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<p>Thyroid eye disease (TED) and diabetes mellitus (DM) cause vision loss, with DM aggravating TED. In this study, we aimed to explore the mechanisms of this interaction, and therefore, analyzed gene expression data from representative TED and DM datasets using bioinformatic tools. Following normalization and differential expression analysis, common differentially expressed genes (CDEGs) between the datasets were identified and subjected to Gene Set Enrichment Analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Interaction networks were constructed and the diagnostic potential of the CDEGs was assessed using receiver operating characteristic (ROC) curve analysis. The TED_Dataset and DM_Dataset contained 449 and 108 DEGs, respectively, with seven CDEGs. GO and KEGG analyses linked the CDEGs to biological processes including leukocyte adhesion and apoptosis. GSEA emphasized their roles in inflammation and fibrosis. Protein-protein interaction network analysis identified MFAP4 as a key hub gene. Constructed mRNA-transcription factor, mRNA-RNA binding protein, mRNA-microRNA, and mRNA-drug interaction networks revealed extensive regulatory relationships. ROC curve analysis demonstrated that <i>CXCL12</i> and <i>SFRP4</i> were potential diagnostic biomarkers for TED, and <i>SFRP4</i>, <i>IL6</i>, <i>MFAP4</i>, and <i>CRISPLD2</i> were potential diagnostic biomarkers for DM. Altogether, our findings provide comprehensive molecular insights into DM and TED, identifying novel targets for therapeutic intervention and diagnostic biomarkers.</p>

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Integrative analysis identifies shared therapeutic pathways in thyroid eye disease and diabetes mellitus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Yifei Wang,
  • Hui Li

摘要

Thyroid eye disease (TED) and diabetes mellitus (DM) cause vision loss, with DM aggravating TED. In this study, we aimed to explore the mechanisms of this interaction, and therefore, analyzed gene expression data from representative TED and DM datasets using bioinformatic tools. Following normalization and differential expression analysis, common differentially expressed genes (CDEGs) between the datasets were identified and subjected to Gene Set Enrichment Analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Interaction networks were constructed and the diagnostic potential of the CDEGs was assessed using receiver operating characteristic (ROC) curve analysis. The TED_Dataset and DM_Dataset contained 449 and 108 DEGs, respectively, with seven CDEGs. GO and KEGG analyses linked the CDEGs to biological processes including leukocyte adhesion and apoptosis. GSEA emphasized their roles in inflammation and fibrosis. Protein-protein interaction network analysis identified MFAP4 as a key hub gene. Constructed mRNA-transcription factor, mRNA-RNA binding protein, mRNA-microRNA, and mRNA-drug interaction networks revealed extensive regulatory relationships. ROC curve analysis demonstrated that CXCL12 and SFRP4 were potential diagnostic biomarkers for TED, and SFRP4, IL6, MFAP4, and CRISPLD2 were potential diagnostic biomarkers for DM. Altogether, our findings provide comprehensive molecular insights into DM and TED, identifying novel targets for therapeutic intervention and diagnostic biomarkers.