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. 2024 May 14:2024:5550812.
doi: 10.1155/2024/5550812. eCollection 2024.

Identification of Lipotoxicity-Related Biomarkers in Diabetic Nephropathy Based on Bioinformatic Analysis

Han Nie  1 , Huan Yang  1 , Lidan Cheng  1 , Jianxin Yu  1
Affiliations

Identification of Lipotoxicity-Related Biomarkers in Diabetic Nephropathy Based on Bioinformatic Analysis

Han Nie et al. J Diabetes Res. .

Abstract

Objective: This study is aimed at investigating diagnostic biomarkers associated with lipotoxicity and the molecular mechanisms underlying diabetic nephropathy (DN). Methods: The GSE96804 dataset from the Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs) in DN patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the DEGs. A protein-protein interaction (PPI) network was established to identify key genes linked to lipotoxicity in DN. Immune infiltration analysis was employed to identify immune cells with differential expression in DN and to assess the correlation between these immune cells and lipotoxicity-related hub genes. The findings were validated using the external dataset GSE104954. ROC analysis was performed to assess the diagnostic performance of the hub genes. The Gene set enrichment analysis (GSEA) enrichment method was utilized to analyze the key genes associated with lipotoxicity as mentioned above. Result: In this study, a total of 544 DEGs were identified. Among them, extracellular matrix (ECM), fatty acid metabolism, AGE-RAGE, and PI3K-Akt signaling pathways were significantly enriched. Combining the PPI network and lipotoxicity-related genes (LRGS), LUM and ALB were identified as lipotoxicity-related diagnostic biomarkers for DN. ROC analysis showed that the AUC values for LUM and ALB were 0.882 and 0.885, respectively. The AUC values for LUM and ALB validated in external datasets were 0.98 and 0.82, respectively. Immune infiltration analysis revealed significant changes in various immune cells during disease progression. Macrophages M2, mast cells activated, and neutrophils were significantly associated with all lipotoxicity-related hub genes. These key genes were enriched in fatty acid metabolism and extracellular matrix-related pathways. Conclusion: The identified lipotoxicity-related hub genes provide a deeper understanding of the development mechanisms of DN, potentially offering new theoretical foundations for the development of diagnostic biomarkers and therapeutic targets related to lipotoxicity in DN.

Keywords: bioinformatic analysis; biomarker; diabetic nephropathy; immune cell infiltration; lipotoxicity-related genes.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of the study. DEGs: differentially expressed genes; LRGS: lipotoxicity-related genes.
Figure 2
Figure 2
Identification of DEGs in GSE96804 (a) volcano plot of DEGs and (b) heat map of DEGs.
Figure 3
Figure 3
Functional enrichment analysis of DEGs. (a) Results of GO and KEGG in upregulated DEGs are depicted on bar charts. (b) Results of GO and KEGG in downregulated DEGs are depicted on bar charts. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cellular component; MF: molecular function.
Figure 4
Figure 4
Construction of PPI networks and identification of hub genes and hub LRGS. (a) PPI network constructed using DEGs. Each diamond represents a DEG. (b) The hub genes were screened by four algorithms (MCC, MNC, degree, and closeness). (c) Intersecting hub genes with LRGS to identify hub LRGS. PPI: protein–protein interaction.
Figure 5
Figure 5
Immune cell infiltration analysis. (a) Distribution of 22 kinds of immune cells in tissues of the DN and control groups. (b) Correlation diagram between immune cells. (c) Expression of immune cells in the DN and control groups.
Figure 6
Figure 6
Correlation of immune cells with hub LRGS.
Figure 7
Figure 7
Split violin plot of the expression of hub LRGS in different datasets. The expression of hub LRGS in the GSE96804 dataset is shown in Figures 6(a)–6(c)). The expression of hub LRGS in the GSE104954 dataset is shown in Figures 6(d) and 6(e)).
Figure 8
Figure 8
ROC curve analysis. (a) Hub LRGS in the GSE96804 datasets were analyzed using ROC curves. (b) Hub LRGS in the GSE10495 datasets were analyzed using ROC curves.
Figure 9
Figure 9
(a, b) GSEA of hub LRGS.
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