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

• Published in: Biomedical engineering and computational biology Vol. 15; p. 11795972241277639
• Main Authors: Yadalam, Pradeep Kumar, Anegundi, Raghavendra Vamsi, Thilagar, Sivasankari, Arumuganainar, Deepavalli, Shrivastava, Deepti, Heboyan, Artak
• Format: Journal Article
• Language: English
• Published: United States SAGE Publishing 01.01.2024
• Subjects: deep learning; Periodontium; apoptosis; micro-RNA; biomarkers
• ISSN: 1179-5972; 1179-5972
• DOI: 10.1177/11795972241277639

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. 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. Random Forest emerged as the superior model, achieving the highest score (.985) and the lowest RMSE (0.189) compared to Neural Networks (  = .952, RMSE = 0.332), Linear Regression (  = .949, RMSE = 0.343), and SVM (  = .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. 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.