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. 2023 Dec;17(6):316-326.
doi: 10.1049/syb2.12078. Epub 2023 Sep 30.

Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification

Rui Shi  1 , Wen-Man Zhao  1 , Li Zhu  1 , Rui-Feng Wang  1 , De-Guang Wang  1
Affiliations

Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification

Rui Shi et al. IET Syst Biol. 2023 Dec.

Abstract

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM-related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM-related DEGs were identified. The functional analysis showed that BM-related DEGs were notably associated with basement membrane alterations. Crucially, BM-related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols.

Keywords: bioinformatics; diseases; genetics; medical disorders.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this article.

Figures

FIGURE 1
FIGURE 1
Identification of BM‐related DEGs from the tubules of patients with DKD. (a) Volcano map of BM‐related DEGs, the horizontal axis is logFC and the vertical axis is −log10(adj. p), red plots represent upregulated genes, green plots represents downregulated genes. (b) Heat map of BM‐related DEGs, which showed the differential expression of some BM‐related genes between two groups. Type 1 represents healthy control group. Type 2 represents DKD group.
FIGURE 2
FIGURE 2
Enrichment analysis results of differentially expressed BM‐related genes between patients with DKD and healthy controls. (a) GO analysis in cell component, molecular function, and biological process; (b) The KEGG pathway enrichment analysis of DEGs was carried out, and the top 10 are visualised according to the enrichment score, DEGs were mostly enriched in “ECM −receptor interaction,” “Focal adhesion,” and “PI3K signalling pathway”.
FIGURE 3
FIGURE 3
PPI network of BMs related DEGs visualised by Cytoscape. (a) Identification of a significant module based on the degree of importance examined for DEGs. PPI network was composed of 28 nodes and 157 edges. Blues represent upregulated genes. Yellows represent downregulated genes. (b) The top 10 genes with the highest degree values were identified. Red represents higher degree value. Yellow represents lower degree value.
FIGURE 4
FIGURE 4
Visualisation and evaluation of immune cell infiltration. (a) Immune function correlation heat map. (b) Immune cell correlation heat map. (c) ssGSEA was used to calculate the differences in immune function between DKD and control group (d) Differences in immune cells between DKD and control group. (*p < 0.05; **p < 0.01; ***p < 0.001).
FIGURE 5
FIGURE 5
Correlations between hub genes and infiltrating immune cells and immune function. ITGAV, LAMA5, LAMC1, ITGB3 are closely correlated with infiltrating immune cells.
FIGURE 6
FIGURE 6
Nomogram prediction model on the basis of four hub genes. (a) Nomogram prediction model on the basis of four hub genes, each gene is assigned a point on the top axis by drawing a line upward. The sum of these numbers is located on the Total Points axis, and a line is drawn downwards to the Probability axis to identify the likelihood of advanced DKD. (b) Diagnostic value shown using ROC curve (AUC = 0.815).
FIGURE 7
FIGURE 7
Experimental verification of LAMC1 expression in HK2 using medium containing 40 mmol/L of glucose. (a) Western blot analysis of LAMC1 protein expression in HK2. α‐tubulin was a loading control. ****p < 0.0001 versus control. (b) Quantification of real‐time polymerase chain reaction (qRT‐PCR) of LAMC1 mRNA level in HK2. β‐actin was a loading control. ***p < 0.001 versus control. (c) LAMC1 in the kidney of mice were stained and their expression levels were analysed using immunohistochemistry. The scale bars represent 100 μm.
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