Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Korea.
Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, 21565, Korea.
BMC Med Genomics. 2023 Aug 22;16(1):195. doi: 10.1186/s12920-023-01636-2.
Type 2 diabetes mellitus (T2DM) affects approximately 451 million adults globally. In this study, we identified the optimal combination of marker candidates for detecting T2DM using miRNA-Seq data from 95 samples including T2DM and healthy individuals.
We utilized the genetic algorithm (GA) in the discovery of an optimal miRNA biomarker set. We discovered miRNA subsets consisting of three miRNAs for detecting T2DM by random forest-based GA (miRDM-rfGA) as a feature selection algorithm and created six GA parameter settings and three settings using traditional feature selection methods (F-test and Lasso). We then evaluated the prediction performance to detect T2DM in the miRNA subsets derived from each setting.
The miRNA subset in setting 5 using miRDM-rfGA performed the best in detecting T2DM (mean AUROC = 0.92). Target mRNA identification and functional enrichment analysis of the best miRNA subset (hsa-miR-125b-5p, hsa-miR-7-5p, and hsa-let-7b-5p) validated that this combination was involved in T2DM. We also confirmed that the targeted genes were negatively correlated with the clinical variables related to T2DM in the BxD mouse genetic reference population database.
Using GA in miRNA-Seq data, we identified the optimal miRNA biomarker set for T2DM detection. GA can be a useful tool for biomarker discovery and drug-target identification.
全球约有 4.51 亿成年人患有 2 型糖尿病（T2DM）。在这项研究中，我们利用 95 个样本（包括 T2DM 和健康个体）的 miRNA-Seq 数据，确定了用于检测 T2DM 的标记物候选物的最佳组合。
我们利用遗传算法（GA）在发现最佳 miRNA 生物标志物集方面的作用。我们使用基于随机森林的 GA（miRDM-rfGA）作为特征选择算法发现了由三个 miRNA 组成的 miRNA 亚群，用于检测 T2DM，并创建了六个 GA 参数设置和三个使用传统特征选择方法（F 检验和 Lasso）的设置。然后，我们评估了从每个设置中得出的 miRNA 子集中检测 T2DM 的预测性能。
miRDM-rfGA 在设置 5 中使用的 miRNA 子集中，在检测 T2DM 方面表现最佳（平均 AUROC=0.92）。对最佳 miRNA 子集中（hsa-miR-125b-5p、hsa-miR-7-5p 和 hsa-let-7b-5p）的靶 mRNA 鉴定和功能富集分析验证了这一组合与 T2DM 有关。我们还证实，靶向基因与 BxD 小鼠遗传参考群体数据库中与 T2DM 相关的临床变量呈负相关。
我们使用 GA 在 miRNA-Seq 数据中确定了用于检测 T2DM 的最佳 miRNA 生物标志物集。GA 可以成为生物标志物发现和药物靶点识别的有用工具。
