International Journal of COPD
Revealing Biomarkers and Candidate Drugs for Chronic Obstructive Pulmonary Disease Using Bioinformatics and Deep Learning
By Tianbiao Wu, 王爱平 and Jinggang Xu.
Abstract
Background
There is a dearth of precise treatments capable of effectively decelerating the progression of chronic obstructive pulmonary disease (COPD). Consequently, there is an urgent necessity to discover novel potential biomarkers and rapidly translatable therapeutic drugs.
Methods
The GSE57148, GSE38974, and GSE173896 datasets were included in this study. Cell clustering and annotation were conducted via Seurat, and disease-related genes were screened through an integrated approach combining Scissor-based prioritization, differential expression profiling, and weighted gene co-expression network analysis. Subsequently, 12 machine learning algorithms were used to construct models and identify biomarkers. The mechanisms of immune microenvironment remodeling were analyzed through cell communication and pseudo-time series analysis. Furthermore, an in vitro COPD model was constructed based on BEAS-2B cells and validated by RT-qPCR. Finally, a deep learning model and molecular docking were utilized to conduct AI-driven drug repurposing screening targeting biomarkers.
