Renal tubular gene biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis

Abstract

Objective The present study identified novel renal tubular biomarkers that may influence the diagnosis and treatment of focal segmental glomerulosclerosis (FSGS) based on immune infiltration. Methods Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211, were downloaded from the Gene Expression Omnibus (GEO) database. The R statistical software limma package and the combat function of the sva package were applied for preprocessing and to remove the batch effects. Differentially expressed genes (DEGs) between 120 FSGS and 15 control samples were identified with the limma package. Disease Ontology (DO) pathway enrichment analysis was conducted with statistical R software to search for related diseases. Gene set enrichment analysis (GSEA) was used to interpret the gene expression data and it revealed many common biological pathways. A protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes were identified by the Cytoscape (version 3.7.2) plug-in CytoHubba. The plug-in Molecular Complex Detection (MCODE) was used to screen hub modules of the PPI network in Cytoscape, while functional analysis of the hub genes and hub nodes involved in the submodule was performed by ClusterProfiler. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model. Receiver operating characteristic (ROC) curve analyses were used to investigate the logistic regression model and it was then validated by an external dataset GSE125779, which contained 8 FSGS samples and 8 healthy subjects. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the immune infiltration of FSGS samples. Results We acquired 179 DEGs, 79 genes with downregulated expression (44.1%) and 100 genes with upregulated expression (55.9%), in the FSGS samples. The DEGs were significantly associated with arteriosclerosis, kidney disease and arteriosclerotic cardiovascular disease. GSEA revealed that these gene sets were significantly enriched in allograft rejection signaling pathways and activation of immune response in biological processes. Fifteen genes were demonstrated to be hub genes by PPI, and three submodules were screened by MCODE linked with FSGS. Analysis by machine learning methodologies identified nuclear receptor subfamily 4 group A member 1 (NR4A1) and dual specificity phosphatase 1 (DUSP1) as sensitive tubular renal biomarkers in the diagnosis of FSGS, and they were selected as hub genes, as well as hub nodes which were enriched in the MAPK signaling pathway. Immune cell infiltration analysis revealed that the genetic biomarkers were both correlated with activated mast cells, which may amplify FSGS biological processes. Conclusion ...

Identification of key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and their relationship with immune cell infiltration using weighted gene co-expression network analysis and least absolute shrinkage and selection operator

“…Dual-speci city protein phosphatase1 (DUSP1) and nuclear receptor 4A1 (NR4A1) have been identi ed as potential sensitive biomarkers for the diagnosis of FSGS. Activated mast cells have a decisive effect on the occurrence and development of FSGS through tubular lesions and tubulointerstitial in ammation, and they are expected to become therapeutic targets for FSGS (Bai et al 2022). While we used machine learning to analyze the differences between normal and renal tubular FSGS samples of GSE108112, GSE133288 and GSE121211 in our previous study, this study used WGCNA combined with LASSO to screen normal and renal tubular interstitial FSGS samples of GSE108112, GSE200818 samples.…”

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

“…External validation data were obtained from the GSE26193 dataset, which consisted of 107 patients with gene expression and clinical information. To preprocess the gene expression data and remove batch effects, we applied a combination of normalization and ComBat [ 10 ]. We retrieved 338 ARGs from the GeneCards website ( Supplementary Material 1 , www.wjon.org ).…”

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

“…Currently, lncRNA-related ceRNA networks of FSGS glomeruli have been reported [ 19 ]. We have previously identified recombinant dual specificity phosphatase 1 (DUSP1) and nuclear receptor 4A1 (NR4A1) as potential biomarkers in the tubules of FSGS using machine learning methods [ 20 ]. However, no further analysis was performed on the ceRNA network of potential markers.…”