Exploring ABHD5 as a Lipid-Related Biomarker in Idiopathic Pulmonary Fibrosis: Integrating Machine Learning, Bioinformatics, and In Vitro Experiments

Yi Liao; Xiaying Peng; Yan Yang; Guanghong Zhou; Lijuan Chen; Yang Yang; Hongyan Li; Xianxia Chen; Shujin Guo; Qiunan Zuo; Jun Zou

doi:10.1007/s10753-024-02107-1

Exploring ABHD5 as a Lipid-Related Biomarker in Idiopathic Pulmonary Fibrosis: Integrating Machine Learning, Bioinformatics, and In Vitro Experiments

Inflammation. 2024 Jul 24. doi: 10.1007/s10753-024-02107-1. Online ahead of print.

Authors

Yi Liao¹, Xiaying Peng¹, Yan Yang¹, Guanghong Zhou¹, Lijuan Chen¹, Yang Yang¹, Hongyan Li¹, Xianxia Chen¹, Shujin Guo², Qiunan Zuo³, Jun Zou⁴

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
² Department of Health Management &, Institute of Health Management, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
³ Department of Geriatric Respiratory, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
⁴ Department of Respiratory and Critical Care Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. zoujun@med.uestc.edu.cn.

PMID: 39046603
DOI: 10.1007/s10753-024-02107-1

Abstract

Recent studies increasingly suggest a connection between lipids and idiopathic pulmonary fibrosis (IPF). This study was aimed at exploring potential lipid-related biomarkers for IPF and uncovering the mechanisms underlying pulmonary fibrosis. IPF-related datasets were retrieved from the GEO database, and the ComBat algorithm was used to merge multiple datasets and eliminate batch effects. Weighted gene co-expression network analysis (WGCNA) was utilized to identify modules and genes associated with IPF. Potential hub genes were determined by intersecting these genes with lipid-related genes from the GeneCards database. A machine learning-based integrative approach was developed to construct diagnostic and prognostic signatures, which were validated across several datasets. Additionally, single-cell sequencing data was used to validate the expression differences of core IPF-related genes across various cell types. The effect of ABHD5 on fibroblasts was assessed using the cell counting kit-8, 5-ethynyl-2'-deoxyuridine, and cell scratch assays. The expression levels of fibrotic factors were measured using real-time quantitative polymerase chain reaction and western blot analysis. WGCNA identified a red module potentially related to IPF, and the intersection with lipid-related genes yielded 51 hub genes. These genes were used to build diagnostic and prognostic models that demonstrated robust validation capabilities across multiple datasets. Single-cell sequencing analysis revealed low expression of ABHD5 in the lung tissues of IPF patients, with a higher proportion of fibroblasts exhibiting low ABHD5 expression. Cell experiments showed that under the influence of TGF-β1, knockdown of ABHD5 slightly promoted fibroblast proliferation. Additionally, fibroblasts with low ABHD5 expression exhibited enhanced migratory capabilities and secreted more fibrotic factors. Lipid-related diagnostic and prognostic models for IPF were developed, and ABHD5 may serve as a potential biomarker. Low ABHD5 expression could potentially accelerate the progression of pulmonary fibrosis.

Keywords: ABHD5; Idiopathic pulmonary fibrosis; Lipid; Machine learning.

Grants and funding

24ZDYF0611/Key Research and Development Program of Sichuan Province