Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets

被引:0
|
作者
Wen, Zhuofeng [1 ]
Liang, Weixuan [2 ]
Yang, Ziyang [3 ]
Liu, Junjie [4 ]
Yang, Jing [1 ]
Xu, Runge [2 ]
Lin, Keye [2 ]
Pan, Jia [2 ]
Chen, Zisheng [1 ]
机构
[1] Guangzhou Med Univ, Qingyuan Hosp, Qingyuan Peoples Hosp, Sch Clin Med 6,Dept Resp & Crit Care Med, Qingyuan, Peoples R China
[2] Guangzhou Med Univ, Sch Clin Med 1, Guangzhou, Peoples R China
[3] Guangzhou Med Univ, Sch Clin Med 3, Guangzhou, Peoples R China
[4] Guangzhou Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China
关键词
Idiopathic pulmonary fibrosis; Multi-omics; Therapeutic targets; Genetic insights; Druggability; LIGAND EFFICIENCY; POLYGENIC SCORES; EQTL;
D O I
10.1186/s12967-025-06368-8
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
ObjectiveTo identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.MethodWe integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.ResultWe identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.ConclusionWe identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.
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页数:20
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