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Background <p>Nonalcoholic fatty liver disease (NAFLD), which affects approximately 25% of the adult population, represents a significant contributor to liver-related morbidity and mortality. However, the molecular mechanisms underlying NAFLD-related liver fibrosis remain incompletely understood. Moreover, there is a lack of research integrating transcriptomic data with MRI-derived parameters.</p> Objectives <p>This study aims to construct an animal model of NAFLD-related liver fibrosis, combine transcriptomics and MRI techniques, and explore the molecular mechanisms of liver fibrosis and their correlation with MRI-derived parameters.</p> Methods <p>In this study, a cohort of 24 Sprague–Dawley rats was utilized to model non-advanced and advanced NAFLD fibrosis. RNA sequencing was employed to identify fibrosis-associated genes, and weighted gene co-expression network analysis was conducted to construct a gene co-expression network. MRI scans were utilized to measure liver T2, T2*, R2* values, and proton density fat fraction, then the correlation with gene expression data was analyzed.</p> Results <p>The study identified 11 hub genes implicated in NAFLD-related liver fibrosis, including Loc100365810, Acin1, and Clk2, among others. Validation using the GSE193080 database and RT-qPCR demonstrated significant differences in KDM4A and DNASE1L3 expression between non-advanced and advanced fibrosis groups. RT-qPCR experiments also confirmed that the expression of Eif5a was significantly decreased in the advanced fibrosis group. Gene Ontology enrichment analysis underscored the critical roles of these genes in various signaling pathways, including apoptosis. Gene Set Variation Analysis revealed differential expression of metabolic pathways between non-advanced and advanced fibrosis cohorts. Furthermore, correlation analysis between hub genes and MRI-derived parameters revealed a significant association between liver fibrosis and some hub genes.</p> Conclusions <p>This study preliminarily identifies hub genes and metabolic pathways associated with fibrosis stages in NAFLD and suggests a potential correlation with MRI parameters, offering initial insights into molecular mechanisms and possible non-invasive diagnostic strategies; however, these findings require further validation in larger cohorts and human samples to confirm their clinical relevance.</p>

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Exploring Transcriptomic and MRI-Derived Parameters Correlations in NAFLD Fibrosis: Insights from a Rat Model

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Yao Liang,
  • Na Hu,
  • Xing Xia,
  • Fangyan Li,
  • Meihua Yang,
  • Pinggui Lei,
  • Peng Luo

摘要

Background

Nonalcoholic fatty liver disease (NAFLD), which affects approximately 25% of the adult population, represents a significant contributor to liver-related morbidity and mortality. However, the molecular mechanisms underlying NAFLD-related liver fibrosis remain incompletely understood. Moreover, there is a lack of research integrating transcriptomic data with MRI-derived parameters.

Objectives

This study aims to construct an animal model of NAFLD-related liver fibrosis, combine transcriptomics and MRI techniques, and explore the molecular mechanisms of liver fibrosis and their correlation with MRI-derived parameters.

Methods

In this study, a cohort of 24 Sprague–Dawley rats was utilized to model non-advanced and advanced NAFLD fibrosis. RNA sequencing was employed to identify fibrosis-associated genes, and weighted gene co-expression network analysis was conducted to construct a gene co-expression network. MRI scans were utilized to measure liver T2, T2*, R2* values, and proton density fat fraction, then the correlation with gene expression data was analyzed.

Results

The study identified 11 hub genes implicated in NAFLD-related liver fibrosis, including Loc100365810, Acin1, and Clk2, among others. Validation using the GSE193080 database and RT-qPCR demonstrated significant differences in KDM4A and DNASE1L3 expression between non-advanced and advanced fibrosis groups. RT-qPCR experiments also confirmed that the expression of Eif5a was significantly decreased in the advanced fibrosis group. Gene Ontology enrichment analysis underscored the critical roles of these genes in various signaling pathways, including apoptosis. Gene Set Variation Analysis revealed differential expression of metabolic pathways between non-advanced and advanced fibrosis cohorts. Furthermore, correlation analysis between hub genes and MRI-derived parameters revealed a significant association between liver fibrosis and some hub genes.

Conclusions

This study preliminarily identifies hub genes and metabolic pathways associated with fibrosis stages in NAFLD and suggests a potential correlation with MRI parameters, offering initial insights into molecular mechanisms and possible non-invasive diagnostic strategies; however, these findings require further validation in larger cohorts and human samples to confirm their clinical relevance.