Identification and Construction of a R-loop Mediated Diagnostic Model and Associated Immune Microenvironment of COPD through Machine Learning and Single-Cell Transcriptomics.

Abstract

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic inflammatory airway disease with high incidence and significant disease burden. R-loops, functional chromatin structure formed during transcription, are closely associated with inflammation due to its aberrant formation. However, the role of R-loop regulators (RLRs) in COPD remains unclear. Utilizing both bulk transcriptome data and single-cell RNA sequencing data, we assessed the diverse expression patterns of RLRs in the lung tissues of COPD patients. A lower R-loop score was found in patients with COPD and in neutrophils. 12 machine learning algorithms (150 combinations) identified 14 hub RLRs (CBX8, EHD4, HDLBP, KDM6B, NFAT5, NLRP3, NUP214, PAFAH1B3, PINX1, PLD1, POLB, RCC2, RNF213, and VIM) associated with COPD. A RiskScore based on 14 RLRs identified two distinct COPD subtypes. Patient groups at high risk of COPD (low R-loop scores) had a higher immune score and a significant increase in neutrophils in their immune microenvironment compared to low-risk groups. PD-0325901 and QL-X-138 represent prospective COPD treatments for high-risk (low R-loop score) and low-risk (high R-loop score) patients. Finally, RT-PCR experiments confirmed expression differences of 8 RLRs (EHD4, HDLBP, NFAT5, NLRP3, PLD1, PINX1, POLB, and VIM) in COPD mice lung tissue. R-loops significantly contribute to the development of COPD and constructing predictive models based on RLRs may provide crucial insight into personalized treatment strategies for patients with COPD.