Identification of Hub Genes and Immune-Related Pathways for Membranous Nephropathy by Bioinformatics Analysis

Abstract

OBJECTIVE: We aim to explore the detailed molecular mechanisms of membrane nephropathy (MN) related genes by bioinformatics analysis.METHODS: Two microarray datasets (GSE108109 and GSE104948) with glomerular gene expression data from 65 MN patients and 9 healthy donors were obtained from the Gene Expression Omnibus (GEO) database. After processing the raw data, DEGs screening was conducted using the LIMMA (linear model for microarray data) package and Gene set enrichment analysis (GSEA) was performed with GSEA software (v. 3.0), followed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network analysis was carried out to determine the hub genes, by applying the maximal clique centrality (MCC) method, which was visualized by Cytoscape. Finally, utilizing the Nephroseq v5 online platform, we analyzed subgroups associated with hub genes. The findings were further validated by immunohistochemistry (IHC) staining in renal tissues from MN or control patients.RESULTS: A sum of 370 DEGs (188 up-regulated genes, 182 down-regulated genes) and 20 hub genes were ascertained. GO and KEGG enrichment analysis demonstrated that DEGs of MN were preponderantly associated with cell damage and complement cascade-related immune responses. Combined with literature data and hub gene-related MN subset analysis, CTSS, ITGB2, and HCK may play important roles in the pathological process of MN.CONCLUSION: This study identified novel hub genes in MN using bioinformatics. We found that some hub genes such as CTSS, ITGB2, and HCK might contribute to MN immunopathological process, providing new insights for further study of the molecular mechanisms underlying glomerular injury of MN.

Analysis of complement system and its related factors in Alzheimer’s disease

“…Among the 23 genes previous studies have shown their correlation with AD, we have: GFAP [ 41 , 42 ], PDGFRβ [ 43 – 45 ], NFKBIA [ 46 ], TNFRSF1B [ 47 ], NOTCH1 [ 48 ], BCL6 [ 49 ], CSF1R [ 50 ], LEP [ 51 ], DCLRE1C [ 52 ], KCNJ10 [ 53 ], MAP2K1 [ 54 ], VIP [ 55 ], SNCA [ 56 ], ENO2 [ 57 ], SST [ 58 ], UCHL1 [ 59 ], HPRT1 [ 60 ], STAT4 [ 61 ], CD14 [ 62 ], ITGB2 [ 63 ], SPP1 [ 64 ], STX1A [ 65 ], and SYP [ 66 ]. And these genes are involved in the regulation of molecules associated with the complement system, for instance, GFAP [ 67 ], PDGFRβ [ 68 ], NFKBIA [ 69 ], TNFRSF1B [ 70 ], NOTCH1 [ 71 ], BCL6 [ 72 ], CSF1R [ 73 ], LEP [ 74 ], DCLRE1C [ 75 ], KCNJ10 [ 76 ], MAP2K1 [ 77 ], VIP [ 78 ], SNCA [ 79 ], ENO2 [ 80 ], SST [ 81 ], UCHL1 [ 82 ], HPRT1 [ 83 ], STAT4 [ 84 ], CD14 [ 85 ], ITGB2 [ 86 ], SPP1 [ 87 ], STX1A [ 88 ], and SYP [ 89 ]. However, few studies have discussed their role in AD pathology by modulating the complement system, our study may be a new perspective for our future exploration of the relationship between AD and the complement system.…”

Identification of GMFG as a novel biomarker in IgA nephropathy based on comprehensive bioinformatics analysis

“…The Nephroseq V5 database ( https://v5.nephroseq.org ) [ 21 ] contains clinical traits and gene expression data. To investigate the correlation among the important genes and clinical characteristics of IgAN, we integrated these genes into the database.…”

Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis

“…A PPI network of 65 up-regulated (Figure 4A) and 193 down-regulated (Figure 5A) DEGs were generated using STRING, and an interaction score >0.40 was considered a medium confidence interaction relationship. The nodes with the most interactions with neighboring nodes were considered as the key node [16]. The upregulated protein had 11 nodes and 11 edges (Figure 4B), while the downregulated protein had 31 nodes and 65 edges (Figure 5B).…”