ArticleBiochemical genetics2026
Multi-omics Analysis Reveals the Prognostic and Therapeutic Value of TGF-β Signaling-related Genes in Idiopathic Pulmonary Fibrosis.
Article in Biochemical genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease with a survival rate comparable to or worse than that of many cancers. Proper TGF-β signaling is essential for normal lung function, but its disruption plays a key role in pulmonary fibrosis and cancer progression. This study aims to elucidate the role of TGF-β signaling-related genes in the prognosis and treatment of IPF through multi-omics analysis. We obtained datasets from the GEO database and identified differentially expressed genes, followed by enrichment analyses. Core genes were identified using machine learning algorithms. Next, we evaluated the expression of core genes and their predictive ability for IPF, as well as their relationship with lung function and survival time. Then, mendelian randomization revealed core genes causally associated with IPF. Subsequently, pseudotime analysis, cell communication analysis and metabolic analysis were performed using single-cell data. Furthermore, we performed immune infiltration analysis to reveal the immune microenvironment of IPF. Finally, in vivo experiments validated the mRNA expression of the core genes. Two core genes (ACVRL1 and LTBP1) were identified through differential expression analysis and machine learning algorithms. Validation using multiple external datasets confirmed that these core genes exhibit stable expression patterns and have strong predictive ability for IPF patients. Further analysis revealed that the expression of these core genes correlates with lung function and survival time in IPF patients. Mendelian randomization analysis provided evidence of a causal link between ACVRL1 and IPF. Using eQTLGen data, our summary data-based mendelian randomization (SMR) analysis revealed a possible causal link between ACVRL1 and IPF. Similarly, using GTEx eQTL data, our SMR analysis revealed a potential causal link between ACVRL1 and IPF. Furthermore, single-cell data analysis highlighted differences in cell communication and metabolism between ACVRL1 + endothelial cell (EC) and ACVRL1-EC. Finally, RT-qPCR results support the potential role of core genes in IPF. This study provides new perspectives on the development of IPF and may help identify novel therapeutic targets. Further research may reveal how core genes influence cellular function and disease progression, providing novel insights into the intricate mechanisms underlying IPF.
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