Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · 2025
Delineating unique MASH-endothelial cells in metabolic dysfunction-associated steatotic liver disease using single-cell lensing.
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Liu Lianyong, Du Hong, Wu Fengying, Li Chenxi, Chen Jiajun, Zhang Xiaoying, Zhu Qingyun, Li Xiangqi
Аннотация
BACKGROUND: The role of hepatocytes in metabolic dysfunction-associated steatotic liver disease (MASLD) is well-documented. However, the contribution of endothelial cells to MASLD pathology remains poorly understood.
METHODS: In this study, we employed a multifaceted approach that integrated high-dimensional weighted gene co-expression network analysis (hdWGCNA), interaction analysis, machine learning, immune cell infiltration assessment, and Mendelian randomization. We applied this approach to analyze single-cell sequencing data in conjunction with bulk-sequencing datasets.
RESULTS: Our analysis revealed significant alterations in endothelial cell subpopulations among patients with non-alcoholic steatohepatitis (MASH), particularly with five distinct clusters designated as MASH-endothelial cells (MASH-EC). hdWGCNA identified a key turquoise module within the MASH-EC, which plays a pivotal role in carbohydrate and lipid metabolism pathways. Using machine learning algorithms, we identified ten signature genes with the potential to improve the clinical evaluation of MASLD. Immune infiltration analysis further highlighted intricate interactions between MASH-EC and immune cells, particularly neutrophils and monocytes. Patient stratification revealed marked heterogeneity within MASLD. Mendelian randomization analysis identified two novel causal genes, ENTPD1 and IFITM3, both implicated in MASLD pathogenesis.
CONCLUSIONS: In summary, our study delineates the unique features of MASH-EC, enhancing our understanding of endothelial cell dysfunction in MASLD and laying the groundwork for future investigations into targeted therapeutic strategies.
METHODS: In this study, we employed a multifaceted approach that integrated high-dimensional weighted gene co-expression network analysis (hdWGCNA), interaction analysis, machine learning, immune cell infiltration assessment, and Mendelian randomization. We applied this approach to analyze single-cell sequencing data in conjunction with bulk-sequencing datasets.
RESULTS: Our analysis revealed significant alterations in endothelial cell subpopulations among patients with non-alcoholic steatohepatitis (MASH), particularly with five distinct clusters designated as MASH-endothelial cells (MASH-EC). hdWGCNA identified a key turquoise module within the MASH-EC, which plays a pivotal role in carbohydrate and lipid metabolism pathways. Using machine learning algorithms, we identified ten signature genes with the potential to improve the clinical evaluation of MASLD. Immune infiltration analysis further highlighted intricate interactions between MASH-EC and immune cells, particularly neutrophils and monocytes. Patient stratification revealed marked heterogeneity within MASLD. Mendelian randomization analysis identified two novel causal genes, ENTPD1 and IFITM3, both implicated in MASLD pathogenesis.
CONCLUSIONS: In summary, our study delineates the unique features of MASH-EC, enhancing our understanding of endothelial cell dysfunction in MASLD and laying the groundwork for future investigations into targeted therapeutic strategies.