Machine Learning Approach Identifies miRNA Biomarkers for Post Surgical Patient Stratification in Prostate Cancer
Bibliographic record
Abstract
INTRODUCTION: Effective management of post-prostate cancer is hindered by the limitations of current prognostic tools in accurately assessing disease aggressiveness. Radical prostatectomy remains a standard treatment, but some patients develop biochemical recurrence and metastasis, underscoring the need for improved postsurgical prognostic tools. METHODS: This investigation involved sequencing data derived from 38 matched prostate cancer patients who had undergone RP. Initial statistical analysis helped identify the most significant miRNAs, which were further subjected to unsupervised clustering and stepwise selection. A linear discriminant analysis (LDA) model was then trained and tested using a miRNA combination method to pinpoint biomarkers predictive of metastasis. RESULTS: Out of 1123 miRNAs initially identified, 519 were selected as high-confidence candidates. Parametric analysis of these miRNAs discerned 41 that effectively distinguished between patients who developed metastasis postoperatively and those who did not. Utilizing LDA, this study harnessed 41 miRNAs in a combinatorial approach, identifying eight key miRNAs (hsa-miR-106b-3p, hsa-miR-769-5p, hsa-miR-182-5p, hsa-miR-194-5p, hsa-miR-345-5p, hsa-miR-183-3p, hsa-miR-200a-3p, hsa-miR-301a-3p) that collectively stratified the metastatic group from control with up to 91% accuracy. This model's effectiveness was supported by a receiver operating characteristic analysis, demonstrating an area under the curve of 80% or higher for the best miRNA combinations. Notably, the performance of this eight-miRNA panel was consistent with CAPRA-based risk stratification. CONCLUSION: Our study presents a miRNA-based machine learning model that distinguishes metastatic from non-metastatic prostate cancer patients following surgery. The panel's alignment with CAPRA underscores its clinical relevance and highlights its potential for integration into future clinical frameworks.
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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) | 0.000 | 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".