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ORIGINAL RESEARCH article

Front. Genet.
Sec. Applied Genetic Epidemiology
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1499996

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

Provisionally accepted
Jinxia Li Jinxia Li *Liu Xiuming Liu Xiuming Liu Yonghu Liu Yonghu
  • Department of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, China

The final, formatted version of the article will be published soon.

    Chronic 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. This 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 then used to examine related gene modules. To pinpoint key genes, we employed SVM-RFE, RF, and LASSO. A 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. Our 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

    Received: 22 Sep 2024; Accepted: 31 Jan 2025.

    Copyright: © 2025 Li, Xiuming and Yonghu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jinxia Li, Department of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.