Machine Learning Approach Identifies miRNA Biomarkers for Post Surgical Patient Stratification in Prostate Cancer.

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.