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Background <p>Type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated fatty liver disease (MAFLD) are prevalent metabolic disorders with overlapping pathophysiological mechanisms. Screening and validating shared biomarkers of the two diseases from the brown adipose tissue perspective may provide candidate targets and clues for subsequent mechanistic research and therapeutic intervention development.</p> Methods <p>We utilized two datasets from the Gene Expression Omnibus (GEO) database to identify common differentially expressed genes (DEGs) between T2DM and MAFLD. Subsequently, we conducted Weighted Gene Co-expression Network analysis (WGCNA) to identify key genes associated with brown adipose-related genes (BARGs). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were conducted to determine enriched biological processes and signaling pathways. Machine learning approaches and expression validation were applied to identify overlapping genes. Finally, we established a T2DM with MAFLD comorbidity mouse model using C57BL/6 mice to validate the expression patterns of the identified genes via quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot (WB).</p> Results <p>Our analysis identified 78 DEGs shared between T2DM and MAFLD. Through WGCNA, we discovered 2,057 key module genes associated with BARGs, and their intersection yielded 24 candidate genes. Enrichment analysis revealed their involvement in several pathways, including Cushing syndrome, signal transduction, and hormone synthesis. Machine learning combined with expression validation identified two overlapping genes, <i>IGSF3</i> and <i>TREH</i>. In the mouse model, qRT-PCR confirmed significant upregulation of TREH mRNA in liver tissues of T2DM + MAFLD mice, while <i>IGSF3</i> mRNA showed no significant difference between groups. WB further validated that <i>TREH</i> protein expression was significantly elevated in the T2DM + MAFLD group, but <i>IGSF3</i> failed to be verified as a consistent biomarker due to inconsistent expression patterns between mRNA and protein levels.</p> Conclusion <p>This study screens and validates shared BAT-related biomarkers for T2DM and MAFLD, offering candidate targets and research clues for further mechanistic investigation. The identified gene <i>TREH</i>, associated with critical metabolic pathways, shows promise as a candidate biomarker and potential research target for the comorbidity. Brown adipose tissue may serve as a potential target for novel therapeutic interventions against T2DM with MAFLD comorbidity. <i>IGSF3</i>, despite being identified via bioinformatics and human dataset validation, could not be consistently verified in the mouse model, requiring further investigation.</p>

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Common biomarkers associated with brown adipose tissue in type 2 diabetes and metabolic dysfunction-associated fatty liver disease: a bioinformatics analysis and mouse model investigations experiments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Jun Liu,
  • Xinyi Yang,
  • Jingjing Liang,
  • Bo Yu,
  • Qi Wang,
  • Xiaoyong Bo,
  • Yixin Xu,
  • Wei Wang,
  • Min Chen,
  • Hanlu Meng,
  • Qiuyue Shen,
  • Yong Zhong,
  • Jiaqing Shao

摘要

Background

Type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated fatty liver disease (MAFLD) are prevalent metabolic disorders with overlapping pathophysiological mechanisms. Screening and validating shared biomarkers of the two diseases from the brown adipose tissue perspective may provide candidate targets and clues for subsequent mechanistic research and therapeutic intervention development.

Methods

We utilized two datasets from the Gene Expression Omnibus (GEO) database to identify common differentially expressed genes (DEGs) between T2DM and MAFLD. Subsequently, we conducted Weighted Gene Co-expression Network analysis (WGCNA) to identify key genes associated with brown adipose-related genes (BARGs). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were conducted to determine enriched biological processes and signaling pathways. Machine learning approaches and expression validation were applied to identify overlapping genes. Finally, we established a T2DM with MAFLD comorbidity mouse model using C57BL/6 mice to validate the expression patterns of the identified genes via quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot (WB).

Results

Our analysis identified 78 DEGs shared between T2DM and MAFLD. Through WGCNA, we discovered 2,057 key module genes associated with BARGs, and their intersection yielded 24 candidate genes. Enrichment analysis revealed their involvement in several pathways, including Cushing syndrome, signal transduction, and hormone synthesis. Machine learning combined with expression validation identified two overlapping genes, IGSF3 and TREH. In the mouse model, qRT-PCR confirmed significant upregulation of TREH mRNA in liver tissues of T2DM + MAFLD mice, while IGSF3 mRNA showed no significant difference between groups. WB further validated that TREH protein expression was significantly elevated in the T2DM + MAFLD group, but IGSF3 failed to be verified as a consistent biomarker due to inconsistent expression patterns between mRNA and protein levels.

Conclusion

This study screens and validates shared BAT-related biomarkers for T2DM and MAFLD, offering candidate targets and research clues for further mechanistic investigation. The identified gene TREH, associated with critical metabolic pathways, shows promise as a candidate biomarker and potential research target for the comorbidity. Brown adipose tissue may serve as a potential target for novel therapeutic interventions against T2DM with MAFLD comorbidity. IGSF3, despite being identified via bioinformatics and human dataset validation, could not be consistently verified in the mouse model, requiring further investigation.