Identification of potential COPD genes based on multi-omics data at the functional level

Abstract

Chronic obstructive pulmonary disease (COPD) is a complex disease, which involves dysfunctions in multi-omics. The changes in biological processes, such as adhesion junction, signaling transduction, transcriptional regulation, and cell proliferation, will lead to the occurrence of COPD. A novel systematic approach MMMG (Methylation–MicroRNA–MRNA–GO) was proposed to identify potential COPD genes by integrating function information with a methylation profile, a microRNA expression profile and an mRNA expression profile. 8 co-functional classes and 102 potential COPD genes were identified. These genes displayed a high performance in classifying COPD patients and normal samples, revealed COPD-related pathways, and have been confirmed to be associated with COPD by Matthews correlation coefficient (MCC)-values, literature, an independent data set, and pathways. The MMMG method that analyzed multi-omics data at the functional level could effectively identify potential COPD genes. These potential COPD genes would provide in-depth insights into understanding the complexity of COPD genome landscapes, improve the early diagnostics, and guide new efforts to develop therapeutics in the future.