Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.

IF 2.3 Q3 ENGINEERING, BIOMEDICAL Biomedical Engineering and Computational Biology Pub Date : 2024-09-06 eCollection Date: 2024-01-01 DOI:10.1177/11795972241277639
Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Sivasankari Thilagar, Deepavalli Arumuganainar, Deepti Shrivastava, Artak Heboyan
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Abstract

Background: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.

Methods: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.

Results: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R 2 = .952, RMSE = 0.332), Linear Regression (R 2 = .949, RMSE = 0.343), and SVM (R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.

Conclusion: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.

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基于深度学习的暴露于脂多糖的牙周韧带干细胞外泌体微RNA分析揭示了凋亡和炎症体衍生途径的激活。
背景:LPS(尤其是来自牙龈脓疱疮杆菌的 LPS)会增加牙周炎症因子的产生,加剧对牙周组织的损害。来自牙周韧带干细胞的外泌体改变了细菌 LPS 带来的再生和修复。外泌体携带的 MiRNA 会影响受体细胞的表观遗传功能。因此,本研究旨在利用深度学习算法发现细菌LPS暴露的PDLSC干细胞中的新型微RNA生物标记物,以了解其激活途径:方法:利用NCBI GEO DATA SET GSE163489,发现健康和LPS诱导的PDLSC细胞中差异表达最多的微RNA。深度学习分析采用了随机森林(Random Forest)、人工神经网络(Artificial Neural Network c)、支持向量机(SVM)和线性回归(Linear Regression)模型,并在橙色数据挖掘工具包中实施,从而确定了新型 microRNA 生物标记。橙色数据挖掘工具包用于对 microRNA 表达数据进行深度学习分析,为分类、回归和聚类等机器学习任务提供了用户友好型环境:随机森林是最优秀的模型,与神经网络(R 2 = .952,RMSE = 0.332)、线性回归(R 2 = .949,RMSE = 0.343)和 SVM(R 2 = .931,RMSE = 0.398)相比,随机森林的 R 2 得分最高(0.985),RMSE 最低(0.189)。这表明随机森林具有捕捉 microRNA 表达数据中潜在模式的卓越能力。鉴于其强大的性能,随机森林有望识别新型生物标记物、开发更准确的诊断工具,并有可能指导对牙周病患者进行分层,以采取有针对性的治疗干预措施:当前的研究利用对 microRNA 表达数据的深度学习分析,确定了与炎症小体激活和抗凋亡通路相关的新型生物标志物。这些发现有望指导牙周病新型治疗策略的开发。然而,未来的研究还需要使用独立的数据集和实验方法来验证这些生物标志物。
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