Dimensionality reduction and metabolite panel derivation in urinary metabolomics based on Random Forest with Gini index feature selection.

Abstract

Untargeted urinary metabolomics represents a promising approach for investigating metabolic alterations associated with oncogenic processes such as breast cancer (BC). However, the stable selection of informative m/z features remains a central challenge in biomarker-oriented studies, particularly in the context of early BC detection and population-level screening. Urine samples from two independent cohorts (n = 50 and n = 75) and analyzed using distinct UHPLC-QTOF-ESI⁺ mass spectrometry workflows, yielded 224 and 129 aligned m/z features, respectively. Classification and embedded feature selection were implemented within a leakage-controlled Random Forest (RF) framework using Gini index-based importance ranking. Model evaluation incorporated repeated train-test splits and cross-validation to ensure methodological rigor and minimize overfitting. By consistently applying the same RF-based analytical framework to two analytically distinct cohorts generated under different chromatographic separation conditions, we demonstrate that a unified supervised strategy can achieve comparably high classification performance despite differences in feature dimensionality. Further, controlled dimensionality reduction identified compact panels of 25 m/z features per cohort while preserving classification performance. Importantly, stability was maintained after feature reduction, with strong accuracy, F1 scores, and receiver operating characteristic and precision-recall characteristics observed in both full and reduced models. This cross-cohort consistency indicates that the discriminative signal captured by the RF approach is not cohort-specific nor dependent on a particular separation workflow, but rather reflects reproducible metabolic patterns associated with BC. The stability of feature selection was further supported by substantial overlap between RF-derived Gini importance rankings and variable importance in projection (VIP) scores obtained from partial least squares discriminant analysis (PLS-DA) in MetaboAnalyst 5.0, indicating concordance across distinct supervised multivariate frameworks. Collectively, these findings highlight the advantage of a unified, supervised tree-based strategy capable of delivering stable classification and interpretable dimensionality reduction across independent untargeted metabolomics platforms, providing a structured and transferable framework for metabolomics-driven biomarker discovery and future clinical validation.