Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis

Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis Yan Sun, Chen-chen Wang, Fu-quan Wang, Rui Chen, Chun-lin Yao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-576901/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The discovery of biomarkers has become an attractive field in studying autoimmune diseases. For example, in the study of systemic lupus erythematosus (SLE), various biomarkers such as genes and miRNAs have been identified for the diagnosis of SLE and its organ involvement. Results The expression data of gene microarray GSE50772 was downloaded from the GEO, and 257 differentially expressed genes (DEGs) were obtained by using limma plug-in for R software. The tissue-specific gene expression analyses were performed in BioGPS database. Then, a protein-protein interaction (PPI) network was constructed with STRING and visualized in Cytoscape. Whereafter, top twenty hub genes derived from the PPI network, could basically differentiate the SLE samples from the non-SLE samples, were ascertained through CytoHubba. What is noticeable is that the five novel hub genes ( ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a related miRNA (hsa-let-7e-5p) may be considered as candidate biomarkers of SLE. Conclusions Five genes (ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a miRNA(hsa-let-7e-5p) in this discovery-driven study may become potential biomarkers for diagnosing SLE and assessing its organ damage, and they also will provide valuable information on the pathogenesis of SLE. Bioinformatics Bioinformatic analysis Systemic lupus erythematosus Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background SLE is one of the most prevalent autoimmune diseases and often causes tremendous sufferings to patients. The risk of morbidity and mortality of SLE patients is still significantly high[ 1 , 2 ]. Thus, making a confirmed diagnosis early of SLE has always been essential for initiating the appropriate therapy, but there is no single clinical symptoms or lab abnormality for diagnosing lupus definitely[ 3 ], even if plenty of biomarkers for diagnostic use have been found, such as antinuclear antibodies (ANAs), in particular anti-dsDNA antibodies, anti-Sm antibodies, as well as SLE-associated loci and genes, miRNAs, and other molecules[ 4 – 6 ]. As we all know, SLE is characterized by the protean clinical course and a broad spectrum of organ or system manifestations[ 7 , 8 ], which has an important impact on prognosis of patients[ 9 ]. But the manifestations are usually non-specific at onset, making it easy to confuse lupus with a variety of other diseases [ 10 ]. Therefore, discovering more biomarkers with relatively high specificity and sensitivity is one of the most crucial and urgent problems for auxiliary diagnosis of SLE. Over the years, with the wide application of DNA microarray technology and bioinformatics analysis, genetics and epigenetics have attracted extensive attention in SLE researches, especially the expression levels of genes and miRNAs acting as biomarkers [ 11 , 12 ]. Previous studies have declared that some genes are susceptible to SLE, such as IRF7, STAT4, BLK, etc [ 13 , 14 ], and many genes even have been regarded as good biomarkers for diagnosing SLE, like OASL, ISG15 MX1, etc [ 15 ]. Most expression products of SLE-associated gene participate in immune response [ 16 ], and many genes are also related with damaged target organs. In addition, as critical regulators in regulating post-transcriptional target gene expression, miRNAs can interrupt intercellular signal pathways, perturb immune homeostasis and produce autoantibodies, and eventually trigger the occurrence of autoimmune responses [ 17 – 20 ]. Strong evidences for the correlation between dysregulated miRNAs and the pathogenesis and adverse complications of SLE have been provided by published literatures [ 21 – 25 ]. Bioinformatics faces huge-volume heterogeneous biological data [ 26 ], including fundamental biology and the biology that underlies disease [ 27 ]. During identifying biomarkers for SLE diagnosis, large datasets, through bioinformatic analysis, can be obtained to screen out virtual genetic or epigenetic alternations. In this paper, data quality analysis was performed on the GSE50772, which was downloaded from the GEO public database. In order to identify DEGs, gene expression data of SLE patients and normal controls were extracted by the limma package of R software. Furthermore, DEGs were analyzed for tissue-specific gene expression and the identification of hub genes by respectively using BioGPS and CytoHubba. Subsequently, the functional enrichment of DEGs was analyzed by DAVID, the PPI network of DEGs was constructed through STRING. Moreover, by means of starBase v2.0, the genes, which were involved in the top 10 biological processes with statistical significance, were selected to make miRNAs prediction and gene-miRNA interaction network analysis. Our results will provide new biological information to improve the understanding of the pathogenesis of SLE, and novel biomarkers may be helpful for diagnosiof the disease in early time. Materials And Methods Microarray data The Gene Expression Omnibus Database (GEO, https://www.ncbi.nlm.nih.gov/geo/ ) is an international public repository for the collection and distribution of high-throughput microarrays and next-generation sequenced functional genomic data sets [ 28 ]. The microarray expression dataset GSE50772, uploaded by Kennedy and Maciuca et al., was retrieved and downloaded from the GEO. The selected species was Homo sapiens, the type of data was microarray expression profiles, and the dataset was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array platform. This research contains 81 samples consisting of 61 subjects with SLE and 20 healthy controls. In addition, the annotation file for GPL570 was also obtained from the GEO. Estimation of the RNA quality of samples and preprocessing RNA degradation, proceeding from the 5′ end to the 3′ end, plays an crucial role in modulating gene expression and correcting systematic biases [ 29 , 30 ]. RNA degradation measurement presents the best correlation of the RNA integrity number (RIN) and an independent RNA integrity measurement, and therefore is able to be a valuable tool for quality control [ 31 ]. We used Affy package and affyPLM package of R software (R Foundation for Statistical Computing, Vienna, Austria) to estimate the quality of GSE50772 dataset, and the RNA degradation plot was to display the results of the analysis. RMA and KNN methods were used to preprocess the data of each sample in the dataset. Differential expression analysis R software was used to normalize and process the original expression matrix, and DEGs were screened via the limma package. The P- values were calculated by adopting the T- test methods, and the adjusted P‐ values were computed by applying the Benjamini and Hochberg's method. The DEGs were screened out by the following selection criteria: 1) | log2 (fold-change) | >1, and 2) the adjusted P-values < 0.05. The heatmap and volcano map for the DEGs were created by SangerBox software ( http://sangerbox.com/ ). Tissue-specific gene expression analysis We analyzed the tissue specific expression of the DEGs by the online resource BioGPS ( http://biogps.org ). If two following criteria were satisified, transcripts mapped to the single tissue would be identified as highly tissue specific: 1) The tissue-specific expression of the transcripts was 10 times higher than its median level, 2) The second highest expression level was lower than 1/3 of the highest expression level [ 32 ]. Functional enrichment analysis of DEGs We used Database for annotation, visualization and integrated discovery (DAVID) v6.8 ( https://david.ncifcrf.gov/tools.jsp ) to conduct the functional enrichment analyses of DEGs, including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO, which are used to predict protein functions, includes cell composition (CC), molecular function (MF) and biological process (BP) [ 33 ]. The KEGG pathway analysis, which is used to allot a series of DEGs on specific pathways, constructs the molecular reaction, interaction and relationship [ 34 ]. Thus, we conducted pathway analysis to identify which key pathways might be associated with DEGs. In addition, P- values 5 were chosen as the criteria for significance. Protein-protein interaction (PPI) network analysis and hub genes identification. The DEGs were uploaded to STRING( https://string-db.org/ ) to produce the PPI network diagram. These protein-protein interactions involve both functional and physical connections, with data derived primarily from high-throughput experiments, computational predictions, co-expression networks and automated text mining. In addition, the PPI network constructed from STRING analysis was imported into Cytoscape v.3.8.0 software, thereby to make a visual design of PPI network. And CytoHubba was used to process the network data to identify the top 20 hub genes. Subsequently, immune-related hub genes were identified by intersection between top 20 hub genes and immune genes, which were downloaded from the Immport immune database ( https://www.immport.org/ ). The GO analyses for these hub genes were performed on Metascape ( https://metascape.org/ ). Prediction of pivotal miRNAs and identification of hub genes Based on the results of functional enrichment analysis of DEGs, genes enriched in top 10 statistically significant biological processes were selected and then performed with miRWalk 2.0 software ( http://mirwalk.umm.uni-heidelberg.de/ ) , then their targeted miRNAs were further predicted [ 35 ]. Parallelly, in order to verify the accuracy of the results, we used miRWalk, miRDB and TargetScan for the intersection. Therefore, miRNAs targeting at more than two genes were screened out [ 36 ]. Furthermore, the GeneCards database ( https://www.genecards.org/ ) was used to identify hub genes which were found to serve as candidate biomarkers in our study. Results Study resign The workflow chart of our study design is shown in Fig. 1 . Our original goal is to identify more useful biomarkers involved in SLE pathogenesis. Above all, RNA quality analysis was performed on the GSE50772 dataset, which was downloaded from the GEO public database. Extracted from the limma package of R software, gene expression datas of SLE patients and normal controls were used to filter DEGs. Then, the selected DEGs were analyzed for tissue-specific gene expression and the functional enrichment. The PPI network of DEGs was constructed followed, and through which, the top 20 hub genes were identified. In addition, miRNAs prediction and gene-miRNA interaction network analysis were performed on the genes which involved in the top 10 biological processes with statistical significance. Finally, the GeneCards database was used to verify SLE - related hub genes. Assessment of the RNA quality of samples To assess the quality of the available data of GSE50772 in SLE, We applied Affy package and affyPLM package of R software to draw the RNA degradation plot for all sample arrays. In Fig. 2 , the analysis result demonstrates that 81 nearly parallel curves representing 81 samples have appropriate slopes, which doesn’t display the bias of probe-positional intensity associated with RNA integrity, indicating that the RNA quality of samples is relatively ideal. Differentially expressed genes |Log 2 FC| greater than 1 and adjusted P-values less than 0.05 were considered as criteria to screen the DEGs out. A total of 257 DEGs are obtained, among which 227 are down-regulated and 30 up-regulated. As shown in Table 1 , the RPS4Y1, EIF1AY and KDM5D are the most up-regulated. Similarly, the three most down-regulated genes are CXCL8, ANXA3 and IFI27, whose log₂FC values are − 3.675, -3.186 and − 3.173 respectively (Additional file 1). The volcano plot and heatmap of the DEGs are seen in Fig. 3 . Table 1 Top 10 up-regulated and down-regulated genes. Gene log₂FC † Adjusted P-value Up-regulated RPS4Y1 2.539 2.09E-07 EIF1AY 2.179 5.96E-07 KDM5D 1.800 3.07E-09 USP9Y 1.711 6.81E-08 KLRC4 1.543 6.22E-14 GCSAML 1.542 5.68E-16 ZNF850 1.476 7.35E-31 PDCD4 1.365 3.27E-19 ZNF566 1.325 7.51E-17 FAM169A 1.315 1.31E-19 Down-regulated CXCL8 -3.675 3.99E-13 ANXA3 -3.186 1.85E-12 IFI27 -3.173 5.71E-08 OLFM4 -2.922 8.10E-06 IL1R2 -2.886 6.19E-09 CXCL1 -2.841 1.32E-19 MMP9 -2.789 2.70E-12 EGR1 -2.689 1.30E-13 G0S2 -2.635 3.06E-10 S100P -2.628 1.62E-12 † log₂FC: log₂-transformed fold change of gene expression. Tissue-specific expression of genes We used the BioGPS database, an online tool, to identify 57 genes expressed in specific tissues or organ systems. As shown in Table 2 , the system with the most highly tissue-specific expression is the hematologic/immune system (59.6%, 34/57), followed by the digestive system (15.8%, 9/57). The respiratory and skin/skeletal muscle systems have similar levels of enrichment (about 8.8%, 5/57), while the urinary and reproductive systems have the lowest enrichment levels (about 3.5%, 2/57). Table 2 Tissue-specific expressed genes identified by BioGPS. System Genes Hematologic/immune CAMP,ELANE,MMP8,DEFA4,CEACAM8,RSAD2,IFIT1, FFAR2,CXCR2, RETN,SLC25A37,CXCR1, SELENBP1, MZB1,FPR2,SUCNR1,SLC22A4, CMPK2,IFIT2,FOSB, HBM,AQP9,FPR1,PPP1R15A,MXD1, CCL4,NFE2, AZU1, KCNJ2,MGAM,RNASE2,RNASE3,SH2D1B,EIF1AY Skin/skeletal muscle CXCL1,IL1B,CCL2,TNNT1,CXCL3 Respiratory TCN1,ANXA3,C1QC,AREG,COL17A1 Digestive OLFM4,HP,ORM1,LRG1,ARG1,CYP4F3,ANG,C15orf48, KRT23 Urinary TCN2,ALOX15B Reproductive ADM,S100P Functional and pathway enrichment of DEGs To investigate the biological function of the DEGs, we performed functional enrichment analyses of 257 DEGs, GO annotation and KEGG pathway analyses were conducted by using the DAVID online tool. The top 10 GO items, including BPs, CCs and MFs, and the top 10 KEGG pathways, are listed in Fig. 4 A- 4 D. Moreover, the top 10 biological processes of DEGs and their related details are shown in Table 3 , among which biological pathways with P-value < 0.05 are statistically significant. The results suggest that the biological pathways with DEGs significantly enriching are immune system–related pathways. Furthermore, the extracellular exosome, cytosol and extracellular space, and integral component of plasma membrane account for the majority of CC. And the most abundant MFs are protein binding. KEGG pathway analysis reveals that the DEGs mainly enrich in Cytokine-cytokine receptor interaction, TNF signaling pathway and Chemokine signaling pathway (Fig. 4 D). Table 3 The top10 biological process (BP) of DEGs. Term Count P-Value Genes inflammatory response 32 1.38E-16 OLR1,C3AR1,PROK2,CXCL8,TNFAIP6,CXCL1,CXCL3,PTGS2,HCK,IL1B,CHI3L1,PTX3,SIGLEC1,TLR2,TNF,CXCL2,CXCR1,GPER1,CCL4,CXCR2,FPR1,ORM1,S100A12,CCL2,CCR1,CCL20,NMI,FOS,AZU1,FPR2 immune response 25 9.87E-10 IFITM3,CXCL8,AQP9,CXCL1,CXCL3,CXCL2,FCGR3B,CXCL10,NFIL3,CCL4,CCL2,PGLYRP1,FCGR1B,CCR1,CCL20,IL1R2,IFI6,OAS1,SLPI,OAS3,IL1B,CEACAM8,C1QC,TLR2,TNF innate immune response 22 1.64E-07 C1QB,DEFA4,CRISP3,MX2,MX1,SH2D1B,BMX,HERC5,TLR2,IGLL5,CLEC4D,SLPI,GPER1,LCN2,S100A12,PTX3,CLEC4E,ANG,PGLYRP1,CAMP,C1QC, MSRB1 G-protein coupled receptor signaling pathway 18 0.070008171 CXCL8,CCL20,FPR1,FPR2,CXCL3,CXCL2,AREG,CCL4,CCL2,CXCL10,HCAR3,CXCR1,GPER1,C3AR1,PROK2,FFAR2,CXCL1, SUCNR1 Type I interferon signaling pathway 15 5.67E-14 IFITM3,EGR1,RSAD2,MX2,MX1,IFI6,ISG15,IFI35,IFIT1,IFIT3,IFIT2,OASL,IFI27,OAS1, OAS3 chemotaxis 15 5.27E-10 CCR1,CXCL8,CCL20,FPR1,CXCL1,FPR2,RNASE2,CXCL2,CXCL10,CXCR1,CXCR2,C3AR1,PROK2,CCL2,CMTM2 defense response to virus 15 2.75E-08 IFITM3,RSAD2,MX2,MX1,ISG15,AZU1,IFIT1,IFIT3,OASL,IFIT2,HERC5,CXCL10,OAS1,OAS3,IFI44L positive regulation of cell proliferation 15 0.002819349 IRS2,ADM,AREG,CDC20,CXCL10,HCK,HLX,CEACAM6,NAMPT,CXCR2,MZB1,PROK2,PRTN3,CAMP,GPER1 response to virus 14 1.54E-09 IFITM3,RSAD2,MX2,MX1,IFI44,IFIT1,TNF,IFIT3,IFIT2,OASL,OAS1,OAS3,CCL4,LCN2 response to lipopolysaccharide 13 1.36E-06 JUN,CXCL1,FOS,CXCL3,PTGS2,SOD2,MPO,CXCL2,CXCL10,SLPI,ELANE,TLR2,ADM PPI network construction, hub genes selection and analysis The 257 DEGs were inputted to the STRING tool for further analysis, and a PPI network with 224 nodes and 1485 edges were visualized with Cytoscape. The interaction score of PPI network is greater than 0.4. The nodes correspond to genes, and the edges represent the links between genes. Green nodes represent down-regulated genes, red nodes represent up-regulated genes. The local clustering coefficient is 0.532 and PPI enrichment P-value is less than 1.0e-16. Then, the data file was processed with Cytoscape ( Fig. 5 A). CytoHubba was used to process the network data, and then to identify hub genes, the top 20 hub genes (CXCL1, CAMP, HP, PTX3, ARG1, ELANE, LCN2, RETN, MMP8, SLPI, PGLYRP1, LTF, OLFM4, ORM1, TCN1, LRG1, CRISP3, CHI3L1, MMP9 and DEFA4 ) were identified ( Fig. 5 B). The color of a node in the network reflects the rank of hub genes. Clustering shows that the hub genes could basically differentiate the SLE samples from the non-SLE samples. Most hub genes are highly expressed in SLE samples, while relatively low in non-SLE samples (Fig. 5 C). In addition, functional enrichment analysis indicates that these hub genes mainly enrich in immune system process as shown in Fig. 5 D. In order to further explore and confirm the nature of hub genes, as shown in the supplementary materials for this study, 2482 immune genes downloaded from the Immport immune database were used to intersect with the top 20 hub genes. As expected, we found that 13 hub genes, including CXCL1, CAMP, PTX3, ARG1, ELANE, LCN2, RETN, SLPI, PGLYRP1, LTF, ORM1, MMP9 and DEFA4, were immune-related genes (Additional file 2). Further miRNA mining and identification of key genes Among the top 10 biological processes, eighty-six genes, associated with statistically significant biological processes, were selected, and the gene-miRNA analysis was conducted with miRWalk 2.0 software. The intersection of miRNA results predicted by miRWalk, TargetScan and miRDB databases was considered as the result. The selection condition was set as P-value < 0.05, the target gene binding region was 3′UTR. Therefore, hsa-let-7e-5p with high number of gene cross‐links (≥ 2) is identified, it targets at OLR1 and IRS2 as shown in Fig. 6 . The score of it is 1, which means that it has high reliability. In addition, using the GeneCards database, SLE - related genes were manually identified (Additional file 3), and three of our novel hub genes (ORM1, SLPI and TCN1) were verified to be potentially involved in the pathogenesis of SLE (Fig. 7 ). Table 4 Fifteen hub genes that have been reported in previous SLE studies. Gene symbol Full name Role in SLE References HP haptoglobin display immunosuppressive abilities to consist in the host defence responses to inflammation and infection [ 73 – 75 ] RETN, MMP8 Resistin, Matrix metalloprotein-ase-8 associate with the presence of coronary artery calcium and increase vulnerability to atherosclerotic plaque [ 76 , 77 ] ARG1, CXCL1 arginase 1, chemokine with C-X-C motif ligand 1 manipulate type 17 T helper cells (Th17) pathway [ 78 , 79 ] CAMP, LTF Cathelicidin antimicrobial peptide, Lactotransferr-in estrogen exerts powerful effects on the immune response by affecting the expression of CAMP and LTF in B cells [ 80 ] DEFA4 Defensin alpha 4 strongly link to immune function and numerous autoimmune diseases [ 80 ] PTX3 Pentraxin 3 a biomarker or therapeutic target of SLE [ 81 ] ELANE Elastase participate into end-organ damage [ 82 ] LCN2 Lipocalin 2 a nephritis-associated inflammatory mediator [ 83 ] LRG1 Leucine-rich alpha-2-glycoprotein 1 reflect specific pathologic lesions in kidney and activity of lupus nephritis [ 84 ] PGLYRP1 Peptidoglycan recognition protein 1 might perturb the cytotoxic effect of autoantibodies, reduce tissue injury by competitively binding with autoantibodies [ 85 ] CHI3L1 Chitinase 3 like 1 evaluate the activity of lupus [ 86 ] MMP9 Matrix metallopeptid-ase 9 participate in pathways and immune system responses associated with SLE [ 87 , 88 ] Discussion SLE is one of the most common systemic autoimmune diseases that seriously endangers human health. There are many factors causing the pathogenesis of SLE, among which the abnormal expression of important genes may contribute to lupus pathogenesis by participating in critical pathways, including immune complex processing, type I interferon producing, toll-like receptor signaling, and so on [37]. However, the accurate mechanism of SLE caused these microenvironmental factors has not been completely elucidated, so more attention should be paid to the detection and evaluation of the expression level of lupus - related genes in lupus researches. In present study, we are committed to discover possible SLE - causing molecules, and 257 DEGs (30 up-regulated genes and 227 down-regulated genes) are screened out from GSE50772 expression dataset. They certainly has laid a foundation for our subsequent analyses, and that may be able to illuminate the initiation and progression of SLE. Considering that multiple organs or systems involvement caused by autoantibodies is the feature of SLE, we performed tissue - specific expression analysis on DEGs. As revealed by the result, the most highly enriched system is the hematologic/immune system, which is in line with the pathogenesis and clinical manifestations of SLE, and this seems to explain the underlying molecular mechanisms of a self-aimed immune response in SLE patients. Besides, skin/skeletal muscle system, respiratory system, digestive system, urinary and reproductive systems are also enriched by DEGs. Some studies have also suggested that ANXA3 [38, 39], TCN1 [40], C1QC [41], AREG [42] and COL17A1 [43] are associated with respiratory injury. ALOX15B [44] may be related to kidney diseases as reported in the literature. ADM [45] and S100P [46] possibly play important roles in lesions of the reproductive system. And changes in expression of OLFM4 [47], CYP4F3 [48], HP [49], ORM1 [50], ANG [51], LRG1 [52], C15orf48 [53], KRT23 [54] and ARG1 [55] are involved with digestive system diseases. Even some of them have been thought to be classic markers of a particular tissue injury. While whether those tissue - specific DEGs above-mentioned are essential for the development of complications of SLE remains inconclusive, and we postulate that the abnormal expression of those genes probably can indicate organ involvement in SLE patients. However, in our results, there are other relatively common organs and tissues that are not significantly enriched by DEGs, such as the central nervous system, cardiovascular and circulatory systems. Limited gene expression microarray data with insufficient samples may be a by-no-means negligible cause. In order to understand disease machinery more deeply and to visualize the overview of the functional connections between all DEGs, we constructed PPI networks, the vital tools for analysis by identifying subnetworks or modules that display specific topology and/or functional characteristics [56]. Afterwards, on the basis of DEGs’ PPI networks, the top 20 hub genes (CXCL1, CAMP, HP, PTX3, ARG1, ELANE, LCN2, RETN, MMP8, SLPI, PGLYRP1, LTF, OLFM4, ORM1, TCN1, LRG1, CRISP3, CHI3L1, MMP9 and DEFA4) were selected. According to cluster analysis results on them, it is obvious that most hub genes are up-expressed in SLE patients, while relatively low-expressed in normal subjects, which highlights the importance and representativeness of these hub genes in SLE disease. And further functional enrichment analysis on them manifests that immune system processes are dominant. Furthermore, 13 hub genes verified by Immport database are thought to be immune-related genes as expected, namely CXCL1, CAMP, PTX3, ARG1, ELANE, LCN2, RETN, SLPI, PGLYRP1, LTF, ORM1, MMP9 and DEFA4. The result exactly supports the idea that these 20 hub genes probably play essential roles in immune-related pathways which can trigger autoimmune dysfunction in patients, and resulting in the pathogenesis and development of SLE. In prior studies on 20 hub genes mentioned above, the aberrant expression levels of 15 genes have been investigated that they may have various and crucial influences for different processes of SLE development. Given the roles of five novel genes in SLE as shown in Table 4, several novel genes, including ORM1, SLPI, OLFM4, TCN1 and CRISP3 may also have diagnostic value in the condition. ORM1 (orosomucoid 1) is an acute phase plasma protein known to activate NFκB, p38 and JNK pathways in macrophages, and it has been reported in rheumatoid arthritis (RA) [57], sarcoidosis and other immune diseases [58]. In experimental autoimmune encephalomyelitis, SLPI (secretory leukocyte peptidase inhibitor) exerted potent pro-inflammatory actions by regulating T cell activity, a process that might benefit the patient [59]. OLFM4(olfactomedin 4) could mediate the autoimmune inflammatory responses of generalized pustular psoriasis, a severe inflammatory skin disease [60]. Low expression of TCNI (transcobalamin I) involved in innate immunity might be partly responsible for the pathogenesis of IgG4-related disease, due to impairments in the innate immune system [61]. Since CRISP3 (cysteine-rich secretory protein 3) was detected to be significantly elevated in RA, the researchers hypothesized that it was implicated in the development of RA [62]. In addition, using the GeneCards database, three novel hub genes (ORM1, SLPI, and TCN1) were confirmed to be potentially involved in the pathogenesis of SLE. Almost all of these genes, either high or low expression, are associated in the development of immune diseases. Consequently, chances are that the five hub genes play pivotal roles in the molecular mechanism of SLE pathogenesis, and we reasonably confer that these novel hub genes may be used as biomarkers to help improve the diagnostic rate of SLE and to provide valuable information for the evaluation of organ or system involvement.It is well known that miRNAs can interfere with the transcription and regulate gene expression [63]. Altered miRNA expression has been regarded as another important factor to the pathogenesis of immune-related diseases, such as SLE. And because of the nature of stability of miRNA, measuring effective miRNA levels may be conducive to disease detectionin [64]. In immune cells, aberrant miRNAs can disturb immune homeostasis, produce massive autoantibodies and induce autoimmunity [65]. Following GO terms, we performed miRNA mining and interaction network analysis. MiRNA hsa-let-7e-5p targeting at OLR1(oxidized low- density lipoprotein receptor 1) and IRS2 ( insulin receptor substrate 2) was identified. The miRNA let-7e is a member of the let-7 family, and it plays a key role in inhibiting or promoting inflammatory response by regulating cytokine expression in various inflammatory and autoimmune diseases [66]. In an animal experiment, down-regulating the expression of hsa-let-7e-5p and other two miRNAs, 17β-estradiol could amplify the activation of IFN-α signaling in B cells to contribute to the sex bias in SLE [67–69]. Moreover, as target genes of has-let-7e-5p in this study, OLR1 and IRS2 are down-regulated in SLE patients compared with non-SLE subjects. When it comes to biological processes, OLR1 is associated with inflammatory response, while ISR2 is related to positive regulation of cell proliferation. Regarding molecular function, OLR1 and IRS2 both participate in exerting protein binding. Recent studies show that OLR1 is an inflammation-induced receptor. Together with a host of other reactions, an increase in OLR1 can trigger the formation of neutrophil extracellular traps, which can promote systemic inflammation, vascular damage and lung injury. Elevated expression level of OLR1 has been recognized as a possible indicator of high risk of SLE - related cardiovascular disease [70, 71]. Targeted by MiR-203a, IRS2 regulates the proliferation and apoptosis of pancreatic β cell [72], which implies the expression of IRS2 is closely related to type 1 diabetes mellitus (T1DM), an autoimmune disease. These results rend us to speculate that has-let-7e-5p may be a potential molecule to induce and deteriorate the SLE even though there have been rare relevant studies are published on this subject. Thus, we propose that hsa-let-7e-5p probably acts as another novel latent biomarker of SLE, and we hope it could provide new insights into molecular mechanism underlying the development and progression SLE. Additionally, since the GSE50772 is a public dataset, patient consent or ethics committee approval is not required, but the information on individuals’ age, gender and health status, as well as medication use, is absent, which appears to be an underlying limitation. Conclusions In conclusion, some DEGs specifically expressed in a tissue or system might be a signal to estimate organ involvement in SLE, and novel candidate biomarkers including hub genes ( ORM1, SLPI, OLFM4, TCN1 and CRISP3) and has-let-7e-5p were identified to assist in diagnosing SLE through comprehensive bioinformatic analyses. Our point will provide new and meaningful reference for later SLE studies. Since the current finding is limited by the lack of experimental validation in vivo and in vitro, it is necessary to conduct multiple in-depth studies to detect and verify those potential biomarkers. Declarations Acknowledgements Thanks for all the authors who provided the help for the analysis. Funding This study was supported by the grants from the 2019 College-level Teaching Reform Research Project [grant numbers 02.03.2019.15-15] Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Authors’ contributions YS, CW and FW conceived the study and participated in the study design, performance, coordination and manuscript writing. YS, CW, FW, RC, and CY carried out the analysis. YL and YW revised the manuscript. All authors reviewed and approved the final manuscript. Ethics approval and consent to participate Not applicable. Consent for publication All the authors have consented for the publication. Competing interests The authors declare that they have no competing interest. Contributor Information Yan Sun, Email: [email protected] . Chen-chen Wang, Email: [email protected] . Fu-quan Wang, Email: [email protected] . Rui Chen, Email: [email protected] . 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Supplementary Files Supplementarymaterial.docx Additional file 1: 30 up-regulated genes and 227 down-regulated genes. Additional file 2: The 13 genes obtained from the intersection are shown in bold red. Additional file 3: SLE-related genes obtained from the GeneCards database. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-576901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":31684222,"identity":"6049c630-9e94-46f1-b935-8a6e792b08d0","order_by":0,"name":"Yan Sun","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Sun","suffix":""},{"id":31684223,"identity":"ba9c8c6d-6438-47aa-bd98-02144cae056e","order_by":1,"name":"Chen-chen Wang","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chen-chen","middleName":"","lastName":"Wang","suffix":""},{"id":31684224,"identity":"ee3d3788-0d78-4237-8d01-7327f44dc612","order_by":2,"name":"Fu-quan Wang","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Fu-quan","middleName":"","lastName":"Wang","suffix":""},{"id":31684225,"identity":"baaa9f2e-3e78-4aff-926e-6885c8149a9e","order_by":3,"name":"Rui Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Chen","suffix":""},{"id":31684226,"identity":"a186e6d6-366d-4e67-b95a-dd9ceb8e6b36","order_by":4,"name":"Chun-lin Yao","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chun-lin","middleName":"","lastName":"Yao","suffix":""},{"id":31684227,"identity":"9960fd5a-06b8-4544-bc49-eb5e4f2f6eca","order_by":5,"name":"Yan Wu","email":"","orcid":"","institution":"Wuhan Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":31684228,"identity":"0c1fd51f-09c1-4520-9ad9-42456a601119","order_by":6,"name":"Yun Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBAC9gYGBsaGAgkQm/ExVNAArxaeAyAtBmAtzMZAQoJYLWA2mzRxWqQPP3s4w8Aiz+BG7rHqwra6Ogb25m0SDDV3cGvhSzM33GAgUWxwIy/t9sy2wxIMPMfKJBiOPcOpxZ6HwUzygYFE4oYbOWa3edsOSDBI5JhJMDYcxm0LD/s3uJZi3rY6CQb5N4S08JhJboBqYeZtYwbawkNQS5nkDKCWmWfeGEvznDss2caTVmyRcAyvw7ZJ9lTUJfYdzzH8zFNWx8/PfnjjjQ81uLXAgcIBKIMNRCQQ1sDAIN9AjKpRMApGwSgYkQAAvHdLVsKm8LwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9705-6972","institution":"Wuhan Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2021-05-31 12:55:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-576901/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-576901/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":10159180,"identity":"db1655cb-09b8-4fed-8371-d720511ad27a","added_by":"auto","created_at":"2021-06-09 14:32:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42227,"visible":true,"origin":"","legend":"Flow chart of data preparation, processing, analysis, and validation. The gene expression profiles of GSE50772 were downloaded from the GEO database. Tissue-specific expression of genes, the functional enrichment and PPI networks were used to investigate potential biomarkers associated with the pathogenesis and clinical manifestation of SLE. In addition, key miRNA and hub genes were were further identified, and some SLE-related hub genes were validated based on data from the GeneCards database.","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/a8ed0c0f568744209a12c3e5.png"},{"id":10159184,"identity":"f02d5276-7812-4366-bfd1-7276b144b034","added_by":"auto","created_at":"2021-06-09 14:32:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83037,"visible":true,"origin":"","legend":"the RNA degradation plot.","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/9b9e1f6339849258f43bbdcd.png"},{"id":10159460,"identity":"dd69198e-77f8-4519-ac9c-bbe4dbdb2404","added_by":"auto","created_at":"2021-06-09 14:35:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312275,"visible":true,"origin":"","legend":"DEGs in 61 SLE patients and 20 normal individuals.\n(A)\tThe heatmap: the potential DEGs between SLE samples and normal samples in GSE50772. (B) The volcano plot: blue dots represent significantly down‐regulated genes, and red dots represent significantly up‐regulated genes.\n","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/a3dc91f61007bb3b1e1c6737.png"},{"id":10159465,"identity":"40aba3a1-9ffe-4cec-a15e-d47b148d308c","added_by":"auto","created_at":"2021-06-09 14:35:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439354,"visible":true,"origin":"","legend":"A bar chart of top 10 GO and KEGG terms of DEGs based on the count of genes.\n(A)BP: biological process; (B) CC: cellular component; (C) MF: molecular function;\n(D) KEGG: kyoto encyclopedia of genes and genomes.\n","description":"","filename":"OnlineFig.4ad.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/75ec8f83a3f5596e833d4997.png"},{"id":10159185,"identity":"ac8eca6e-ced7-47d4-8527-22a0df07a491","added_by":"auto","created_at":"2021-06-09 14:32:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2905042,"visible":true,"origin":"","legend":"The PPI networks and the most significant module of DEGs.\n(A) The PPI network of DEGs was constructed using Cytoscape. The red dot represents up-regulated gene and the green dot represents down-regulated gene. (B) Top 20 hub genes in network ranked by MCC method, and the color of a node in the network reflects the rank of hub genes. (C) Hierarchical clustering of hub genes was constructed using Excel. The samples under the pink bar are non-SLE samples and the samples under the glue bar are SLE samples. High expression of genes is marked in red; lower expression of genes is marked in blue. (D)The top-level Gene Ontology biological processes of hub genes were performed using Metascape.\n","description":"","filename":"OnlineFig.5ab.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/6ed89be7eedb465d860ef4f2.png"},{"id":10159459,"identity":"61c62a79-35fa-46cb-95f0-084ef23083c0","added_by":"auto","created_at":"2021-06-09 14:35:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62944,"visible":true,"origin":"","legend":"Interaction network between genes involved in top 10 biological processes and its targeted miRNAs. \nGenes are coloured in blue, and node size is adjusted according to number of targeted miRNAs; miRNAs are coloured in red; miRNAs targeting more than two genes simultaneously are coloured in green.\n","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/c83cd5a9279a2ea3a331085c.png"},{"id":10159458,"identity":"41eae55a-f5f5-46b5-82b1-0f60476ab5fb","added_by":"auto","created_at":"2021-06-09 14:35:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":134500,"visible":true,"origin":"","legend":"Venn diagram of key genes between five novel hub genes in our study and SLE-related genes in GeneCards. ORM1,SPLI and TCN1 are identified.","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/af70492830b5c4989fc022a5.png"},{"id":13697793,"identity":"3968df37-b002-481e-9ad0-155c56c40ffd","added_by":"auto","created_at":"2021-09-17 13:11:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4031661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/9cb30ed7-5e2e-49ab-b6aa-6cd89bc8b157.pdf"},{"id":10159464,"identity":"673967d6-be12-4f2d-8569-57aadf1266f7","added_by":"auto","created_at":"2021-06-09 14:35:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":425926,"visible":true,"origin":"","legend":"Additional file 1: 30 up-regulated genes and 227 down-regulated genes. Additional file 2: The 13 genes obtained from the intersection are shown in bold red. Additional file 3: SLE-related genes obtained from the GeneCards database.","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-576901/v1/fa56484ad3e4e28e8a960c8e.docx"}],"financialInterests":"","formattedTitle":"Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis","fulltext":[{"header":"Background","content":" \u003cp\u003eSLE is one of the most prevalent autoimmune diseases and often causes tremendous sufferings to patients. The risk of morbidity and mortality of SLE patients is still significantly high[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, making a confirmed diagnosis early of SLE has always been essential for initiating the appropriate therapy, but there is no single clinical symptoms or lab abnormality for diagnosing lupus definitely[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], even if plenty of biomarkers for diagnostic use have been found, such as antinuclear antibodies (ANAs), in particular anti-dsDNA antibodies, anti-Sm antibodies, as well as SLE-associated loci and genes, miRNAs, and other molecules[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As we all know, SLE is characterized by the protean clinical course and a broad spectrum of organ or system manifestations[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which has an important impact on prognosis of patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. But the manifestations are usually non-specific at onset, making it easy to confuse lupus with a variety of other diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, discovering more biomarkers with relatively high specificity and sensitivity is one of the most crucial and urgent problems for auxiliary diagnosis of SLE.\u003c/p\u003e \u003cp\u003eOver the years, with the wide application of DNA microarray technology and bioinformatics analysis, genetics and epigenetics have attracted extensive attention in SLE researches, especially the expression levels of genes and miRNAs acting as biomarkers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have declared that some genes are susceptible to SLE, such as IRF7, STAT4, BLK, etc [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and many genes even have been regarded as good biomarkers for diagnosing SLE, like OASL, ISG15 MX1, etc [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Most expression products of SLE-associated gene participate in immune response [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and many genes are also related with damaged target organs. In addition, as critical regulators in regulating post-transcriptional target gene expression, miRNAs can interrupt intercellular signal pathways, perturb immune homeostasis and produce autoantibodies, and eventually trigger the occurrence of autoimmune responses [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Strong evidences for the correlation between dysregulated miRNAs and the pathogenesis and adverse complications of SLE have been provided by published literatures [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBioinformatics faces huge-volume heterogeneous biological data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], including fundamental biology and the biology that underlies disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. During identifying biomarkers for SLE diagnosis, large datasets, through bioinformatic analysis, can be obtained to screen out virtual genetic or epigenetic alternations. In this paper, data quality analysis was performed on the GSE50772, which was downloaded from the GEO public database. In order to identify DEGs, gene expression data of SLE patients and normal controls were extracted by the limma package of R software. Furthermore, DEGs were analyzed for tissue-specific gene expression and the identification of hub genes by respectively using BioGPS and CytoHubba. Subsequently, the functional enrichment of DEGs was analyzed by DAVID, the PPI network of DEGs was constructed through STRING. Moreover, by means of starBase v2.0, the genes, which were involved in the top 10 biological processes with statistical significance, were selected to make miRNAs prediction and gene-miRNA interaction network analysis. Our results will provide new biological information to improve the understanding of the pathogenesis of SLE, and novel biomarkers may be helpful for diagnosiof the disease in early time.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus Database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) is an international public repository for the collection and distribution of high-throughput microarrays and next-generation sequenced functional genomic data sets [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The microarray expression dataset GSE50772, uploaded by Kennedy and Maciuca et al., was retrieved and downloaded from the GEO. The selected species was Homo sapiens, the type of data was microarray expression profiles, and the dataset was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array platform. This research contains 81 samples consisting of 61 subjects with SLE and 20 healthy controls. In addition, the annotation file for GPL570 was also obtained from the GEO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of the RNA quality of samples and preprocessing\u003c/h2\u003e \u003cp\u003eRNA degradation, proceeding from the 5\u0026prime; end to the 3\u0026prime; end, plays an crucial role in modulating gene expression and correcting systematic biases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. RNA degradation measurement presents the best correlation of the RNA integrity number (RIN) and an independent RNA integrity measurement, and therefore is able to be a valuable tool for quality control [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We used Affy package and affyPLM package of R software (R Foundation for Statistical Computing, Vienna, Austria) to estimate the quality of GSE50772 dataset, and the RNA degradation plot was to display the results of the analysis. RMA and KNN methods were used to preprocess the data of each sample in the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis\u003c/h2\u003e \u003cp\u003eR software was used to normalize and process the original expression matrix, and DEGs were screened via the limma package. The P- values were calculated by adopting the T- test methods, and the adjusted P‐ values were computed by applying the Benjamini and Hochberg's method. The DEGs were screened out by the following selection criteria: 1) | log2 (fold-change) | \u0026gt;1, and 2) the adjusted P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The heatmap and volcano map for the DEGs were created by SangerBox software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTissue-specific gene expression analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the tissue specific expression of the DEGs by the online resource BioGPS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biogps.org\u003c/span\u003e\u003c/span\u003e). If two following criteria were satisified, transcripts mapped to the single tissue would be identified as highly tissue specific: 1) The tissue-specific expression of the transcripts was 10 times higher than its median level, 2) The second highest expression level was lower than 1/3 of the highest expression level [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eWe used Database for annotation, visualization and integrated discovery (DAVID) v6.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/tools.jsp\u003c/span\u003e\u003c/span\u003e) to conduct the functional enrichment analyses of DEGs, including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO, which are used to predict protein functions, includes cell composition (CC), molecular function (MF) and biological process (BP) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The KEGG pathway analysis, which is used to allot a series of DEGs on specific pathways, constructs the molecular reaction, interaction and relationship [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, we conducted pathway analysis to identify which key pathways might be associated with DEGs. In addition, P- values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and enriched gene count\u0026thinsp;\u0026gt;\u0026thinsp;5 were chosen as the criteria for significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein-protein interaction (PPI) network analysis and hub genes identification.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe DEGs were uploaded to STRING(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e) to produce the PPI network diagram. These protein-protein interactions involve both functional and physical connections, with data derived primarily from high-throughput experiments, computational predictions, co-expression networks and automated text mining. In addition, the PPI network constructed from STRING analysis was imported into Cytoscape v.3.8.0 software, thereby to make a visual design of PPI network. And CytoHubba was used to process the network data to identify the top 20 hub genes. Subsequently, immune-related hub genes were identified by intersection between top 20 hub genes and immune genes, which were downloaded from the Immport immune database \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The GO analyses for these hub genes were performed on Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of pivotal miRNAs and identification of hub genes\u003c/h2\u003e \u003cp\u003eBased on the results of functional enrichment analysis of DEGs, genes enriched in top 10 statistically significant biological processes were selected and then performed with miRWalk 2.0 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, then their targeted miRNAs were further predicted [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Parallelly, in order to verify the accuracy of the results, we used miRWalk, miRDB and TargetScan for the intersection. Therefore, miRNAs targeting at more than two genes were screened out [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e) was used to identify hub genes which were found to serve as candidate biomarkers in our study.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy resign\u003c/h2\u003e \u003cp\u003eThe workflow chart of our study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Our original goal is to identify more useful biomarkers involved in SLE pathogenesis. Above all, RNA quality analysis was performed on the GSE50772 dataset, which was downloaded from the GEO public database. Extracted from the limma package of R software, gene expression datas of SLE patients and normal controls were used to filter DEGs. Then, the selected DEGs were analyzed for tissue-specific gene expression and the functional enrichment. The PPI network of DEGs was constructed followed, and through which, the top 20 hub genes were identified. In addition, miRNAs prediction and gene-miRNA interaction network analysis were performed on the genes which involved in the top 10 biological processes with statistical significance. Finally, the GeneCards database was used to verify SLE - related hub genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the RNA quality of samples\u003c/h2\u003e \u003cp\u003eTo assess the quality of the available data of GSE50772 in SLE, We applied Affy package and affyPLM package of R software to draw the RNA degradation plot for all sample arrays. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the analysis result demonstrates that 81 nearly parallel curves representing 81 samples have appropriate slopes, which doesn\u0026rsquo;t display the bias of probe-positional intensity associated with RNA integrity, indicating that the RNA quality of samples is relatively ideal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes\u003c/h2\u003e \u003cp\u003e|Log\u003csub\u003e2\u003c/sub\u003eFC| greater than 1 and adjusted P-values less than 0.05 were considered as criteria to screen the DEGs out. A total of 257 DEGs are obtained, among which 227 are down-regulated and 30 up-regulated. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the RPS4Y1, EIF1AY and KDM5D are the most up-regulated. Similarly, the three most down-regulated genes are CXCL8, ANXA3 and IFI27, whose log₂FC values are \u0026minus;\u0026thinsp;3.675, -3.186 and \u0026minus;\u0026thinsp;3.173 respectively (Additional file 1). The volcano plot and heatmap of the DEGs are seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 up-regulated and down-regulated genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog₂FC \u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUp-regulated\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPS4Y1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIF1AY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.96E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKDM5D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSP9Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.81E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLRC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22E-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCSAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.68E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.35E-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDCD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.51E-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM169A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDown-regulated\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANXA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOLFM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.10E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.19E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG0S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e log₂FC: log₂-transformed fold change of gene expression.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTissue-specific expression of genes\u003c/h2\u003e \u003cp\u003eWe used the BioGPS database, an online tool, to identify 57 genes expressed in specific tissues or organ systems. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the system with the most highly tissue-specific expression is the hematologic/immune system (59.6%, 34/57), followed by the digestive system (15.8%, 9/57). The respiratory and skin/skeletal muscle systems have similar levels of enrichment (about 8.8%, 5/57), while the urinary and reproductive systems have the lowest enrichment levels (about 3.5%, 2/57).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTissue-specific expressed genes identified by BioGPS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic/immune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAMP,ELANE,MMP8,DEFA4,CEACAM8,RSAD2,IFIT1, FFAR2,CXCR2, RETN,SLC25A37,CXCR1, SELENBP1,\u003c/p\u003e \u003cp\u003eMZB1,FPR2,SUCNR1,SLC22A4, CMPK2,IFIT2,FOSB,\u003c/p\u003e \u003cp\u003eHBM,AQP9,FPR1,PPP1R15A,MXD1, CCL4,NFE2, AZU1,\u003c/p\u003e \u003cp\u003eKCNJ2,MGAM,RNASE2,RNASE3,SH2D1B,EIF1AY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin/skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL1,IL1B,CCL2,TNNT1,CXCL3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCN1,ANXA3,C1QC,AREG,COL17A1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLFM4,HP,ORM1,LRG1,ARG1,CYP4F3,ANG,C15orf48,\u003c/p\u003e \u003cp\u003eKRT23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCN2,ALOX15B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADM,S100P\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFunctional and pathway enrichment of DEGs\u003c/h2\u003e \u003cp\u003eTo investigate the biological function of the DEGs, we performed functional enrichment analyses of 257 DEGs, GO annotation and KEGG pathway analyses were conducted by using the DAVID online tool. The top 10 GO items, including BPs, CCs and MFs, and the top 10 KEGG pathways, are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. Moreover, the top 10 biological processes of DEGs and their related details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, among which biological pathways with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are statistically significant. The results suggest that the biological pathways with DEGs significantly enriching are immune system\u0026ndash;related pathways. Furthermore, the extracellular exosome, cytosol and extracellular space, and integral component of plasma membrane account for the majority of CC. And the most abundant MFs are protein binding. KEGG pathway analysis reveals that the DEGs mainly enrich in Cytokine-cytokine receptor interaction, TNF signaling pathway and Chemokine signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top10 biological process (BP) of DEGs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einflammatory response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOLR1,C3AR1,PROK2,CXCL8,TNFAIP6,CXCL1,CXCL3,PTGS2,HCK,IL1B,CHI3L1,PTX3,SIGLEC1,TLR2,TNF,CXCL2,CXCR1,GPER1,CCL4,CXCR2,FPR1,ORM1,S100A12,CCL2,CCR1,CCL20,NMI,FOS,AZU1,FPR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimmune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.87E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFITM3,CXCL8,AQP9,CXCL1,CXCL3,CXCL2,FCGR3B,CXCL10,NFIL3,CCL4,CCL2,PGLYRP1,FCGR1B,CCR1,CCL20,IL1R2,IFI6,OAS1,SLPI,OAS3,IL1B,CEACAM8,C1QC,TLR2,TNF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einnate immune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC1QB,DEFA4,CRISP3,MX2,MX1,SH2D1B,BMX,HERC5,TLR2,IGLL5,CLEC4D,SLPI,GPER1,LCN2,S100A12,PTX3,CLEC4E,ANG,PGLYRP1,CAMP,C1QC, MSRB1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG-protein coupled receptor\u003c/p\u003e \u003cp\u003esignaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070008171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL8,CCL20,FPR1,FPR2,CXCL3,CXCL2,AREG,CCL4,CCL2,CXCL10,HCAR3,CXCR1,GPER1,C3AR1,PROK2,FFAR2,CXCL1,\u003c/p\u003e \u003cp\u003eSUCNR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType I interferon signaling\u003c/p\u003e \u003cp\u003epathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.67E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFITM3,EGR1,RSAD2,MX2,MX1,IFI6,ISG15,IFI35,IFIT1,IFIT3,IFIT2,OASL,IFI27,OAS1,\u003c/p\u003e \u003cp\u003eOAS3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.27E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCR1,CXCL8,CCL20,FPR1,CXCL1,FPR2,RNASE2,CXCL2,CXCL10,CXCR1,CXCR2,C3AR1,PROK2,CCL2,CMTM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edefense response to virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFITM3,RSAD2,MX2,MX1,ISG15,AZU1,IFIT1,IFIT3,OASL,IFIT2,HERC5,CXCL10,OAS1,OAS3,IFI44L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive regulation of cell\u003c/p\u003e \u003cp\u003eproliferation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002819349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRS2,ADM,AREG,CDC20,CXCL10,HCK,HLX,CEACAM6,NAMPT,CXCR2,MZB1,PROK2,PRTN3,CAMP,GPER1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFITM3,RSAD2,MX2,MX1,IFI44,IFIT1,TNF,IFIT3,IFIT2,OASL,OAS1,OAS3,CCL4,LCN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to lipopolysaccharide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJUN,CXCL1,FOS,CXCL3,PTGS2,SOD2,MPO,CXCL2,CXCL10,SLPI,ELANE,TLR2,ADM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePPI network construction, hub genes selection and analysis\u003c/h2\u003e \u003cp\u003eThe 257 DEGs were inputted to the STRING tool for further analysis, and a PPI network with 224 nodes and 1485 edges were visualized with Cytoscape. The interaction score of PPI network is greater than 0.4. The nodes correspond to genes, and the edges represent the links between genes. Green nodes represent down-regulated genes, red nodes represent up-regulated genes. The local clustering coefficient is 0.532 and PPI enrichment P-value is less than 1.0e-16. Then, the data file was processed with Cytoscape ( Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). CytoHubba was used to process the network data, and then to identify hub genes, the top 20 hub genes (CXCL1, CAMP, HP, PTX3, ARG1, ELANE, LCN2, RETN, MMP8, SLPI, PGLYRP1, LTF, OLFM4, ORM1, TCN1, LRG1, CRISP3, CHI3L1, MMP9 and DEFA4 ) were identified ( Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The color of a node in the network reflects the rank of hub genes. Clustering shows that the hub genes could basically differentiate the SLE samples from the non-SLE samples. Most hub genes are highly expressed in SLE samples, while relatively low in non-SLE samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In addition, functional enrichment analysis indicates that these hub genes mainly enrich in immune system process as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. In order to further explore and confirm the nature of hub genes, as shown in the supplementary materials for this study, 2482 immune genes downloaded from the Immport immune database were used to intersect with the top 20 hub genes. As expected, we found that 13 hub genes, including CXCL1, CAMP, PTX3, ARG1, ELANE, LCN2, RETN, SLPI, PGLYRP1, LTF, ORM1, MMP9 and DEFA4, were immune-related genes (Additional file 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFurther miRNA mining and identification of key genes\u003c/h2\u003e \u003cp\u003eAmong the top 10 biological processes, eighty-six genes, associated with statistically significant biological processes, were selected, and the gene-miRNA analysis was conducted with miRWalk 2.0 software. The intersection of miRNA results predicted by miRWalk, TargetScan and miRDB databases was considered as the result. The selection condition was set as P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the target gene binding region was 3\u0026prime;UTR. Therefore, hsa-let-7e-5p with high number of gene cross‐links (\u0026ge;\u0026thinsp;2) is identified, it targets at OLR1 and IRS2 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The score of it is 1, which means that it has high reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, using the GeneCards database, SLE - related genes were manually identified (Additional file 3), and three of our novel hub genes (ORM1, SLPI and TCN1) were verified to be potentially involved in the pathogenesis of SLE (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFifteen hub genes that have been reported in previous SLE studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene symbol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFull name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRole in SLE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehaptoglobin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edisplay immunosuppressive abilities to consist in the host defence responses to inflammation and infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRETN,\u003c/p\u003e\n\u003cp\u003eMMP8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResistin,\u003c/p\u003e\n\u003cp\u003eMatrix metalloprotein-ase-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eassociate with the presence of coronary artery calcium and increase vulnerability to atherosclerotic plaque\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG1,\u003c/p\u003e\n\u003cp\u003eCXCL1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003earginase 1,\u003c/p\u003e\n\u003cp\u003echemokine with C-X-C motif ligand 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emanipulate type 17 T helper cells (Th17) pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAMP,\u003c/p\u003e\n\u003cp\u003eLTF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCathelicidin antimicrobial peptide,\u003c/p\u003e\n\u003cp\u003eLactotransferr-in\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eestrogen exerts powerful effects on the immune response by affecting the expression of CAMP and LTF in B cells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDEFA4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDefensin alpha 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003estrongly link to immune function and numerous autoimmune diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePTX3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePentraxin 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea biomarker or therapeutic target of SLE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELANE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElastase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eparticipate into end-organ damage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLCN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLipocalin 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea nephritis-associated inflammatory mediator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLRG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeucine-rich alpha-2-glycoprotein 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereflect specific pathologic lesions in kidney and activity of lupus nephritis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePGLYRP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeptidoglycan recognition protein 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emight perturb the cytotoxic effect of autoantibodies, reduce tissue injury by competitively binding with autoantibodies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHI3L1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChitinase 3 like 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eevaluate the activity of lupus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatrix metallopeptid-ase 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eparticipate in pathways and immune system responses associated with SLE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n \u003c/div\u003e "},{"header":"Discussion","content":"\u003cp\u003eSLE is one of the most common systemic autoimmune diseases that seriously endangers human health. There are many factors causing the pathogenesis of SLE, among which the abnormal expression of important genes may contribute to lupus pathogenesis by participating in critical pathways, including immune complex processing, type I interferon producing, toll-like receptor signaling, and so on [37]. However, the accurate mechanism of SLE caused these microenvironmental factors has not been completely elucidated, so more attention should be paid to the detection and evaluation of the expression level of lupus - related genes in lupus researches. In present study, we are committed to discover possible SLE - causing molecules, and 257 DEGs (30 up-regulated genes and 227 down-regulated genes) are screened out from GSE50772 expression dataset. They certainly has laid a foundation for our subsequent analyses, and that may be able to illuminate the initiation and progression of SLE.\u003c/p\u003e\n\u003cp\u003eConsidering that multiple organs or systems involvement caused by autoantibodies is the feature of SLE, we performed tissue - specific expression analysis on DEGs. As revealed by the result, the most highly enriched system is the hematologic/immune system, which is in line with the pathogenesis and clinical manifestations of SLE, and this seems to explain the underlying molecular mechanisms of a self-aimed immune response in SLE patients. Besides, skin/skeletal muscle system, respiratory system, digestive system, urinary and reproductive systems are also enriched by DEGs. Some studies have also suggested that ANXA3 [38, 39], TCN1 [40], C1QC [41], AREG [42] and COL17A1 [43] are associated with respiratory injury. ALOX15B [44] may be related to kidney diseases as reported in the literature. ADM [45] and S100P [46] possibly play important roles in lesions of the reproductive system. And changes in expression of OLFM4 [47], CYP4F3 [48], HP [49], ORM1 [50], ANG [51], LRG1 [52], C15orf48 [53], KRT23 [54] and ARG1 [55] are involved with digestive system diseases. Even some of them have been thought to be classic markers of a particular tissue injury. While whether those tissue - specific DEGs above-mentioned are essential for the development of complications of SLE remains inconclusive, and we postulate that the abnormal expression of those genes probably can indicate organ involvement in SLE patients. However, in our results, there are other relatively common organs and tissues that are not significantly enriched by DEGs, such as the central nervous system, cardiovascular and circulatory systems. Limited gene expression microarray data with insufficient samples may be a by-no-means negligible cause.\u003c/p\u003e\n\u003cp\u003eIn order to understand disease machinery more deeply and to visualize the overview of the functional connections between all DEGs, we constructed PPI networks, the vital tools for analysis by identifying subnetworks or modules that display specific topology and/or functional characteristics [56]. Afterwards, on the basis of DEGs\u0026rsquo; PPI networks, the top 20 hub genes (CXCL1, CAMP, HP, PTX3, ARG1, ELANE, LCN2, RETN, MMP8, SLPI, PGLYRP1, LTF, OLFM4, ORM1, TCN1, LRG1, CRISP3, CHI3L1, MMP9 and DEFA4) were selected. According to cluster analysis results on them, it is obvious that most hub genes are up-expressed in SLE patients, while relatively low-expressed in normal subjects, which highlights the importance and representativeness of these hub genes in SLE disease. And further functional enrichment analysis on them manifests that immune system processes are dominant. Furthermore, 13 hub genes verified by Immport database are thought to be immune-related genes as expected, namely CXCL1, CAMP, PTX3, ARG1, ELANE, LCN2, RETN, SLPI, PGLYRP1, LTF, ORM1, MMP9 and DEFA4. The result exactly supports the idea that these 20 hub genes probably play essential roles in immune-related pathways which can trigger autoimmune dysfunction in patients, and resulting in the pathogenesis and development of SLE.\u003c/p\u003e\n\u003cp\u003eIn prior studies on 20 hub genes mentioned above, the aberrant expression levels of 15 genes have been investigated that they may have various and crucial influences for different processes of SLE development. Given the roles of five novel genes in SLE as shown in Table\u0026nbsp;4, several novel genes, including ORM1, SLPI, OLFM4, TCN1 and CRISP3 may also have diagnostic value in the condition. ORM1 (orosomucoid 1) is an acute phase plasma protein known to activate NF\u0026kappa;B, p38 and JNK pathways in macrophages, and it has been reported in rheumatoid arthritis (RA) [57], sarcoidosis and other immune diseases [58]. In experimental autoimmune encephalomyelitis, SLPI (secretory leukocyte peptidase inhibitor) exerted potent pro-inflammatory actions by regulating T cell activity, a process that might benefit the patient [59]. OLFM4(olfactomedin 4) could mediate the autoimmune inflammatory responses of generalized pustular psoriasis, a severe inflammatory skin disease [60]. Low expression of TCNI (transcobalamin I) involved in innate immunity might be partly responsible for the pathogenesis of IgG4-related disease, due to impairments in the innate immune system [61]. Since CRISP3 (cysteine-rich secretory protein 3) was detected to be significantly elevated in RA, the researchers hypothesized that it was implicated in the development of RA [62]. In addition, using the GeneCards database, three novel hub genes (ORM1, SLPI, and TCN1) were confirmed to be potentially involved in the pathogenesis of SLE. Almost all of these genes, either high or low expression, are associated in the development of immune diseases. Consequently, chances are that the five hub genes play pivotal roles in the molecular mechanism of SLE pathogenesis, and we reasonably confer that these novel hub genes may be used as biomarkers to help improve the diagnostic rate of SLE and to provide valuable information for the evaluation of organ or system involvement.It is well known that miRNAs can interfere with the transcription and regulate gene expression [63]. Altered miRNA expression has been regarded as another important factor to the pathogenesis of immune-related diseases, such as SLE. And because of the nature of stability of miRNA, measuring effective miRNA levels may be conducive to disease detectionin [64]. In immune cells, aberrant miRNAs can disturb immune homeostasis, produce massive autoantibodies and induce autoimmunity [65]. Following GO terms, we performed miRNA mining and interaction network analysis. MiRNA hsa-let-7e-5p targeting at OLR1(oxidized low- density lipoprotein receptor 1) and IRS2 ( insulin receptor substrate 2) was identified. The miRNA let-7e is a member of the let-7 family, and it plays a key role in inhibiting or promoting inflammatory response by regulating cytokine expression in various inflammatory and autoimmune diseases [66]. In an animal experiment, down-regulating the expression of hsa-let-7e-5p and other two miRNAs, 17\u0026beta;-estradiol could amplify the activation of IFN-\u0026alpha; signaling in B cells to contribute to the sex bias in SLE [67\u0026ndash;69]. Moreover, as target genes of has-let-7e-5p in this study, OLR1 and IRS2 are down-regulated in SLE patients compared with non-SLE subjects. When it comes to biological processes, OLR1 is associated with inflammatory response, while ISR2 is related to positive regulation of cell proliferation. Regarding molecular function, OLR1 and IRS2 both participate in exerting protein binding. Recent studies show that OLR1 is an inflammation-induced receptor. Together with a host of other reactions, an increase in OLR1 can trigger the formation of neutrophil extracellular traps, which can promote systemic inflammation, vascular damage and lung injury. Elevated expression level of OLR1 has been recognized as a possible indicator of high risk of SLE - related cardiovascular disease [70, 71]. Targeted by MiR-203a, IRS2 regulates the proliferation and apoptosis of pancreatic \u0026beta; cell [72], which implies the expression of IRS2 is closely related to type 1 diabetes mellitus (T1DM), an autoimmune disease. These results rend us to speculate that has-let-7e-5p may be a potential molecule to induce and deteriorate the SLE even though there have been rare relevant studies are published on this subject. Thus, we propose that hsa-let-7e-5p probably acts as another novel latent biomarker of SLE, and we hope it could provide new insights into molecular mechanism underlying the development and progression SLE.\u003c/p\u003e\n\u003cp\u003eAdditionally, since the GSE50772 is a public dataset, patient consent or ethics committee approval is not required, but the information on individuals\u0026rsquo; age, gender and health status, as well as medication use, is absent, which appears to be an underlying limitation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, some DEGs specifically expressed in a tissue or system might be a signal to estimate organ involvement in SLE, and novel candidate biomarkers including hub genes ( ORM1, SLPI, OLFM4, TCN1 and CRISP3) and has-let-7e-5p were identified to assist in diagnosing SLE through comprehensive bioinformatic analyses. Our point will provide new and meaningful reference for later SLE studies. Since the current finding is limited by the lack of experimental validation in vivo and in vitro, it is necessary to conduct multiple in-depth studies to detect and verify those potential biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks for all the authors who provided the help for the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the grants from the 2019 College-level Teaching Reform Research Project [grant numbers 02.03.2019.15-15]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYS, CW and FW conceived the study and participated in the study design, performance, coordination and manuscript writing. YS, CW, FW, RC, and CY carried out the analysis. YL and YW revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have consented for the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan Sun, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eChen-chen Wang, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eFu-quan Wang, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eRui Chen, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eChun-lin Yao, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eYan Wu, Phone: +8602785351606, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eYun Lin, Phone: +8602785351606, Email: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFrieri M. 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Rev Esp Enferm Dig. 2019;111:329.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatic analysis, Systemic lupus erythematosus, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-576901/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-576901/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe discovery of biomarkers has become an attractive field in studying autoimmune diseases. For example, in the study of systemic lupus erythematosus (SLE), various biomarkers such as genes and miRNAs have been identified for the diagnosis of SLE and its organ involvement. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe expression data of gene microarray GSE50772 was downloaded from the GEO, and 257 differentially expressed genes (DEGs) were obtained by using limma plug-in for R software. The tissue-specific gene expression analyses were performed in BioGPS database. Then, a protein-protein interaction (PPI) network was constructed with STRING and visualized in Cytoscape. Whereafter, top twenty hub genes derived from the PPI network, could basically differentiate the SLE samples from the non-SLE samples, were ascertained through CytoHubba. What is noticeable is that the five novel hub genes ( ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a related miRNA (hsa-let-7e-5p) may be considered as candidate biomarkers of SLE. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFive genes (ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a miRNA(hsa-let-7e-5p) \u0026nbsp;in this discovery-driven study may become potential biomarkers for diagnosing SLE and assessing its organ damage, and they also will provide valuable information on the pathogenesis of SLE.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-09 14:32:49","doi":"10.21203/rs.3.rs-576901/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6e769a3-08ec-45b6-b5e3-efec8befdde6","owner":[],"postedDate":"June 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4903730,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2021-07-07T02:21:52+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-09 14:32:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-576901","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-576901","identity":"rs-576901","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}