Identification of four key genes related to the diagnosis of chronic obstructive pulmonary disease using bioinformatics analysis

Abstract

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IntroductionChronic obstructive pulmonary disease (COPD) is projected to become the third leading cause of death worldwide. Despite extensive research over the past few decades, effective treatments remain elusive, making disease prevention and control a global challenge.MethodsThis study aimed to identify diagnostic key genes for COPD. We utilized the Gene Expression Omnibus database to obtain gene expression data specific to COPD. Differentially expressed genes (DEGs) were identified and analyzed through Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis. Integrated weighted gene co-expression network analysis was employed to examine related gene modules. To pinpoint key genes, we used SVM-RFE, RF, and LASSO.ResultsA total of 1782 DEGs were discovered, many of which were enriched in various biological pathways and activities. Four key genes—MRC1, BCL2A1, GYPC, and SLC2A3—were identified. We observed a significant difference in immune infiltration between COPD and normal groups, indicating potential interactions between immune cells and these genes. The identified key genes were further validated using external datasets.DiscussionOur findings suggest that MRC1, BCL2A1, GYPC, and SLC2A3 are potential biomarkers for COPD. Targeting these diagnostic genes with specific drugs may potentially offer new avenues for COPD management; however, this hypothesis remains preliminary and requires further investigation, as the study does not directly assess therapeutic interventions.

Keywords

• COPD
• enrichment analysis
• machine learning
• immune infiltration analysis
• drug prediction