Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization

Abstract

Backgruound: Diabetic kidney disease (DKD) is recognized as a significant complication of diabetes mellitus and categorized into glomerular DKDs and tubular DKDs, each governed by distinct pathological mechanisms and biomarkers.

Methods: Through the identification of common features observed in glomerular and tubular lesions in DKD, numerous differentially expressed gene were identified by the machine learning, single-cell transcriptome and mendelian randomization.

Results: The diagnostic markers versican (VCAN) was identified, offering supplementary options for clinical diagnosis. VCAN significantly highly expressed in glomerular parietal epithelial cell and proximal convoluted tubular cell. It was mainly involved in the up-regulation of immune genes and infiltration of immune cells like mast cell. Mendelian randomization analysis confirmed that serum VCAN protein levels were a risky factor for DKD, while there was no reverse association. It exhibited the good diagnostic potential for estimated glomerular filtration rate and proteinuria in DKD.

Conclusion: VCAN showed the prospects into DKD pathology and clinical indicator.

Keywords: Diabetic nephropathies; Kidney glomerulus; Kidney tubules; Machine learning; Mendelian randomization analysis; Single-cell gene expression analysis; VCAN protein, human.

Fig. 1.

Identification of differentially expressed genes in the two merge datasets. (A) Glomerulus datasets. (B) Tubule datasets. (C) The Venn plot of intersected gene between glomerulus and tubule. FC, fold change.

Fig. 2.

Identification of key expressed genes by weighted correlation network analysis. (A, B, C, D) The glomerulus merge data. (E, F, G, H) The tubule merge data. ME, module.

Fig. 3.

The immune infiltration pattern of glomerulus and tubules. (A, B) The immune infiltration of glomerular tissue. (C, D) The immune infiltration of tubular tissue. ssGSEA, single-sample gene set enrichment analysis.

Fig. 4.

Expression level of versican (VCAN) in single cell transcriptome data. (A) The elbow plot before annotation. (B) The primary cluster plot. (C) The cluster plot with manual annotation: mesenchymal cell (MES), glomerular parietal epithelial cell (PEC), proximal convoluted tubular cell (PCT), loop of Henle cell (LOH), distal convoluted tubular cell (DCT), convoluted tubular cell (CT), collecting duct-principal cell (CD-PC), collecting duct-intercalated cell type A (CD-ICA), collecting duct-intercalated cell type B (CD-ICB), endothelia cell (ENDO), leukocyte (LEUK). (D) The correlation between cell markers and clusters. (E) The distribution of VCAN in kidney cells of diabetic nephropathy (DN) group. (F) The expression of VCAN in DN group. (G) The distribution of VCAN in kidney cells of control group. (H) The expression of VCAN in control group. (I) The cluster plot with default annotation in Kidney Integrative Transcriptomics (K.I.T.) database. (J) The distribution of VCAN in the control group. (K) The distribution of VCAN in DN group. (L) The violin plot of VCAN expression. (M) The dot plot of VCAN expression. PODO, podocyte.

Fig. 5.

The scatter plot, funnel plot and leave-one-out plot of versican (VCAN). (A, B, C) The causal association from VCAN to diabetic kidney disease (DKD). (D, E, F) The causal association from DKD to VCAN. MR, Mendelian randomization; SNP, single nucleotide polymorphism; SEIV, standard error of the instrumental variable.

Fig. 6.

Clinical correlation of hub-gene. (A) Serum versican (VCAN) mRNA levels in diabetic kidney disease (DKD) patients. (B) Serum fibronectin 1 (FN1) mRNA level in DKD patients. DN, diabetic nephropathy; GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease. ^aP<0.0001 (unpaired t-test).