miRNAs in Prostate Cancer (PCa) using an integrative in silico bioinformatics approach. We have described a new protocol for target prediction and functional analysis which was applied to 40 highly differentially dysregulatedcirculatory miRNAs in PCa. This framework comprises: (i) evidence of involvement of these circulatory miRNAs from previous literature and microarray analysis (ii) overlap of prediction results by target prediction tools, including miRTarBase, miRDB, DIANA- microT 4.0 and TargetScan (combining computational learning, alignment, interaction energy and statistical tests for minimization of false positives), (iii) gene ontology (GO) along with pathway enrichment analysis of the miRNA targets and their pathways and (iv) linking these pathways to oncogenesis and cancer hallmarks. More than 200 target genes and 40 regulatory pathways were retrieved and analysed which was followed by associating their roles with cancer hallmark processes. Wnt signalling, Cell cycle, MAPK signalling, Cadherin signalling, Integrin signalling and Ras pathways were some of the identified regulatory pathways during bioinformatics analysis. These signalling and developmental pathways crosstalk and regulate stem cell renewal thus indicating a definite role of circulatory miRNAs in PCa development. Our study identified miR-181, miR-9, Let-7 family, miR-26b circulatory miRNAs, to be contributing majorly in the oncogenic pathways, thus proposing their role as potential biomarkers in PCa initiation and progression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".