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bioRxiv · 10.64898/2026.01.10.698764

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

Agrawal, S.·Mitra, P.
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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.

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Agrawal, S., Mitra, P.. 2026-01-12. Identification of a Novel miRNA Expression Signature for Lung Adenocarcinoma Using Systematic Machine Learning Optimization. https://doi.org/10.64898/2026.01.10.698764

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