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Background <p>Diabetic nephropathy (DN) is a prevalent and serious complication of diabetes, characterized by high incidence and significant morbidity. Despite growing evidence that the tricarboxylic acid (TCA) cycle plays a crucial role in DN progression, the diagnostic potential of TCA-related genes has yet to be fully explored.</p> Methods <p>This study began by analyzing the GSE131882 dataset to reveal the expression patterns of TCA-related genes in various renal cell types and to identify genes that differ in expression between high and low subgroups. The GSE30122 dataset was then examined to identify genes with differential expression in DN. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were applied to pinpoint TCA-related gene modules. Following this, multiple machine learning techniques were employed to analyze the TCA gene set that showed differential expression at both cellular and sample levels, allowing us to identify the hub genes. A diagnostic model was constructed, with its effectiveness validated through ROC analysis. The immune landscape of DN was assessed using ssGSEA. GeneMANIA and NetworkAnalyst were also utilized to predict genes with similar functions, as well as miRNAs and transcription factors (TFs) that may regulate these diagnostic genes. Finally, single-cell RNA sequencing (scRNA-seq) data confirmed the expression patterns of these genes.</p> Results <p>Two TCA-related genes, HPGD and G6PC, were identified as potential diagnostic markers for DN. ROC analysis demonstrated that these genes and their predictive model exhibited strong diagnostic performance in both training and validation cohorts. Immune landscape analysis revealed a more active immune microenvironment in DN patients compared to controls. Additionally, 59 miRNAs and 15 TFs were predicted to regulate the expression of HPGD and G6PC, along with 20 functionally related genes. scRNA-seq data highlighted that HPGD and G6PC are predominantly expressed in glomerular and proximal tubular cells.</p> Conclusion <p>Two reliable TCA-related biomarkers were pinpointed, potentially advancing early diagnosis and management of DN.</p>

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Identification and validation of tricarboxylic acid cycle-related diagnostic biomarkers for diabetic nephropathy via weighted gene co-expression network analysis and single-cell transcriptome analysis

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Xuelin He,
  • Yichen Wu,
  • Guanghui Ying,
  • Min Xia,
  • Qien He,
  • Zhaogui Chen,
  • Qiao Zhang,
  • Li Liu,
  • Xia Liu,
  • Yongtao Li

摘要

Background

Diabetic nephropathy (DN) is a prevalent and serious complication of diabetes, characterized by high incidence and significant morbidity. Despite growing evidence that the tricarboxylic acid (TCA) cycle plays a crucial role in DN progression, the diagnostic potential of TCA-related genes has yet to be fully explored.

Methods

This study began by analyzing the GSE131882 dataset to reveal the expression patterns of TCA-related genes in various renal cell types and to identify genes that differ in expression between high and low subgroups. The GSE30122 dataset was then examined to identify genes with differential expression in DN. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were applied to pinpoint TCA-related gene modules. Following this, multiple machine learning techniques were employed to analyze the TCA gene set that showed differential expression at both cellular and sample levels, allowing us to identify the hub genes. A diagnostic model was constructed, with its effectiveness validated through ROC analysis. The immune landscape of DN was assessed using ssGSEA. GeneMANIA and NetworkAnalyst were also utilized to predict genes with similar functions, as well as miRNAs and transcription factors (TFs) that may regulate these diagnostic genes. Finally, single-cell RNA sequencing (scRNA-seq) data confirmed the expression patterns of these genes.

Results

Two TCA-related genes, HPGD and G6PC, were identified as potential diagnostic markers for DN. ROC analysis demonstrated that these genes and their predictive model exhibited strong diagnostic performance in both training and validation cohorts. Immune landscape analysis revealed a more active immune microenvironment in DN patients compared to controls. Additionally, 59 miRNAs and 15 TFs were predicted to regulate the expression of HPGD and G6PC, along with 20 functionally related genes. scRNA-seq data highlighted that HPGD and G6PC are predominantly expressed in glomerular and proximal tubular cells.

Conclusion

Two reliable TCA-related biomarkers were pinpointed, potentially advancing early diagnosis and management of DN.