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Identi Cation of Hub Genes and Pathways in Psoriasis Through Identication of hub genes and pathways in psoriasis through bioinformatics and validation by RT-qPCR Ling Ai Zou Huangshi Central Hospital, Aliated Hospital of Hubei Polytechnic University, Edong Health Care Group https://orcid.org/0000-0001-5357-9747 Qichao Jian ( [email protected] ) Research Keywords: Psoriasis, Pathogenesis, Hub genes, Pathways, CXCL10 Posted Date: March 30th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-357690/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/23 Abstract Background Although several studies have attempted to investigate the aetiology and mechanism of psoriasis, the precise molecular mechanism remains unclear. Our study aimed to identify the hub genes and associated pathways that promote its pathogenesis in psoriasis, which would be helpful for the discovery of diagnostic and therapeutic markers. Methods GSE30999, GSE34248, GSE41662, and GSE50790 datasets were extracted from the Gene Expression Omnibus (GEO) database. The GEO proles were integrated to obtain differentially expressed genes (DEGs) using the affy package in R software, with |logFC|> 1.5 and adjusted P < 0.05. The DEGs were utilised for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analyses. Hub genes were identied using Cytoscape and enriched for analysis in www.bioinformatics.com.cn. These hub genes were validated in the four aforementioned datasets and M5-induced HaCaT cells using real-time quantitative polymerase chain reaction (RT-qPCR). Results A total of 359 DEGs were identied, which were mostly associated with responses to bacterium, defence responses to other organism, and antimicrobial humoral response. These DEGs were mostly enriched in the steroid hormone biosynthesis pathway, NOD-like receptor signaling pathway, and cytokine-cytokine receptor interaction. PPI network analysis indicated seven genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) as the probable hub genes of psoriasis; CXCL10 had a positive correlation with the other six hub genes. The chord plot results further supported the GO and KEGG analysis results of the 359 DEGs. Seven predicted hub genes were validated to be upregulated in four datasets and M5-induced HaCaT cells using RT-qPCR. Conclusions The pathogenesis of psoriasis may be associated with seven hub genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) and pathways, such as the NOD-like receptor signaling pathway and cytokine- cytokine receptor interaction. These hub genes, especially CXCL10, can be used as potential biomarkers in psoriasis. Background Page 2/23 Psoriasis is a chronic immune-mediated inammatory skin disease with global prevalence of 2- 3% [1,2]. The occurrence of psoriasis is a complex interaction among genetics, immunology, and environment [1,3]. Although there are plenty of studies to investigate the etiologies and mechanisms, the precise molecular mechanism of psoriasis remains unclear [3]. Therefore, a thorough exploration of psoriasis’ pathogenesis is helpful for discovery of potential biomarkers and provides novel clues for diagnosis and treatment. Up to now, microarray technology has been extensively utilized to analyze gene expression in many diseases, including psoriasis disease [4,5]. Gene Expression Omnibus (GEO) is a comprehensive public database, which contains various disease gene expression datasets [4]. Thus, bioinformatics analysis can mine the hub genes and associated pathways from GEO database, and provide a novel approach for exploring the molecular mechanism of psoriasis. In the current study, we merged four independent gene expression datasets (GSE30999 [6], GSE34248 [7], GSE41662 [7] and GSE50790 [8]) from the GEO database using the affy package in R software, and identied differentially expressed genes (DEGs) of the merged datasets. Then the DEGs were performed for a series of analyses, such as Gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network analysis, and hub genes prediction. In order to verify these predicted hub genes, we compared the expression levels of these genes between the lesional and non-lesional psoriatic skin in the four datasets, and performed RT-qPCR experiments in M5 induced HaCaT cells. Since some studies conrmed M5 (IL-1a, IL-17A, IL-22, TNF-a, and oncostatin M) can induce a better psoriasis cell model than other cytokines, we use M5 to mimic the hyperproliferative pathological state of psoriasis keratinocytes in our experiments [9-14] Methods Microarray data source Fig. 1 shows the overall workow of this study. Using the key words ‘psoriasis’, ‘lesion*’, ‘Tissue’, ‘Homo sapiens’ and ‘Expression proling by array’ were screened in the National Center of Biotechnology Information (NCBI) - GEO (https://www.ncbi.nlm.nih.gov/geo/). The raw proles of GSE30999 [6], GSE34248 [7], GSE41662 [7], and GSE50790 [8], were downloaded from the NCBI GEO database. The detailed information of four enrolled microarray datasets is shown in Table 1. In GSE30999, GSE34248, GSE41662, and GSE50790, only psoriatic lesional skin samples and their matched non-lesional skin samples were selected, which resulted in 127 pairs of skin samples from psoriasis patients for subsequent analysis. DEGs analysis and integration After the raw data of four datasets were collated, the analyses were performed in the R programming environment (version4.0.2.). The affy package was used to preprocess the collated data by conducting Page 3/23 background correction, normalization and expression calculation [15]. Then the ComBat function of sva package was applied for eliminating batch effect between datasets [16]. And the probes were annotated by matrix data combined with HG-U133_Plus_2-na36 annotation le. Lastly we used the limma package of R language to identify the differential expression genes(DEGs). The signicant differential expression cut-off was set as |logFC| >1.5 and adjusted P<0.05, which were demonstrated as Volcano plots. Furthermore, a heatmap was drawn to show the clustering of samples. GO and KEGG pathway analysis of DEGs Metascape (http://metascape.org) is a free, powerful and online analysis tool for gene annotation and analysis [17]. It is also an automated meta-analysis tool to annotate genes, conduct enrichment analysis and construct protein-protein interaction networks. In this study, Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were enriched based on Metascape. GO terms included biological process, cellular component, and molecular function. The cutoff criterions were P-value < 0.05, minimum overlap of 3, and minimum enrichment of 1.5. The top 20 enrichment Go terms and KEGG pathways in our study were listed as gures and tables respectively. Protein-protein interaction(PPI) network construction and hub genes identication The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, version 11.0, http://www.string- db.org/) was utilized to construct a PPI network for proteins encoded by DEGs, and the minimum required interaction score was set as 0.7 (referred as high condence) [18]. In order to further analyze the results from STRING database, Cytoscape 3.8.0 was used as a tool to visualize the PPI network [19]. The modules in the PPI network were extracted using the Cluster Finding algorithm in MCODE with a max depth of 100, a node score cutoff of 0.2, a degree cutoff of 2, and a k-core of 2. The hub genes were identied by CytoHubba, which was a useful plugin in Cytoscape. It can provide 12 topological analysis methods to explore top 10 genes. Specically, these methods are Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), Maximum Neighborhood Component (MNC), Degree, Edge Percolated Component (EPC), BottleNeck, EcCentricity, Closeness, Radiality, Betweenness, Stress, and ClusteringCoecient. A hub gene can be identied, if this gene was predicted as one of the top 10 genes in all the 12 methods. Additionally, interactions between hub genes were analyzed back in STRING database. Functional enrichment analysis of hub genes GO and KEGG analyses of 359 DEGs were performed by Metascape, including seven hub genes. Then the functional enrichment analysis of seven hub genes were explored by http: //www. bioinformatics. com.cn, an online platform for data analysis and visualization. Validation of hub genes in datasets Expression data of hub genes were extracted from GSE30999, GSE34248, GSE41662, and GSE50790. The correlations between CXCL10 and the other hub genes were conrmed in the four aforementioned Page 4/23 datasets. Then the differential expression of these hub genes was validated in these four datasets. Cell culture and treatment HaCaT cell line was purchased from Shanghai iCell Bioscience Inc. HaCaT cells were cultured in DMEM(Dulbecco's Modied Eagle's Medium) with 10% fetal bovine serum and 1% penicillin and streptomycin, in a humidied incubator at 37 °C and 5% CO2. Then the cells were treated with 10 ng/ml M5 (IL-1α [PeproTech, 200-01A], IL-17A [PeproTech, 200–17], IL-22 [PeproTech, 200–22], TNF-α [PeproTech, 300-01A], Oncostatin M [Peprotech, 300–10]) for 48 h, to establish the psoriasis cell model. Real-time quantitative PCR (RT-qPCR) Total RNA was extracted from M5 treated HaCaT cells by TRIpure Total RNA Extraction Reagen t(#EP013, ELK Biotechnology, China). Total RNA was reverse transcribed using EntiLink™
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