Mechanisms of Tianlongjie in the Treatment of Idiopathic Pulmonary Fibrosis Using Network Pharmacology Combined with Machine Learning

Haiqing JING; Chunyan YANG; Yanrui WU; Bin CHEN; Ting SU; Jie LIU; Yi Fu

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Article Navigation > Journal of Kunming Medical University > 2026 > Accepted Manuscript

Haiqing JING, Chunyan YANG, Yanrui WU, Bin CHEN, Ting SU, Jie LIU, Yi Fu. Mechanisms of Tianlongjie in the Treatment of Idiopathic Pulmonary Fibrosis Using Network Pharmacology Combined with Machine Learning[J]. Journal of Kunming Medical University.

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Mechanisms of Tianlongjie in the Treatment of Idiopathic Pulmonary Fibrosis Using Network Pharmacology Combined with Machine Learning

Department of Pulmonary Diseases，Third Affiliated Hospital of Yunnan University of Chinese Medicine/Kunming Hospital of Traditional Chinese Medicine，Kunming Yunnan 650500，China

Received Date: 2026-05-14
Available Online: 2026-09-18

Abstract

Abstract

Objective To systematically investigate the potential mechanisms of Tianlongjie in the treatment of idiopathic pulmonary fibrosis （IPF） using network pharmacology combined with machine learning methods. Methods The active components and therapeutic targets of Tianlongjie （Rhodiola rosea, Lumbricus, and Dragon’s blood） were screened using the HERB and SymMap databases. Differentially expressed genes （DEGs） associated with IPF were identified from the GEO datasets （GSE150910 and GSE32537）, and the intersection of these datasets was used to obtain potential therapeutic targets. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses were performed to elucidate their biological functions and signaling pathways, and a protein–protein interaction （PPI） network was constructed. Key targets were screened using LASSO regression and SVM-RFE algorithms and validated by ROC curve analysis. Gene set enrichment analysis （GSEA）, immune infiltration analysis, and construction of a ceRNA regulatory network were subsequently performed. Molecular docking was used to validate the binding capacity of key components to their targets, and in vivo experiments were conducted using a bleomycin-induced mouse model of pulmonary fibrosis. Results A total of 123 active components and 1, 147 potential targets were identified, yielding 135 IPF-related differentially expressed targets. GO and KEGG analyses showed that these targets were mainly enriched in lipid metabolism, responses to hypoxia, and the PPAR and IL-17 signaling pathways. Machine learning identified four key targets—VCAM1, MMP2, SPP1, and LCN2—which demonstrated a certain degree of discriminatory ability in both the training and validation sets （AUC > 0.7）. （4） Immune infiltration analysis indicated that the key targets were significantly associated with multiple immune cell types （|r| > 0.4, FDR < 0.05）. Molecular docking results showed that the candidate compounds had good binding activity with the targets. Animal experiments confirmed that Tianlongjie significantly alleviated pulmonary fibrosis, reduced the levels of inflammatory factors and the expression of fibrosis markers （P < 0.05 or P < 0.01）. Conclusion Tianlongjie may exert anti-IPF effects through the coordinated action of multiple components, targets, and pathways by regulating inflammatory responses, the immune microenvironment, and fibrosis-related signaling pathways. Its key targets include VCAM1, MMP2, SPP1, and LCN2. This study provides a theoretical basis for investigating the mechanisms underlying the use of Tianlongjie in the treatment of IPF.
- Network pharmacology,
- Idiopathic pulmonary fibrosis,
- Machine learning,
- Tianlongjie

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References(74)

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