Abstract
Pulmonary fibrosis is characterized by progressive lung scarring, leading to a decline in lung function and an increase in morbidity and mortality. This study leverages single-cell sequencing and machine learning to unravel the complex cellular and molecular mechanisms underlying pulmonary fibrosis, aiming to improve diagnostic accuracy and uncover potential therapeutic targets. By analyzing lung tissue samples from pulmonary fibrosis patients, we identified distinct cellular phenotypes and gene expression patterns that contribute to the fibrotic process. Notably, our findings revealed a significant enrichment of activated B cells, CD4 T cells, macrophages, and specific fibroblast subpopulations in fibrotic versus normal lung tissue. Machine learning analysis further refined these observations, resulting in the development of a diagnostic model with enhanced precision, based on key gene signatures including TMEM52B, PHACTR1, and BLVRB. Comparative analysis with existing diagnostic models demonstrates the superior accuracy and specificity of our approach. Through In vitro experiments involving the knockdown of PHACTR1, TMEM52B, and BLVRB genes demonstrated that these genes play crucial roles in inhibiting the expression of α-SMA and collagen in lung fibroblasts induced by TGF-β. Additionally, knockout of the PHACTR1 gene reduced inflammation and collagen deposition in a bleomycin-induced mouse model of pulmonary fibrosis in vivo. Additionally, our study highlights novel gene signatures and immune cell profiles associated with pulmonary fibrosis, offering insights into potential therapeutic targets. This research underscores the importance of integrating advanced technologies like single-cell sequencing and machine learning to deepen our understanding of pulmonary fibrosis and pave the way for personalized therapeutic strategies.