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Background <p>MicroRNA (miRNA) biomarker studies in Alzheimer’s disease (AD) typically assume monotonic relationships between expression levels and disease status, overlooking the non-linear, context-dependent nature of miRNA regulatory networks. This simplification limits mechanistic insight and clinical translation. We aimed to characterise non-linear contribution patterns of miRNAs across the AD continuum using explainable machine learning and to define stage-specific “operating windows” where individual miRNAs drive classification.</p> Methods <p>Candidate miRNAs were prioritised from APPtg/TAUtg mouse hippocampus (accession number GSE110743) using minimum-redundancy-maximum-relevance selection. A three-miRNA panel (miR-155-5p, miR-339-5p, and miR-455-5p) was validated in human serum (GSE120584; AD, MCI, and healthy controls). Linear and non-linear classifiers were compared, and SHAP dependence analysis was used to quantify sample-level contributions across expression ranges.</p> Results <p>Non-linear models (SVM-RBF and k-NN) consistently outperformed linear classifiers, with discrimination strongest for MCI vs. healthy controls (AUC: 0.844). SHAP analysis revealed that miR-155-5p functions as a stable primary driver across disease stages, whereas miR-339-5p and miR-455-5p act as context-dependent modulators contributing only within restricted expression ranges. Each miRNA exhibited distinct, stage-specific non-linear operating windows with threshold effects and inflection points rather than uniform dose-response patterns.</p> Conclusions <p>This study reframes circulating miRNAs as dynamic, interaction-governed signals rather than static biomarkers. The operating window framework provides interpretable, threshold-aware guidance for clinical decision-making and supports stage-sensitive early screening strategies.</p>

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Discovering non-linear dynamics of miRNAs in Alzheimer’s disease-related cognitive impairment: a cross-species approach with explainable machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Seong-Hun Lee,
  • Shin Kim,
  • Seung-Bo Lee

摘要

Background

MicroRNA (miRNA) biomarker studies in Alzheimer’s disease (AD) typically assume monotonic relationships between expression levels and disease status, overlooking the non-linear, context-dependent nature of miRNA regulatory networks. This simplification limits mechanistic insight and clinical translation. We aimed to characterise non-linear contribution patterns of miRNAs across the AD continuum using explainable machine learning and to define stage-specific “operating windows” where individual miRNAs drive classification.

Methods

Candidate miRNAs were prioritised from APPtg/TAUtg mouse hippocampus (accession number GSE110743) using minimum-redundancy-maximum-relevance selection. A three-miRNA panel (miR-155-5p, miR-339-5p, and miR-455-5p) was validated in human serum (GSE120584; AD, MCI, and healthy controls). Linear and non-linear classifiers were compared, and SHAP dependence analysis was used to quantify sample-level contributions across expression ranges.

Results

Non-linear models (SVM-RBF and k-NN) consistently outperformed linear classifiers, with discrimination strongest for MCI vs. healthy controls (AUC: 0.844). SHAP analysis revealed that miR-155-5p functions as a stable primary driver across disease stages, whereas miR-339-5p and miR-455-5p act as context-dependent modulators contributing only within restricted expression ranges. Each miRNA exhibited distinct, stage-specific non-linear operating windows with threshold effects and inflection points rather than uniform dose-response patterns.

Conclusions

This study reframes circulating miRNAs as dynamic, interaction-governed signals rather than static biomarkers. The operating window framework provides interpretable, threshold-aware guidance for clinical decision-making and supports stage-sensitive early screening strategies.