Identification of Mucus-Associated Molecular Subtypes of Chronic Obstructive Pulmonary Disease: A Latent Profile Analysis Based on MUC5B-Associated Genes.

Abstract

BackgroundChronic obstructive pulmonary disease (COPD) is a disease with high heterogeneity, which is a major challenge in clinical individualized treatment. A mucus phenotype is one of the main characteristics of COPD.Material/MethodsGene expression profiles of lung tissue samples were from the Lung Genomics Research Consortium. MUC5B-associated gene signatures were obtained based on a nonlinear feature screening algorithm. These signatures were used to fit a latent profile analysis (LPA) model to identify COPD molecular subtypes and build a subtype classifier to verify the subtypes. Then, we explored the characteristics of cilium assembly and beating signatures, transcriptome features, immune infiltration among the 3 subtypes by xCell, single-sample gene set enrichment analysis, network perturbation amplitude, and weighted gene co-expression network analysis algorithms. An external dataset was used to verify the above COPD subtypes.ResultsThree subtypes associated with mucus were identified by LPA and verified in an external dataset. Subtype 1 displayed higher T helper type 1 (Th1) and basophil infiltration, higher Th17/regulatory T cells (Tregs) ratio, a higher level of cilium assembly and beating, and lower mast cell and Treg infiltration. The subtypes 2 and 3 demonstrated higher macrophage M2 infiltration in lung tissue, while subtype 3 had higher neutrophil and eosinophil infiltration than subtype 2.ConclusionsOverall, this work identified 3 mucus-associated molecular subtypes related to MUC5B expression, which deepens the understanding of airway mucus secretion in COPD and potentially provides valuable information for precision therapy.