Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

Abstract

Objective The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells. Methods The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed. Results A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways. Conclusion TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

An integrated bioinformatics approach to identify key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and construction of mRNA–miRNA-lncRNA/circRNA networks

“…Finally, we jointly analyzed the relevant potential biomarkers and ceRNA networks in the glomeruli of FSGS [ 19 , 64–66 ] according to the above key biomarkers of the FSGS tubulointerstitium and found that there was a relationship among the biomarkers. First, a total of 12 biomarkers identified by bioinformatics were summarized in the FSGS glomerular microarray dataset (FN1, EGF, TTR, BMP-2, COL4A1, C3AR1, CCR1, CX3CL1, MTNR1A, P2RY13, TGFB1, and NOTCH1).…”

“…To fully understand the regulatory mechanisms of the biomarkers, a detailed analysis of the transcription factors and ceRNAs of the key biomarker PPARGC1A in the FSGS tubulointerstitium was conducted, and the results of previous studies on FSGS glomeruli [ 19 , 64 ] were compared and analyzed. First, regarding the transcription factors, there was no intersecting gene among the three, and the intersecting gene between the glomeruli of the two was determined to be RUNX3.…”