Identification of a Novel miRNA Expression Signature for Lung Adenocarcinoma Using Systematic Machine Learning Optimization

Abstract

Lung adenocarcinoma (LUAD), the most common lung cancer subtype, urgently requires reliable microRNA (miRNA) biomarkers for early detection and therapy. This study introduces a machine learning framework integrating feature stability analysis, precision-recall curves, and resampling strategies (e.g., SMOTE) to robustly identify miRNA signatures from imbalanced TCGA-LUAD data (564 samples: 519 tumor, 45 normal). We selected 8 stable features (hsa-mir-143, hsamir-210, hsa-mir-21, hsa-mir-183, hsa-mir-96, hsa-mir-182, hsa-mir-130b, hsa-mir-141) with 100% cross-fold stability via 10-fold cross-validation. A Random Forest classifier yielded excellent training performance (AUC: 1.0000; accuracy: 98%) and good generalization on an independent test set (AUC: 0.8438; accuracy: 75%). Consistent feature importance across folds supports biological relevance over overfitting. The framework mitigates class imbalance, high dimensionality, and distribution shifts--key hurdles in biomarker discovery. These reproducible miRNAs hold promise as non-invasive diagnostic tools, though external validation underscores generalization challenges across cohorts.