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ORIGINAL RESEARCH article

Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1462868

Identifying Biomarkers of Endoplasmic Reticulum Stress and Analyzing Immune Cell Infiltration in Chronic Obstructive Pulmonary Disease Using Machine Learning

Provisionally accepted
Shuai-Yang Zhang Shuai-Yang Zhang 1Hang-Yu Yu Hang-Yu Yu 2*Jun Yan Jun Yan 1*
  • 1 Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
  • 2 Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China

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

    Background Endoplasmic reticulum stress (ERS) is a crucial factor in the progression of chronic obstructive pulmonary disease (COPD). However, the key genes associated with COPD and immune cell infiltration remain to be elucidated. Therefore, this study aimed to identify biomarkers pertinent to the diagnosis of ERS in COPD and delve deeper into the association between pivotal genes and their possible interactions with immune cells. Methods We selected the genetic data of 189 samples from the Gene Expression Omnibus database, including 91 control and 98 COPD samples. First, we identified the differentially expressed genes between patients with COPD and controls and then screened the ERS genes associated with COPD. Second, 22 core ERS genes associated with COPD were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model and Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the predictive effects of the screened core genes in COPD were evaluated. Third, we explored immune cell infiltration associated with COPD and conducted an in-depth analysis to explore the possible connections between the identified key genes and their related immune cells. Results A total of 66 differentially expressed endoplasmic reticulum stress-related genes (DE-ERGs) were identified in this study, among which 12 were upregulated and 54 were downregulated. The 22 key genes screened were as follows: AGR3,

    Keywords: chronic obstructive pulmonary disease, Endoplasmic Reticulum Stress, machine learning, immune cell infiltration BCHE, CBY1, CHRM3, CYP1B1, DCSTAMP

    Received: 10 Jul 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Zhang, Yu and Yan. 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:
    Hang-Yu Yu, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China
    Jun Yan, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 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.