Jiang et al. Eur J Med Res (2020) 25:40 https://doi.org/10.1186/s40001-020-00441-2 European Journal of Medical Research RESEARCH Open Access Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy Xue Jiang1, Zhijie Xu2, Yuanyuan Du1 and Hongyu Chen1* Abstract Background: Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel bio- markers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods: Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omni- bus database, the diferentially expressed genes (DEGs) between IgAN samples and normal controls were identifed. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identifed and the most important module among the DEGs, Biological Networks Gene Ontol- ogy tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results: In total, 148 DEGs were identifed, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fbroblast growth factor stimulus, infammatory response, and innate immunity. Module analysis showed that genes in the top 1 signifcant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and infammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We fnally identi- fed the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions: We identifed key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN. Keywords: IgA nephropathy, Gene expression profling, Bioinformatics analysis Background glomerular mesangial matrix. Nearly 25–30% of IgA nephropathy (IgAN), the most prevalent type afected patients develop end-stage renal disease. Pres- of glomerulonephritis in humans, is characterized ently, several clinical biomarkers have been identifed by mesangial cell proliferation, the expansion of the to be associated with IgAN progression, such as pro- teinuria, serum creatinine, hypertension and advanced *Correspondence: [email protected] histological involvement [1]. In 2011 [2], Suzuki et al. 1 Department of Nephropathy, Hangzhou Hospital of Traditional Chinese hypothesized that the pathogenesis of IgAN is based Medicine, Hangzhou 310012, Zhejiang, China on four hits: frst, the occurrence of an abnormal IgA1 Full list of author information is available at the end of the article glycosylation process leading to galactose-defcient © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Jiang et al. Eur J Med Res (2020) 25:40 Page 2 of 11 IgA1 (Gd-IgA1); second, the formation of antiglycan Te present study aimed to identify potential novel antibodies against Gd-IgA1; third, the formation of candidate hub genes to diagnose and treat IgAN. nephrogenic circulating immune complexes; fourth, the deposition of these complexes in the mesangium of Methods glomeruli, leading to renal injury with variable clinical Microarray data expression. However, the exact pathogenesis is not very Te microarray data were downloaded from the GEO clear. database (http://www.ncbi.nlm.nih.gov/geo) using IgAN Many studies have also shown a genetic predisposition as the search term. GSE93798 is based on the Afym- to IgAN [3]. Serino et al. found six signifcantly upregu- etrix Human GeneChip U133 2.0 platform (includes lated miRNAs, two of which modulate the O-glycosyla- 42 samples, 20 IgAN patients and 22 healthy controls). tion process of IgA1. Specifcally, let-7b regulates the GSE37460 is based on the Human Genome U133A Afy- gene GALNT2 and miR-148 modulates the gene tar- metrix platform (includes 54 samples, 27 IgAN patients get C1GALT1, which has been considered an underly- and 27 healthy controls). ing biomarker to predict the probability of IgAN [4, 5]. Wang et al. found that low urinary levels of miR-29b and Identifcation of DEGs miR-29c are correlated with proteinuria and renal func- Te DEGs were identifed based on the series matrix fle tion. High levels of miR-93 were correlated with glomer- using the Limma package in R software (version 3.5.0). ular scarring. miR-200a, miR-200b, and miR-429 have An adj P value < 0.01 and a |log FC (fold change) | ≥ 1 also been suggested as potential biomarkers to monitor were defned as the thresholds for DEG screening. Te the progression of the disease at the renal level in IgAN DEGs overlapped between the two datasets were iden- patients [6]. However, due to the lack of large-scale stud- tifed and then used for further functional enrichment ies, the limitation of animal models and current low- analysis. Te overlapped DEGs were subjected to bidirec- throughput genetic studies, the crucial genes involved in tional hierarchical clustering analysis using the Pheatmap the development and efective treatment of IgAN have package in R to recognize and visualize the diferences in remained elusive. DEGs between IgAN and the control. Bioinformatics studies have been widely performed in various felds to extract potential information and reveal Enrichment analysis of the DEGs the underlying mechanics of various diseases. Recently, Te DAVID (http://david .abcc.Ncifc rf.gov/) [10] tool bioinformatics analysis has gradually provided insight was used to conduct GO/KEGG (http://www.genom e.jp/ into the molecular mechanisms of kidney disease. For kegg/pathw ay.html) [11] pathway enrichment analyses example, PSMB8, as a novel hub gene, plays a signifcant for DEGs. Te number of enrichment genes (count num- role in the occurrence of membrane nephropathy [7]. In ber) ≥ 2 and P value < 0.05 were chosen as cut-of criteria. lupus, bioinformatics analysis revealed that CD38 and CCL2 are hub macrophage-related genes [8]. Addition- PPI network construction and module analysis hub gene ally, EST1 may be a drug target for diabetic nephropa- identifcation thy treatment [9]. Currently, only a few bioinformatics Te DEGs identifed were subjected to PPI analysis using analyses have been performed on IgAN; its critical asso- the search functionality of STRING (http://strin g.embl. ciated genes and interactions have not been thoroughly de/) [12] to explore the association between the DEGs, investigated. and a network interaction matrix was built. An interac- In the present study, two original microarray datasets tion with a combined score > 0.4 was set as the cut-of were selected from the Gene Expression Omnibus (GEO) value. Next, the network was visualized using Cytoscape database. After identifying the diferentially expressed software [13], which is a broadly used tool to visualize genes (DEGs) in IgAN patients and control group, we the interaction networks among numerous biomolecules, employed the Database for Annotation, Visualization and including proteins and genes. Te MCODE plug-in was Integration Discovery (DAVID) to identify the functions used to identify the most signifcant module in the PPI of the identifed DEGs and performed Gene Ontology networks with MCODE scores > 5, degree cut-of = 2, (GO) and Kyoto Encyclopedia of Genes and Genomes node score cut-of = 0.2, max depth = 100 and k score = 2. (KEGG) pathway analyses. Te protein–protein inter- CytoHubba [14] is a tool used to identify hub objects and action (PPI) network was generated using the STRING subnetworks from a complex interactome. ‘MCC’ is a database, and hub genes and the most signifcant mod- topological analysis method in CytoHubba that was used ule among the PPI networks were identifed using cyto- to identify featured nodes and the hub genes from all the Hubba and the Molecular Complex Detection (MCODE) DEGs. Te biological processes of the hub genes were plug-in. visualized using the Biological Networks Gene Ontology Jiang et al. Eur J Med Res (2020) 25:40 Page 3 of 11 tool (BiNGO) (version 3.0.3) plug-in of Cytoscape [15], obtained from the IgAN group vs. control group, com- with a signifcance threshold of 0.01 and Homo sapiens as prising 53 upregulated and 95 downregulated genes. Te the selected organism. results of the expression level analysis are presented in a volcano plot in Fig. 1a. As indicated in the clustering External microarray dataset validation heat map (Fig. 1b), these DEGs could well distinguish the To validate the expression of FCER1G and ITGB2 in IgAN and control group completely.