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Updated: Jan 12, 2026
Author Spotlight: Establishing MASLD Cell Models for Investigating Disease Mechanisms and the Lipid-Lowering Effects of Koumiss
Published on: July 19, 2024
Development of a diagnostic model for MASLD and identification of daidzein as the potential drug using bioinformatics
Tao Wang1, Hao Zhang2, Kaixia Wang1
1Department of Infectious Diseases, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Background:
Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the predominant chronic liver disease globally, yet effective therapeutic strategies remain elusive.
Methods:
MASLD-related datasets were download from GEO. Subsequently, genes associated with MASLD were found through the intersection of differentially expressed genes and WGCNA. Then, key candidate genes were further screened using 113 machine learning algorithms and their diagnostic value was evaluated using ROC curve analysis across multiple datasets. Genes are then screened by Shapley Additive exPlanations (SHAP) analysis. Molecular docking (MD) and molecular dynamics simulations (MDS) were employed to validate the interaction between Daidzein and Enolase 3 (ENO3). Finally, an in vitro fatty liver cell model was constructed to validate the "Enrichr" platform to identify poteitial drugs for MASLD.
Results:
62 MASLD-DEGs were finally identified. The optimal predictive model for MASLD was the 17-gene signature (IGFBP1, ENO3, SOCS2, GADD45G, NR4A2, RTP4, RAB26, CRYAA, PPP1R3C,MCAM, IL6, IER3, RTP3, NR4A1, CCL5, FOS, JUNB) selected through combined glmBoost+GBM algorithms, which was demonstrated robust predictive performance. SHAP analysis suggested that ENO3 may be the most prominent genes associated with MASLD severity. More importantly, we measured the effect of daidzein on improving lipid accumulation in vitro model.
Conclusion:
We developed a predictive model for MASLD and identified ENO3 as a key predictive gene. Furthermore, we discovered that daidzein may serve as a potential therapeutic agent for MASLD. Through in vitro studies, we further confirmed that daidzein alleviates lipid deposition and improves MASLD by modulating the ENO3/PPAR signaling pathway.
Insights
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global concern. This study identifies a 17-gene signature for MASLD prediction and suggests daidzein as a potential therapeutic agent by targeting the ENO3/PPAR pathway.
Area of Science:
- Hepatology
- Genomics
- Pharmacology
Background:
- Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease worldwide.
- Current therapeutic strategies for MASLD are limited, necessitating novel approaches.
Purpose of the Study:
- To develop a predictive model for MASLD using gene expression data.
- To identify potential therapeutic agents for MASLD.
Main Methods:
- Differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify MASLD-associated genes.
- Machine learning algorithms, including glmBoost and GBM, were employed to build a predictive model.
- Shapley Additive exPlanations (SHAP) analysis and molecular dynamics simulations were used to evaluate gene significance and drug interactions.
- In vitro fatty liver models were utilized to assess the therapeutic potential of daidzein.
Main Results:
- A 17-gene signature was identified as an optimal predictive model for MASLD, demonstrating robust performance.
- Enolase 3 (ENO3) was highlighted as a key gene associated with MASLD severity by SHAP analysis.
- Daidzein demonstrated efficacy in improving lipid accumulation in an in vitro MASLD model.
Conclusions:
- A novel 17-gene predictive model for MASLD was successfully developed.
- ENO3 is identified as a critical gene in MASLD pathogenesis.
- Daidzein shows promise as a potential therapeutic agent for MASLD by modulating the ENO3/PPAR signaling pathway.
