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<p>Diabetes remains a significant global health challenge, marked by increasing incidence and a complex pathophysiological mechanism involving dysregulated lipid metabolism, impaired autophagy, and chronic inflammatory responses. Lipophagy, an autophagic process that involves the targeting of lipid droplets, is crucial for metabolic homeostasis. Therefore, investigating lipophagy-associated molecules may facilitate the discovery of novel biomarkers and potential therapeutic targets for diabetes. In this study, two GEO datasets, GSE33440 and GSE9006, were combined to identify differentially expressed genes (DEGs) linked to diabetes. By integrating weighted gene coexpression network analysis (WGCNA) with machine learning algorithms, this study identified <i>EGR2</i> and <i>CCR1</i> as key hub genes related to lipophagy. The results of the rank sum test revealed a strong positive correlation between these two genes, both of which were significantly upregulated in diabetic samples. Functional analyses, such as gene set enrichment analysis (GSEA), gene ontology (GO) enrichment, and protein‒protein interaction (PPI) network analysis, were used to validate their coherence. Diagnostic models and receiver operating characteristic (ROC) curve analysis further underscore the potential of <i>CCR1</i> and <i>EGR2</i> as biomarkers. Importantly, experimental validation demonstrated that the expressions of both genes were significantly elevated in the serum of diabetic patients and in the liver tissues of BKS-db diabetic mice. Notably, compared with control mice, <i>CCR1</i> knockout mice (<i>CCR1</i>-/-) exhibited improved glucose homeostasis under high-fat diet conditions. Collectively, these findings suggest that <i>EGR2</i> and <i>CCR1</i> may be potential biomarkers associated with lipophagy in diabetes.</p>

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Identification and verification of the key genes, CCR1 and EGR2, in diabetes-associated lipophagy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Jiongjiong Liu,
  • Xiao Zhang,
  • Yanlei Wang,
  • Wenjun Pei,
  • Qiu Zhang

摘要

Diabetes remains a significant global health challenge, marked by increasing incidence and a complex pathophysiological mechanism involving dysregulated lipid metabolism, impaired autophagy, and chronic inflammatory responses. Lipophagy, an autophagic process that involves the targeting of lipid droplets, is crucial for metabolic homeostasis. Therefore, investigating lipophagy-associated molecules may facilitate the discovery of novel biomarkers and potential therapeutic targets for diabetes. In this study, two GEO datasets, GSE33440 and GSE9006, were combined to identify differentially expressed genes (DEGs) linked to diabetes. By integrating weighted gene coexpression network analysis (WGCNA) with machine learning algorithms, this study identified EGR2 and CCR1 as key hub genes related to lipophagy. The results of the rank sum test revealed a strong positive correlation between these two genes, both of which were significantly upregulated in diabetic samples. Functional analyses, such as gene set enrichment analysis (GSEA), gene ontology (GO) enrichment, and protein‒protein interaction (PPI) network analysis, were used to validate their coherence. Diagnostic models and receiver operating characteristic (ROC) curve analysis further underscore the potential of CCR1 and EGR2 as biomarkers. Importantly, experimental validation demonstrated that the expressions of both genes were significantly elevated in the serum of diabetic patients and in the liver tissues of BKS-db diabetic mice. Notably, compared with control mice, CCR1 knockout mice (CCR1-/-) exhibited improved glucose homeostasis under high-fat diet conditions. Collectively, these findings suggest that EGR2 and CCR1 may be potential biomarkers associated with lipophagy in diabetes.