🤖 AI Summary
Traditional single-omics approaches struggle to fully elucidate the complex regulatory mechanisms of long non-coding RNAs (lncRNAs) in type 2 diabetes (T2D). This study proposes the first multimodal machine learning framework integrating lncRNA expression profiles, secondary structures, and sequence features. By combining eight classifiers, hierarchical cross-validation, and SHAP-based interpretability analysis, the framework enables precise association mapping from population to individual levels across two independent cohorts. The approach identifies multiple T2D-significant lncRNAs—including GAS5, XIST, MEG3, and ANRIL—with MEG3 consistently emerging as the top cross-cohort driver according to SHAP values. These findings not only corroborate results from conventional statistical methods but also yield a higher-resolution regulatory landscape, advancing the potential of lncRNAs in T2D precision medicine.
📝 Abstract
Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D). We investigated ten literature reported lncRNAs associated with T2D: MALAT1, MEG3, MIAT, ANRIL, GAS5, KCNQ1OT1, H19, BCYRN1, XIST, and HOTAIR across two independent population-based RNA-seq cohorts. Single-omics approaches provide an incomplete view of disease biology, therefore, an integrative multi-feature framework was developed, extracting expression, secondary-structure, and sequence features for each lncRNA. Eight machine learning (ML) classifiers were evaluated under stratified k-fold, leave-one-out cross-validation (LOOCV), and repeated hold-out schemes to ensure robust performance estimation. SHAP analysis was applied for subject-level association interpretation. In one cohort, GAS5 and XIST expression features, along with GAS5, MEG3, and ANRIL sequence features, were found to be associated with T2D, while MALAT1 expression and KCNQ1OT1, ANRIL, and MEG3 sequence features were found to be associated in the second cohort. MEG3 was identified by SHAP as the dominant lncRNA in both cohorts. ML results were consistent with established statistical methods while additionally providing population- and subject-level disease association profiles linked to specific molecular feature types. The proposed framework advances mechanistic understanding of T2D and supports lncRNA-based precision medicine.