Integrative multi-omics and machine learning identify CHRNA1 putative circadian-immune hub in COPD

Abstract

Circadian rhythm disruption is increasingly recognized as a contributor to chronic inflammatory disorders; however, its specific significance and underlying mechanisms in chronic obstructive pulmonary disease (COPD) remain unclear. This study aimed to identify circadian rhythm-associated biomarkers in COPD and explore their diagnostic value, immune correlations, and therapeutic potential.

Introduction

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder characterized by persistent airflow limitation and symptoms including dyspnoea, cough, and sputum production The Global Burden of Disease (GBD) Study reported a rising worldwide prevalence from 100.54 million cases in 1990 to 213.39 million in 2021, with disability-adjusted life-years (DALYs) increasing from 56.86 million to 79.78 million over the same period

Methods

Data acquisition and preprocessing

This study utilized four publicly available lung tissue microarray datasets related to chronic obstructive pulmonary disease (COPD) from the Gene Expression Omnibus (GEO) database (accession URL: http://www.ncbi.nlm.nih.gov/geo/): GSE151052 (COPD: n = 77; Control: n = 40), GSE38974 (COPD: n = 23; Control: n = 9), GSE76925 (COPD: n = 111; Control: n = 40), and GSE47460

Identification of circadian rhythm-related gene sets

Differential expression analysis between COPD patients and healthy controls in the batch corrected discovery cohort was performed using the limma package (v3.58.1). Differentially Expressed Genes (DEGs) were identified using the criteria of |log2FC| > 0.585 and a Benjamini Hochberg adjusted P value (FDR) < 0.05.

Results

Differentially expressed circadian rhythm genes

A flowchart of the study is presented in  The three discovery datasets (GSE151052, GSE38974, and GSE76925) were integrated to generate a unified expression matrix comprising 300 lung tissue samples (COPD, n = 211; control, n = 89).

Machine-learning–driven feature selection

Using an ensemble machine-learning strategy, we systematically identified COPD-specific circadian rhythm-related genes. Lasso regression screened out 8 key genes (GMNN, HBB, EGR1, TSEN15, SNX10, CHRNA1, EGR3, SLC6A4) The SVM-RFE algorithm identified nine optimal genes 

Discussion

The core pathological features of COPD are primarily driven by chronic immune-inflammatory processes arising from gene-environment interactions, which are closely associated with abnormal immune cell infiltration and dysregulation In mammals, circadian rhythms are orchestrated through transcriptional-translational feedback loops centered around core clock genes, which help maintain homeostasis in various physiological processes—including immune and inflammatory responses.

Conclusion

This study suggests that circadian rhythm related genes, particularly CHRNA1, may participate in the pathogenesis of COPD through neuro immune crosstalk. The exploratory risk stratification model constructed based on the eight feature genes demonstrated favorable risk stratification capability.

Citation: Zhang L, Li Z, Xia T, Yang T, Fu J, Lu Y, et al. (2026) Integrative multi-omics and machine learning identify CHRNA1 putative circadian-immune hub in COPD. PLoS One 21(7): e0353838. https://doi.org/10.1371/journal.pone.0353838

Editor: Atsushi Asakura, University of Minnesota Medical School, UNITED STATES OF AMERICA

Received: February 4, 2026; Accepted: June 29, 2026; Published: July 31, 2026

Copyright: © 2026 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The datasets analyzed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository under accession numbers GSE151052, GSE38974, GSE76925, GSE47460, and GSE136831. All custom R scripts generated for data processing, analysis, and figure generation are included in S2_Code.zip in the Supporting Information files. The minimal data set underlying the findings is fully available within the manuscript and its Supporting Information files.

Funding: This work was supported by the Natural Science Foundation of Heilongjiang Province (Grant No. PL2024H081 to KH) and the Horizontal Cooperation Project of the Second Affiliated Hospital of Harbin Medical University (Grant No. 20220815 to KH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.