Curcumenol exerts anti-pulmonary fibrosis effects through modulation of the immune signature target SPP1: based on...

The efficacy of Curcuma wenyujin (C. wenyujin) volatile oil components in the treatment of lung diseases, including pulmonary fibrosis (PF), is gradually being recognized. However, the anti-PF potential and underlying mechanisms of curcumenol (Cur), one of the Q-markers of C. wenyujin, remain underexplored.

Gene expression profiles of PF were mined using an integrated bioinformatics approach to identify immune signature genes associated with disease development. Additionally, molecular dynamic simulation and experiments were performed to assess the potential of Cur to modulate the immune signature genes to exert anti-PF effects.

The GSE53845 and GSE10667 datasets were downloaded from the GEO database and merged as a training set to identify PF-related differentially expressed genes. Immunity-related genes were obtained from ImmPort Portal database, and these genes were intersected to obtain the immunity differential genes, which were then enriched for analysis. Key genes were screened using CytoHubba plug in Cytoscape software. The biomarkers were further identified and screened using LASSO regression and SVM-RFE algorithms, with discriminatory ability assessed by receiver operator characteristic (ROC) curve analysis. The column line diagram of the characterized genes and the calibration curve were established for validation. The GSE24206 dataset was used for further validation. Immune cell infiltration analysis and correlation analyses between immune cells and characteristic biomarkers were performed using the Cibersort algorithm. Molecular docking and molecular dynamics simulations were performed to assess the binding affinity and stability of Cur with core targets. In vitro and in vivo experiments were carried out to validate the findings.

A total of 152 PF immune-related differential genes were identified, with the enrichment analysis revealing involvement in multiple signaling pathways, cellular components, and molecular functions. A total of 3 characterized genes, including IL6R, CXCL12 and SPP1, were identified using integrated machine learning approaches. ROC curve validation results confirmed the discriminatory capability of these genes. Immune cell infiltration analysis showed significant associations between multiple immune cells and the identified biomarkers. Molecular docking and dynamics simulations demonstrated stable binding between Cur and key targets. In vitro cellular interference assays demonstrated that Cur could modulate the key target SPP1 to improve PF. In vivo experiments further supported these findings.

Three immune-signature biomarkers of PF were identified by integrated bioinformatics and machine learning approaches, with SPP1 emerging as a key target. Cur regulates the immune target SPP1, thereby exerting anti-PF effects. This study provides new insights into the pathogenesis, targeted therapy, and potential drug development for PF.