CHINESE JOURNAL OF MEDICINAL GUIDE >
Integrating Bioinformatics and Machine Learning Algorithms to Elucidate Target Characteristics and Molecular Mechanisms of CangZhu in Non-Alcoholic Fatty Liver Disease Intervention
Received date: 2025-03-18
Revised date: 2025-09-02
Accepted date: 2025-11-12
Online published: 2025-11-18
Objective To explore the potential molecular mechanism of CangZhu in the treatment of non-alcoholic fatty liver disease （NAFLD） by network pharmacology combined with bioinformatics methods. Methods Network pharmacology method was used to obtain the potential targets of CangZhu. The targets of NAFLD were obtained by searching the Gene Expression Omnibus （GEO） database， and the core targets were screened by constructing the target PPI network. Then R4.2.2 software was used for differential analysis and correlation analysis of the target genes to obtain the core genes with significant differential expression （SDECGs）. Next， enrichment analysis and immune infiltration analysis of SDECGs were performed. SDECGs was used to construct a machine learning model to screen feature genes and construct a Nomo map. Finally， the molecular docking and external validation methods of GSE63067 dataset were used to validate the above results. Results Fourteen SDECGs were identified between NAFLD and normal subjects. These SDECGs were mainly enriched in IL-17， AGE-RAGE and other signaling pathways， and had a variety of mutual regulatory relationships with immune cells. XGBoost model （XGB） is the best model in a variety of machine learning. CTNNB1， IL10， PTGS2， IL6 and JUN were identified as the top five feature genes in the XGB model and used to construct the nomo map. External data sets demonstrated the reliability of the model （AUC=0.73）. Finally， molecular docking confirmed that the core active components of CangZhu （MOL000179， MOL000186， MOL000449 and MOL000188） could form a stable structure with NAFLD characteristic genes. Conclusion CangZhu and its active components may alleviate NAFLD by regulating CTNNB1， IL10， PTGS2， IL6， JUN， and immune-and inflammation-related pathways. These findings have potential implications for clinical practice and future research.
HUANG Xinni, LU Aihua, ZHANG Shu, FANG Ruxve, LIU Xuling, LI Junxiong
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Integrating Bioinformatics and Machine Learning
Algorithms to Elucidate Target Characteristics and Molecular Mechanisms of
CangZhu in Non-Alcoholic Fatty Liver Disease Intervention
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