Hub Genes and Key Pathway Identification in Wilms Tumor Based on Bioinformatics Analysis

Hub Genes and Key Pathway Identification in Wilms Tumor Based on Bioinformatics 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 Article Hub Genes and Key Pathway Identification in Wilms Tumor Based on Bioinformatics Analysis Basavaraj Vastrad , Chanabasayya Vastrad , Iranna Kotturshetti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-133323/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 Wilms tumor (WT) is a childhood kidney cancer with unknown etiology. Gene expression analysis has become very essential in WT. Thus, we performed an integrated analysis of gene expression data to identify new molecular mechanisms and key functional genes in WT. Gene expression (GSE60850) dataset was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using limma. Pathway and Gene Ontology (GO) enrichment analyses were performed for the DEGs by ToppGene database. Then, protein–protein interaction (PPI) networks and modules were established by the Mentha database and PEWCC1, and visualized by Cytoscape software. Target gene - miRNA regulatory network and target gene - TF regulatory network were established by the Network Analyst database and visualized by Cytoscape software. Finally, survival analysis, expression analysis, stage analysis, mutation analysis, immunohistochemical (IHC) analysis, receiver operating characteristic (ROC), reverse transcription polymerase chain reaction (RT-PCR) and immune infiltration analysis of hub genes was performed. We identified 988 DEGs ultimately including 502 up regulated genes and 486 down regulated genes. Pathway and GO enrichment analysis revealed that DEGs were mainly enriched in D-myo-inositol (3,4,5,6)-tetrakisphosphate biosynthesis, platelet activation, cholesterol biosynthesis III, and complement, coagulation cascades, embryo development, cell surface, DNA-binding transcription factor activity, carboxylic acid metabolic process, extracellular space and signaling receptor binding. FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were filtrated as the hub genes. These identified DEGs and hub genes facilitate our knowledge of the underlying molecular mechanism of WT and have the potential to be used as diagnostic and prognostic biomarkers or therapeutic targets for WT. Bioinformatics protein–protein interaction gene expression Wilms tumor pathways differentially expressed genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 Introduction Wilms tumor (WT) is the rare diagnosed pediatric tumor worldwide and is named as nephroblastoma [1]. WT is form of kidney cancer that mostly advances in children under age under 10 years [2]. Because of routine early screening and recent advances in treatment techniques, long-term survival rates have upgraded [3]. However, in developing countries, most WT patients are diagnosed at an end stage, with poor prognosis [4]. Therefore, further studies should still be emphasized for the early diagnoses, prognosis and targeted therapy of WT. Genetic aberrations and its related pathways have been reported to be significant factors contributing to the progression of WT. Genes such as IGF2 [5], WT1 [6], RASSF1A [7], PAF1 [8], and DROSHA and DICER1 [9] as well as signaling pathways such as WNT/β‐catenin pathway [10], IGF signaling pathway [11], S1P/S1P1 signaling pathway [12], PTEN/PI3K/AKT signaling pathway [13] and VEGF‐C/VEGFR‐2 signaling pathway [14] were responsible for pathogenesis of WT. Despite improvement and progress in WT diagnosis, prognosis and treatment, the underlying WT molecular mechanisms are not entirely clear and novel diagnosis, prognosis and treatment options are still needed for more effective control of WT development. Gene expression profile analysis is a high-throughput method for detecting messenger RNA expression in various cancer tissues or cell samples. By analyzing the different gene expression between cancer patients and normal controls, an improved understanding of the molecular mechanism of a various tumors can be obtained, facilitating the identification of the potential key genes and pathways for diagnostics markers, prognostics markers and targeted therapy [15-16]. The current study aimed to explore the molecular pathogenesis of WT by a computational bioinformatics analysis of gene expression. Gene expression data from the Gene Expression Omnibus (GEO) database was extracted, and differentially expressed genes (DEGs) between WT and normal samples were identified. The possible functions of the DEGs were predicted using pathway and gene ontology (GO) enrichment analysis. Furthermore, protein-protein interaction (PPI) networks were constructed using mentha PPI database, and visualized and module analysis was conducted using Cytoscape software to search for essential hub genes that may be associated in the progression of WT. Dysregulation of microRNAs (miRNAs) and transcription factors (TFs) have been indicated to be associated with the pathogenesis of WT, the WT specific regulatory networks of target gene and miRNA, and target gene and TFs were constructed. Validation of the hub genes was performed to screen genes with prognostic and diagnostics significance in WT. Materials And Methods Microarray data Human gene expression microarray data of WT samples (n = 36) and normal samples (n = 36) were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) with an accession ID of GSE60850. The platform of GSE60850 is GPL19130 Breakthrough Human 17K 2.1.2. Data preprocessing The raw data in GSE60850 were preprocessed using limma [17], an R software package and it implemented background correcting, quantile normalization and expression calculation automatically. Then, probe values were translated to gene-symbol values based on message associated in microarray platform, and probes without proper gene-symbols were excluded. Differentially Expressed Genes Based on the gene expression microarray data, DEGs between WT samples and normal samples were identified using limma [17], an R software package. The corresponding p-values were calculated using t-test provided by limma. The genes met the criteria of p-value<0.05 and |log2 fold change (FC)|≥1.22 for up regulated genes and |log2 fold change (FC)|≥ -1.39 for down regulated genes were defined as significant DEGs between the two groups. Pathway enrichment analysis of DEGs BIOCYC ( https://biocyc.org/ ) [18], Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) [19], Pathway Interaction Database (PID, http://pid.nci.nih.gov/) [20], Reactome ( https://reactome.org/PathwayBrowser/ ) [21], Molecular signatures database (MSigDB, http://software.broadinstitute.org/gsea/msigdb/ ) [22], GenMAPP (http://www.genmapp.org/) [23], Pathway Ontology (https://bioportal.bioontology.org/ontologies/PW) [24] and PantherDB (http://www.pantherdb.org/) [25] database are used to understand the high-level functions and utilities of the biological system. ToppGene (ToppFun) ( https://toppgene.cchmc.org/enrichment.jsp ) [26] is a comprehensive set of functional annotation tools for researchers to understand biological meaning behind large scale of genes. P < 0.05 was set as the cut-off criterion. GO enrichment analysis of DEGs GO (http://www.geneontology.org/) [27] enrichment analysis is a universal genes analysis method, which can contribute functional classification for genomic data, including categories of BPs, cellular component (CC), and molecular function (MF). ToppGene (ToppFun) ( https://toppgene.cchmc.org/enrichment.jsp ) [26] is an online tool for gene functional classification, which can systematic and integrative analysis of large gene lists. In this study, to analyze the functions of DEGs, GO enrichment analysis were conducted using the ToppGene online tool; p < 0.05 was set as the cutoff point. PPI network construction and module analysis Mentha ( https://mentha.uniroma2.it/index.php ) [28] was an online biological tool which had a major role in the analysis of biological information and integrates different PPI database such as IntAct (https://www.ebi.ac.uk/intact/) [29], MINT ( https://mint.bio.uniroma2.it/ ) [30], BioGRID (https://thebiogrid.org/) [31], DIP ( http://dip.doe-mbi.ucla.edu/dip/Main.cgi ) [32] and MatrixDB ( http://matrixdb.univ-lyon1.fr/ ) [33]. As a result, based on the STRING database, a protein–protein interaction (PPI) network of WT was built. PPIs of DEGs (up and down regulated genes) were selected with a combination score >0.9. Subsequently, the PPI network was input into Cytoscape (http://www.cytoscape.org/) (version: 3.7.2) [34]. Five topological methods (node degree, betweenness centrality, stress centrality, closeness centrality and clustering coefficient ) using to rank and evaluated hub genes using network analyzer [35-39] and modules analysis were taken using PEWCC1 of Cytoscape plugin [40]. Construction of target gene - miRNA regulatory network The target genes - miRNA interactions were predicted with NetworkAnalyst (https://www.networkanalyst.ca/) [41], which involves two miRNA databases such as DIANA-TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) [42] and miRTarBase ( http://mirtarbase.mbc.nctu.edu.tw/php/download.php ) [43]. Subsequently, the target genes - miRNA regulatory network was input into Cytoscape (version: 3.7.2) [34]. Construction of target gene - TF regulatory network Experimentally-validated target genes and their TFs were screened in one TF database ChEA database (http://amp.pharm.mssm.edu/lib/chea.jsp) [44]. TFs that have a regulatory relationship with the target genes in the constructed network were identified. The NetworkAnalyst (https://www.networkanalyst.ca/) [41] online tool was used to predict TF-regulating genes in the network. Cytoscape (version: 3.7.2) [34], an open-source platform for visualizing complex networks, was used to visualize target genes - TF regulatory network. Validation of hub genes The survival probability study was implemented using Kaplan-Meier method to compare overall survival curves between high and low expression gene groups UALCAN (https://ualcan.path.uab.edu/index.html) online dataset [45], which is a user-friendly, interactive web resource for the analysis of cancer transcriptome data.. P<0.05 was considered to indicate a statistically significant difference. The expression analysis and stage analysis of hub genes were analyzed using UALCAN online dataset [45]. The mutation frequencies of up and down hub genes were inquired in cBioportal online database (http://www.cbioportal.org/) [46]. In addition, up and down regulated hub genes were further validated for their prognostic values (immunohistochemical (IHC) analysis in normal and cancer tissue) using The Cancer Genome Atlas database ( https://www.proteinatlas.org/ ) [47]. Receiver operating characteristic (ROC) analyses are generally used to check out the conduct of disease diagnosis and prognosis. The area under the curve (AUC) was used to demonstrate the accuracy of an individual gene for predicting recurrence using R package“pROC” [48]. Reverse transcription polymerase chain reaction (RT-PCR) was carried out for validation of up and down regulated hub genes. Total RNA was extracted from the WT tissue sample and normal kidney tissue samples using TRI Reagent® (Sigma, USA) according to the manufacturer's protocol. A RNA was reverse transcribed into cDNA using FastQuant RT kit (with gDNase; Tiangen Biotech Co., Ltd.), according to the manufacturer's protocol. The primer sequences (Genewiz, Inc.) used for RT-PCR are listed in Table 1. The mRNA expression levels of hub genes were measured by Real time-PCR using the QuantStudio 7 Flex real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA) . The following reaction conditions were used for RT-PCR: Initial denaturation at 95˚C for 3 min followed by 40 cycles of denaturation at 95˚C for 10 sec and annealing and elongation at 60˚C for 30 sec. The relative expression levels of up and down regulated hub genes were determined using the 2 -ΔΔCt method [49] and normalized to the internal reference gene, β-actin. Immune infiltration analysis was performed using the TIMER (https://cistrome.shinyapps.io/timer/) [50] is a RNA-Seq expression profiling database from The Cancer Genome Atlas (TCGA) portal for up and down regulated hub genes, which is used to check the immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) across WT. Results Identification of DEGs After data, including 36 WT samples and 36 normal samples, was downloaded from GEO database and preprocessed. The results before and after normalization are shown Fig. 1A and Fig. 1B. 988 DEGs, including 486 up genes and 502 down genes were identified using limma packages on the basis of the cut off criteria (P 1.39 for up regulated genes and |log2 fold change (FC)| < -1.22 for down regulated genes) in WT samples compared with normal samples (Table 1). The volcano plot showed the up regulated and down regulated genes in dataset GSE60850 is shown in Fig. 2. The details of up and down regulated gene expression heat map are shown in Fig. 3 and Fig. 4. Pathway enrichment analysis of DEGs Pathway enrichment analysis of the DEGs (up and down regulated genes) was performed using ToppGene. Pathways were identified for the up regulated genes, including the cholesterol biosynthesis III (via desmosterol), superpathway of methionine degradation, complement and coagulation cascades, ECM-receptor interaction, FOXA1 transcription factor network, direct p53 effectors, hemostasis, extracellular matrix organization, phenylalanine tyrosine and tryptophan biosynthesis, tyrosine metabolism, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, genes encoding enzymes and their regulators involved in the remodeling of the extracellular matrix, plasminogen activating cascade, blood coagulation, altered lipoprotein metabolic, gluconeogenesis pathway, phenylalanine and tyrosine metabolism. Similarly, pathways were identified for the up regulated genes including the D-myo-inositol (3,4,5,6)-tetrakisphosphate biosynthesis, 1D-myo-inositol hexakisphosphate biosynthesis V (from Ins(1,3,4)P3), platelet activation, protein digestion and absorption, endothelins, alpha-synucleinsignaling, extracellular matrix organization, degradation of the extracellular matrix, MAP kinase kinase activity, glycolysis, gluconeogenesis, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, ensemble of genes encoding core extracellular matrix including ECM glycoproteins, collagens and proteoglycans, Wnt signaling pathway, integrin signalling pathway, activinsignalin, parkinson disease, quinapril pathway and diltiazem pathway. The detailed results of pathway enrichment analysis for up and down regulated genes are presented in Table 2 and Table 3. GO enrichment analysis of DEGs All up and down regulated genes were uploaded to the ToppGene software to identify GO function. GO enrichment analysis results for up and down regulated genes are presented in Table 4 and Table 5. For biological processes (BP), the top GO terms of up and down regulated genes were significantly enriched in carboxylic acid metabolic process, oxoacid metabolic process, embryo development and animal organ morphogenesis, were included. For cell component (CC), top GO terms of up and down regulated genes were significantly enriched in cell surface, endoplasmic reticulum, neuron projection and neuron part. For molecular function (MF), the top GO terms of up and down regulated genes were significantly enriched in signaling receptor binding, identical protein binding, DNA-binding transcription factor activity and calcium ion binding. PPI network construction and module analysis The Mentha PPI database was used to construct PPI networks. The PPI network of the up regulated genes is illustrated in Fig. 5 with 7649 nodes and 17236 edges. The topology analysis (higest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustring coefficient) for up regulated genes showed that ESR1, FN1, AURKA, SMURF1, PDK1, NANOG, SLC25A5, NUDT21, KCNQ3, ADM, CEL, CXCL3 and GABRA5 were the hub genes (Table. 6) and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are shown in Fig. 6A - 6E. These identified hug genes were enriched in neuron part, ECM-receptor interaction, metabolism of proteins, negative regulation of response to stimulus, carboxylic acid metabolic process, response to oxygen-containing compound, programmed cell death, identical protein binding, cell surface, signaling receptor binding, metabolic pathways, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, and regulation of response to stress. Similarly, PPI network of the down regulated genes is illustrated in Fig. 7 with 7691 nodes and 16050 edges. The topology analysis (higest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustring coefficient) for down regulated genes showed that VCAM1, DDIT4L, TCF4, PLK1, RB1, MEOX2, SYK, PLXDC1, TCF7L2, MAPK10, MAGI1 and MRPL15 were the hub genes (Table. 6) and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are shown in Fig. 8A - 8E. These identified hug genes were enriched in cell adhesion molecules (CAMs), regulation of Wnt-mediated beta catenin signaling and target gene transcription, FoxO family signaling, regulation of retinoblastoma protein, embryo development, animal organ morphogenesis, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, pathways in cancer, innate immune system and ATP binding. Based on the hub genes (up and down regulated) from the PPI module, pathway and GO terms for further analysis. We chose the four most significant modules (up regulated genes) for further analysis (Fig.9). Module 14 consisted of 174 nodes and 311 edges, module 24 consisted of 131 nodes and 144 edges, module 39 consisted of 113 nodes and 111 edges, and module 40 consisted of 97 nodes and 99 edges. Hub genes in these PPI modules were mainly enriched in the ECM-receptor interaction, metabolism of proteins, negative regulation of response to stimulus, programmed cell death, response to endogenous stimulus, neuron part, protein-containing complex binding, focal adhesion, proteoglycans in cancer, regulation of cell differentiation, signaling receptor binding, enzyme regulator activity, carboxylic acid metabolic process, regulation of response to stress, metabolism of amino acids and derivatives, metabolism of lipids and lipoproteins, cell motility and enzyme binding. Finally, we chose the four most significant modules (down regulated genes) for further analysis (Fig.10). Module 17 consisted of 145 nodes and 186 edges, module 24 consisted of 122 nodes and 188 edges, module 34 consisted of 100 nodes and 117 edges, and module 40 consisted of 93 nodes and 95 edges. Hub genes in these PPI modules were mainly enriched in the pathways including DNA-binding transcription factor activity, transcription regulatory region DNA binding, sequence-specific DNA binding, pathways in cancer, extracellular matrix organization, hemostasis, innate immune system, PDGFR-beta signaling pathway, cytokine signaling in immune system, signaling receptor binding, molecular function regulator, Wnt signaling pathway, embryo development, neurogenesis, regulation of cell differentiation, positive regulation of multicellular organismal process and cell surface. Construction of target gene - miRNA regulatory network Based on the interaction information of target genes and miRNAs in corresponding miRNA databases, the integrated regulatory network of target genes (up and down regulated) and relevant miRNAs were constructed (Fig. 11 and Fig. 12). We found that up regulated target genes such as CCND1 can be targeted by 197 miRNAs (ex, hsa-mir-2392), SCD can be targeted by 167 miRNAs (ex, hsa-mir-1269a), PTP4A1 can be targeted by 132 miRNAs (ex, hsa-mir-6731-5p), LDLR can be targeted by 123 miRNAs (ex, hsa-mir-4295) and RRM2 can be targeted by 102 miRNAs (ex, hsa-mir-4458) are listed in Table 7. These identified target genes were enriched in focal adhesion, PPAR signaling pathway, cell motility, organic substance catabolic process and superpathway of purine nucleotide salvage. Similarly, we found that down regulated target genes such as ZNF703 can be targeted by 115 miRNAs (ex, hsa-mir-3938), ENPP5 can be targeted by 114 miRNAs (ex, hsa-mir-4768-3p), MYLIP can be targeted by 113 miRNAs (ex, hsa-mir-552-5p), ENAH can be targeted by 92 miRNAs (ex, hsa-mir-4282) and ZBTB20 can be targeted by 85 miRNAs (ex, hsa-mir-4282) are listed in Table 7. These identified target genes were enriched in regulation of multicellular organismal development, integral component of plasma membrane, adaptive immune system, axon guidance and positive regulation of multicellular organismal process. Construction of target gene - TF regulatory network Based on the interaction information of target genes and TFs in corresponding TF database, the integrated regulatory network of target genes (up and down regulated) and relevant TFs were constructed (Fig. 13 and Fig. 14). We found that up regulated target genes such as MAGEC2 can be targeted by 207 TFs (ex, SOX2), TSPAN7 can be targeted by 173 TFs (ex, MYC), ESR1 can be targeted by 172 TFs (ex, HNF4A), PCSK6 can be targeted by 166 TFs (ex, EGR1) and LDLR can be targeted by 145 TFs (ex, TP63) are listed in Table 8. These identified target genes were enriched in organic substance catabolic process, intrinsic component of plasma membrane, neuron part, golgi apparatus and identical protein binding.Similarly, we found that down regulated target genes such as PLEKHO1 can be targeted by 184 TFs (ex, SOX2), CACHD1 can be targeted by 151 TFs (ex, AR), CASD1 can be targeted by 139 TFs (ex, NANOG), GLIS3 can be targeted by 132 TFs (ex, STAT3) and AFF3 can be targeted by 130 TFs (ex, TP53) are listed in Table 8. These identified target genes were enriched in cell projection part, regulation of transcription by RNA polymerase II and DNA-binding transcription factor activity. Validation of hub genes Te overall survival rates of patients with high expression of UCHL1, FN1, AURKA, TRIM41 and TXNDC5 were all significantly lower than those of patients with low/medium expression (Fig. 15), while overall survival rates of patients with low expression of SIN3A, MAGI1, GPRASP2, FXYD6 and NFKBIA were all significantly lower than those of patients with high expression (Fig. 16). The box plots (expression analysis) showed that the expression levels of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were significantly higher in primary tumor than those in the normal kidney for WT patients from TCGA (Fig. 17A -17E), while the expression levels of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were significantly lower in primary tumor than those in the normal kidney for WT patients from TCGA (Fig. 17F -17J). The box plot suggested (stage analysis) that the high expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 show significant distance in different pathological stages in KT compared to normal (Fig. 18A -18E), while low expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 show significant distance in different pathological stages in KT compared to normal (Fig. 18F -18J). Up and down regulated hub genes’ alteration statuses in TCGA WT patients were analyzed using the CbioPortal database. FN1 altered (2%), and missense mutation, truncating mutation, amplification and deep dilation were the main type. AURKA altered (0%). TRIM41 altered (8%), and missense mutation and amplification were the main type. NFKBIA altered (0.3%), and amplification was the main type. TXNDC5 altered (0.7%), and missense mutation and truncating mutation were the main type. SIN3A altered (0.3%), and missense mutation was the main type. MAGI1 altered (2.8%), and inframe mutation, amplification and deep dilation was the main type. GPRASP2 altered (2%), and truncating mutation and amplification were the main type. UCHL1 altered (0%). FXYD6 altered (0.3%), and amplification was the main type. The frequencies of alteration of each hub gene are shown in Fig. 19. The Human Protein Atlas database, which indicated the expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were higher in WT tissue compared to normal kidney tissues (Fig. 20A-20E), while expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were lower in WT tissue compared to normal kidney tissues (Fig. 20F-20J). The ROC curve defined an optimal threshold to predict the recurrence risk of WT, and the AUC values of the ROC for FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were 0.991, 0.998, 0.952, 0.939, 0.994, 0.954, 0.905, 0.947, 0.938 and 0.973, respectively (Fig. 21). RT-PCR result were consistent with the results of the database analysis, mRNA expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were significantly higher in WT tissues compared with normal kidney tissues (Fig. 22A - 22E), while mRNA expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were significantly lower in WT tissues compared with normal kidney tissues (Fig. 22F – 22J). The Immune infiltration analysis of up and down hub genes from the TIMER was investigated using TCGA database. The results demonstrated that the higher expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were all negatively associated with tumor purity (Fig. 23A - 23E), while lower expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 positively associated with tumor purity (Fig. 23F - 23J). Discussion To better uncover the molecular pathogenesis and develop new prognostic, diagnostics and therapeutic strategies for WT, we performed this integrated analysis between WT patients and normal controls. A total of 988 genes across the studies were consistently differentially expressed in WT (502 up regulated and 486 down regulated) with FDR < 0.05. MEIS1 was identified with development of WT [51]. GUCY1A3 was linked with angiogenesis in glioma [52], but this gene may be involved in angiogenesis in WT. TFF1 was linked with progression of gastric cancer [53], but this gene may be involved in pathogenesis of WT. Elevated expression of genes such as FGG (fibrinogen gamma chain) [54] and CGA (glycoprotein hormones, alpha polypeptide) [55] were liable for advancement of hepatocellular carcinoma, but high expression of these genes may be associated with pathogenesis of WT. Methylation inactivation of tumor suppressor genes such as HOXA11 [56] and MAPK10 [57] were associated with development of various cancers types, but loss of these genes may be liable for progression of WT. Genes such as COL3A1 [58], S100P [59] and MYO1B [60] were important for invasion of various cancer cells types, but these genes may be linked with invasion of WT cells. In pathway enrichment analysis for up regulated genes were carried out.. High expression of enriched genes such as SERPINA1 [61], FGB (fibrinogen beta chain) [62], SCG3 [63], ITIH3 [64], FST (follistatin) [65], AMBP (alpha-1-microglobulin/bikunin precursor) [66], IGFBP1 [67], IGFBP6 [68] and PLOD3 [69] were responsible for advancement of various cancers types, but over expression of these genes may linked with pathogenesis of WT. Enriched genes such as CLU (clusterin) [70], VTN (vitronectin) [71], SERPINE1 [72], SERPINE2 [73], FN1 [74], SLC3A2 [75], ITGA2 [76], ITGA3 [77], ITGA5 [78], DOCK2 [79], L1CAM [80], CAV1 [81], TSPAN7 [82], CRLF1 [83], SRPX (sushi-repeat-containing protein, X-linked) [84], FGL1 [85], CCL20 [86], COL1A2 [87], SEMA3C [88], GDF15 [89], ANXA11 [90], SPP1 [91], LAMA1 [92], TDGF1 [93], CXCL3 [94], LGALS3 [95], SERPINB1 [96] and LUM (lumican) [97] were associated with invasion of various cancer cells types, but these genes may be liable for invasion of WT cells. Enriched genes such as SERPINA5 [98], ENO2 [99] and CSTB (cystatin B (stefin B)) [100] were involved in development of various cancers types, but these genes may be responsible for advancement of WT. Alteration in genes such as ESR1 [101], FOXA1 [102] and PRSS1 [103] were important for development of various cancer types, but mutation in these genes may be liable for progression of WT. Enriched genes such as NKX3-1 [104], GATA2 [105], CEACAM1 [106], RAB27B [107], SCUBE2 [108] and THBS2 [109] were involved in advancement of various cancers types, but these genes may be responsible for progression of WT. Enriched polymorphic genes such as MMP1 [110], APOA1 [111], ITPR3 [112], MMP3 [110], IGFBP3 [113], CSH1 [114], SERPINA6 [115] and APOC2 [116] were associated with pathogenesis of various cancers types, but these polymorphic genes may be linked with development of WT. Enriched genes such as VEGFC (vascular endothelial growth factor C) [117], GATA3 [118], CD44 [119] and S100A4 [120] were important for development of WT. Our study found that FDFT1, EBP (emopamil binding protein (sterol isomerase)), DHCR7, F5 (coagulation factor V (proaccelerin, labile factor)), SERPINC1, C4A, C4BPB, APOB (apolipoprotein B (including Ag(x) antigen)), PDE2A, DGKZ (diacylglycerol kinase, zeta 104kDa), APOH (apolipoprotein H (beta-2-glycoprotein I)), LRP8, HBE1, HBG1, GOT1, PAH (phenylalanine hydroxylase), FSTL3, AREG (amphiregulin (schwannoma-derived growth factor), ITIH2, COCH (coagulation factor C homolog, cochlin (limulus polyphemus)), CLEC2B, HGD (homogentisate 1,2-dioxygenase (homogentisate oxidase)) and YARS (tyrosyl-tRNAsynthetase) are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. In pathway enrichment analysis for down regulated genes were carried out. FCER1G was associated with chronic inflammation in kidney cancer [121], but this gene may be important for pathogenesis of WT. Enriched genes such as COL1A1 [122] STIM1 [123], MFAP2 [124], EFEMP1 [125], MMP11 [126], VCAM1 [127], COL2A1 [128], COL4A2 [129], COL13A1 [130], FMOD (fibromodulin) [131], ITGA8 [132], CAPN6 [133], MGP (matrix Gla protein) [134], SPON1 [135], FGF7 [136], PLXNA2 [137], CXCL12 [138], PTN (pleiotrophin (heparin binding growth factor 8, neurite growth-promoting factor 1)) [139], FNDC1 [140], SERPING1 [141], PCSK6 [142], TCF7L2 [143] and TLE4 [144] were associated with invasion of various cancer cells types, but these genes may be liable for invasion of WT cells. Methylation inactivation of enriched tumor suppressor genes such as ADCY4 [145], FBLN1 [146], FBN2 [147], ADAMTS9 [148], NELL2 [149] and PCDH18 [150] were important for progression of various cancers such as breast cancer, colorectal cancer, nasopharyngeal cancer and kidney cancer, but loss of these genes may be linked with development of WT. Enriched genes such as VWF (Von Willebrand factor) [151] and ACVR2B [152] were identified with progression of WT. ITPR1 was linked with activation of autophagy in kidney cancer [153], but this gene may be responsible for induction of autophagy in WT. SYK (spleen tyrosine kinase) was liable for cancer drug resistance in ovarian cancer [154], but this gene may be involved in chemo resistance in WT. Enriched polymorphic genes such as MMP7 [155] and C1QA [156] were answerable for progression of various cancer types, but these polymorphic genes may be important for advancement of WT. Enriched genes such as S100A9 [157], SCUBE3 [158], PDGFC (platelet derived growth factor C) [159] and CTBP2 [160] were linked with development of various cancer types, but elevated expression of these genes may be responsible for progression of WT. Low expression of genes such as SPARCL1 [161], SEMA3F [162], PCDH9 [163], CDH11 [164] and NR3C2 [165] were liable for development various cancer types, but decrease expression of these genes may be associated with advancement of WT. PLXDC1 was identified with angiogenesis in ovarian cancer [166], but this gene may be associated with angiogenesis in WT. Our study found that ITPK1, ADCY2, MYLK (myosin, light polypeptide kinase), EDNRA (endothelin receptor type A), COL14A1, FBN3, ASPN (asporin (LRR class 1)), NCAM1, KLKB1, TLL1, F13A1, FGF14, C1QTNF7, PAPPA2, FRAS1, CILP (cartilage intermediate layer protein, nucleotide pyrophosphohydrolase), NRG3, FREM1, FGL2, BMPER (BMP binding endothelial regulator), RSPO3, PLXDC2, TNFSF8, DCHS1, MYCN (V-mycmyelocytomatosis viral related oncogene, neuroblastoma derived (avian)), PCDHB14, CDH5, ACVR2A, KCNJ8 and ABCC9 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. GO enrichment analysis for up regulated genes were carried out. High expression of enriched genes such as PDK1 [167], GCLM (glutamate-cysteine ligase, modifier subunit) [168], SPHK1 [169], NQO1 [170], CBS (cystathionine-beta-synthase) [171], P4HB [172], PCK2 [173], ADA (adenosine deaminase) [174], ADM (adrenomedullin) [175], AGR2 [176], ULBP2 [177], PTPRZ1 [178], LDLR (low density lipoprotein receptor (familial hypercholesterolemia)) [179], CASK (calcium/calmodulin-dependent serine protein kinase (MAGUK family)) [180], KRT17 [181], TRAP1 [182] and EMP2 [183] were liable for progression of various cancer types, but increase expression of these genes may be important for pathogenesis of WT. Enriched genes such as ACSL4 [184], TYRP1 [185], PPA1 [186], PSMB8 [187], STAT5B [188], ENPP1 [189], CLIC1 [190], STC2 [191], BCHE (butyrylcholinesterase) [192], APOM (apolipoprotein M) [193], HMGA1 [194], ICAM3 [195], DNER (delta-notch-like EGF repeat-containing transmembrane) [196] and CAV2 [197] were linked with invasion of various cancer cells types, but these genes may be liable for invasion of WT cells. Enriched genes such as FBP1 [198], INSIG1 [199], IER3 [200], GLDC (glycine dehydrogenase (decarboxylating)) [201], KYNU (kynureninase (L-kynurenine hydrolase)) [202], PLA2G2A [203], DKK1 [204], AZGP1 [205], WFDC1 [206] and SOCS2 [207] were answerable for pathogenesis of various cancer types, but low expression of these genes may be associated with development of WT. Enriched polymorphic genes such as COMT (catechol-O-methyltransferase) [208], PTGS2 [209], UTS2 [210], TMBIM1 [211], TAP2 [212] and EFNA1 [213] were culpable for progression of various cancer types, but these polymorphic genes may be linked with development of WT. Enriched genes such as SLC27A2 [214] and KRT10 [215] were important for drug resistance in ovarian cancer, but these genes may be liable for chemo resistance in WT. Methylation inactivation of tumor suppressor CDH13 was associated with progression of breast cancer [216], but inactivation of this gene may be important for advancement of WT. Our study found that CDH13, CEL (carboxyl ester lipase (bile salt-stimulated lipase)), MAT1A, ETFB (electron-transfer-flavoprotein, beta polypeptide), TYR (tyrosinase (oculocutaneous albinism IA)), MID1IP1, SCD (stearoyl-CoA desaturase (delta-9-desaturase)), PYCR2, PLP1, WARS (tryptophanyl-tRNAsynthetase), ERO1A, SLC7A2, DECR2, ALDH4A1, CTPS1, GALE (UDP-galactose-4-epimerase), CYP8B1, DCT (dopachrometautomerase (dopachrome delta-isomerase, tyrosine-related protein 2)), PSMD6, RGN (regucalcin (senescence marker protein-30)), OTC (ornithine carbamoyltransferase), MECR (mitochondrial trans-2-enoyl-CoA reductase), EHHADH (enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase), UGT2A3, PCCB (propionyl coenzyme A carboxylase, beta polypeptide), NRN1, HIST1H2BG, HIST1H2BK, NLGN1, HAMP (hepcidin antimicrobial peptide), KLHL17, HIST1H2BJ, LCP1, KLRK1, IL1RAP, GABRA5, BCAS3, CD3G and TSPAN4 are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Similarly, GO enrichment analysis for down regulated genes was carried out. Enriched genes such as MEOX2 [217], SIX1 [218] and RARA (retinoic acid receptor, alpha) [219] were identified with development of WT. Methylation inactivation of enriched tumor suppressor genes such as HOXA5 [220], SALL2 [221], TCF21 [222], PAX1 [223], ZNF516 [224], EBF3 [225] and ZNF677 [226] were liable for advancement of various cancer types, but loss of these genes may be important for pathogenesis of WT. Low expression of enriched genes such as HOXB6 [227], TSHZ3 [228], SIN3A [229], CAMTA1 [230], TCF4 [231] and MEIS2 [232] were linked with progression of various cancer types, but decrease expression of these genes may be responsible for advancement of WT. Enriched genes such as HOXC10 [233], RECK (reversion-inducing-cysteine-rich protein with kazal motifs) [234], NRP1 [235], STOX2 [236], NR2F2 [237], GPR161 [238], PBX1 [239], KLF7 [240], TCF12 [241], TFAP4 [242], KLF12 [243] and ZBTB20 [244] were linked with invasion of various cancer cells types, but these genes may be responsible for invasion of WT cells. Enriched genes such as FGFR2 [245], RARB (retinoic acid receptor, beta) [246], EFNB1 [247], ABCG1 [248], AUTS2 [249], MLLT11 [250], GLIS3 [251], HIF3A [252], ELF1 [253], STAG1 [254], BCL11A [255] and TSC22D1 [256] were important for pathogenesis of various cancer types, but these genes may be linked with progression of WT. CNOT2 was linked with angiogenesis in breast cancer [257], but this gene may be associated with angiogenesis in WT. AFF3 was important for drug resistance in breast cancer [258], but this gene may be involved with chemo resistance in WT. Our study found that PDGFRB (platelet-derived growth factor receptor, beta polypeptide), KIDINS220, CLIC5, PGAP1, FLRT3, SLC8A1, ENAH (enabled homolog (Drosophila)), SMO (smoothened homolog (Drosophila)), STOX1, NRK (nik related kinase), MAFB (V-mafmusculoaponeuroticfibrosarcoma oncogene homolog B (avian)), RB1, NR2F1, MED25, ZNF211, ZNF605, ZNF420, ZNF135, ZNF300, ZNF501 and ZNF532 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. PPI network was constructed and analyzed for up regulated genes. AURKA was important for pathogenesis WT [259]. SMURF1 was responsible for invasion of breast cancer cells [260], but this gene may be liable for invasion of WT cells. NUDT21 was involved in proliferation of glioblastoma cells [261], but this gene may be associated with proliferation WT cells. Our study found that NANOG (nanoghomeobox), SLC25A5 and KCNQ3 are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Similarly, PPI network was constructed and analyzed for down regulated genes. PLK1 was associated with proliferation of kidney cancer cells [262], but this gene may be liable for proliferation of WT cells. Low expression of MAGI1 was linked with progression of kidney cancer [263], but decrease expression of this gene may be responsible for pathogenesis of WT. Our study found that DDIT4L and MRPL15 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Module analysis was performed for up regulated genes. Genes such as IGF2BP1 [264] and PIR (Pirin (iron-binding nuclear protein)) [265] were linked with invasion of various cancer cells types, bur these genes may be involved in invasion of WT cells. Over expression of CCND1 was involved in pathogenesis of breast cancer [266], but high expression of this gene may be linked with progression of WT. Our study found that APRT, HBZ, EIF2S1, CUL7 and TKT are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Similarly, module analysis was performed for down regulated genes. FANCC (fanconianemia, complementation group C) was important for advancement of WT [267]. Target gene ‐ miRNA network was constructed and analyzed for up regulated genes. PTP4A1 was important for invasion of breast cancer cells [268], but this gene may be linked with invasion of WT cells. High expression RRM2 of was involved in advancement of cervical cancer [269], but elevated expression of this gene may be associated with development of WT. Similarly, target gene ‐ miRNA network was constructed and analyzed for down regulated genes. ZNF703 was liable for invasion of colorectal cancer cells [270], but this gene may be responsible for invasion of WT cells. Target gene ‐ TF network was constructed and analyzed for up regulated genes. MAGEC2 was linked with invasion of breast cancer cells [271], but this gene may be involved in invasion of WT cells. Similarly, target gene ‐ TF network was constructed and analyzed for down regulated genes. Methylation inactivation of tumor suppressor PLEKHO1 was responsible for advancement of gastric cancer [272], but loss of this gene may be important for pathogenesis of WT. Our study found that CACHD1 and CASD1 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. In the current investigation, the DEGs between WT and normal tissue samples in the GSE60850 dataset were determined, and the up and down regulated hub genes among the DEGs were demonstrated to be associated with the prognosis and diagonsis of patients with WT. Furthermore, FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were identified as possible candidate biomarkers for patients with WT. High FN1, AURKA, TRIM41, NFKBIA, TXNDC5 mRNA expression levels and low SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 mRNA expression levels were validated by TCGA database, human protein atlas database and subsequent ROC analysis and RT‑qPCR analysis, which may preliminarily discover the pathophysiological role of these hub genes in WT at the molecular level. In conclusion, 988 DEGs and 10 hub genes were identified as potential diagnostic or prognostic biomarkers of WT. The current investigation identified several genes which had not been already associated with WT and implemented evidence that these genes were associated with this disease. Encourage examines are recommended to authenticate these results and to more precisely analyze the associations between these genes and WT. Overall, the current investigation highlights possibly new targets for more individualized treatment of patients with WT. Declarations Acknowledgement I thank Richard Dafydd Williams, UCL Institute of Child Health, Developmental Biology and Cancer, 30 Guilford Street, London, United Kingdom, very much, the author who deposited their microarray dataset, GSE60850, into the public GEO database. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent No informed consent because this study does not contain human or animals participants. Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE60850) ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE60850 )] Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author Contributions B. V. - Writing original draft, and review and editing C. V. - Software and investigation I. K. - Supervision and resources Authors Basavaraj Vastrad ORCID ID: 0000-0003-2202-7637 Chanabasayya Vastrad ORCID ID: 0000-0003-3615-4450 Iranna Kotturshetti ORCID ID: 0000-0003-1988-7345 References Ford K, Gunawardana S, Manirambona E Philipoh GS Mukama B Kanyamuhunga A Cartledge P, Nyoni MJ, Mwaipaya D, Mpwaga J et al. Investigating Wilms' Tumours Worldwide: A Report of the OxPLORE Collaboration-A Cross-Sectional Observational Study. World J Surg. 2019. doi:1007/s00268-019-05213-6 Rivera MN, Haber DA. Wilms' tumour: connecting tumorigenesis and organ development in the kidney. 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Pirin: a potential novel therapeutic target for castration‐resistant prostate cancer regulated by miR‐455‐ Molecular oncology. 2019;13(2):322-337. doi:10.1002/1878-0261.12405 Elsheikh S, Green AR, Aleskandarany MA, Grainge M, Paish CE, Lambros MB, Reis-Filho JS, Ellis IO. CCND1 amplification and cyclin D1 expression in breast cancer and their relation with proteomic subgroups and patient outcome. Breast cancer research and treatment. 2008;109(2):325-335. doi:1007/s10549-007-9659-8 Bissig H, Staehelin F, Tolnay M, Avoledo P, Richter J, Betts D, Bruder E, Kühne T. Co-occurrence of neuroblastoma and nephroblastoma in an infant with Fanconi's anemia. Human pathology. 2002;33(10):1047-1051. doi:1053/hupa.2002.128062 Flores-Pérez A, Marchat LA, Rodríguez-Cuevas S, Bautista VP, Fuentes-Mera L, Romero-Zamora D, Maciel-Dominguez A, de la Cruz OH, Fonseca-Sánchez M, Ruíz-García E et al. Suppression of cell migration is promoted by miR-944 through targeting of SIAH1 and PTP4A1 in breast cancer cells. BMC cancer. 2016;16(1):379. doi:1186/s12885-016-2470-3 Wang N, Zhan T, Ke T, Huang X, Ke D, Wang Q, Li H. Increased expression of RRM2 by human papillomavirus E7 oncoprotein promotes angiogenesis in cervical cancer. British journal of cancer. 2014;110(4):1034-1044. doi:1038/bjc.2013.817 Ma F, Bi L, Yang G, Zhang M, Liu C, Zhao Y, Wang Y, Wang J, Bai Y, Zhang Y. ZNF703 promotes tumor cell proliferation and invasion and predicts poor prognosis in patients with colorectal cancer. Oncology reports. 2014;32(3):1071-1077. doi:3892/or.2014.3313 Yang F, Zhou X, Miao X, Zhang T, Hang X, Tie R, Liu N, Tian F, Wang F, Yuan J. MAGEC2, an epithelial-mesenchymal transition inducer, is associated with breast cancer metastasis. Breast cancer research and treatment. 2014;145(1):23-32. doi:1007/s10549-014-2915-9 Ma HW, Xie M, Sun M, Chen TY, Jin RR, Ma TS, Chen QN, Zhang EB, He XZ, De W, et al. The pseudogene derived long noncoding RNA DUXAP8 promotes gastric cancer cell proliferation and migration via epigenetically silencing PLEKHO1 expression. Oncotarget. 2017;8(32):52211-52224. doi:18632/oncotarget.11075 Tables Due to technical limitations, Tables 1-8 are only available as a download in the supplementary files section. Supplementary Files Tables.docx 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-133323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":6860380,"identity":"739ed833-eb12-4f43-82a6-0c06e9d1f495","order_by":0,"name":"Basavaraj Vastrad ","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India.","correspondingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":6860381,"identity":"3b303b3d-c306-42d9-bf59-847849ceea90","order_by":1,"name":"Chanabasayya Vastrad ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCRQemw2QYGw8QKQWZpCWNJCWBpK0HAYz8Wrhn9387HFhG0Nif//5Yx9+lJ23W9t+GGhLjU00TkvuHDM3ngnUMuNGMvPMnnO3k7edSQRqOZaW24BDi4FEgpk0bxuDMcMNZmYG3rbbyWYHgFoYGw7j0ZL+DaxF/vxhZsa/beeSzc4/JKQlB2yLnMGBZGZm3rYDdmY3CNgicSOnTJrnnISc4Y1kY2aZc8kJZjeAtiTg8Qv/jPRt0jxlNjxy5w8+ZnxTZmdvdj794YMPNTY4tcAsg7MSwSoT8CtHBfakKB4Fo2AUjIKRAQDSZlsK1y3XFwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3615-4450","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. ","correspondingAuthor":true,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""},{"id":6860382,"identity":"8b189700-8ba8-42e1-95f0-e081079f6e94","order_by":2,"name":"Iranna Kotturshetti ","email":"","orcid":"https://orcid.org/0000-0003-1988-7345","institution":"Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India.","correspondingAuthor":false,"prefix":"","firstName":"Iranna","middleName":"","lastName":"Kotturshetti","suffix":""}],"badges":[],"createdAt":"2020-12-21 15:11:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-133323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-133323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4431096,"identity":"7cb806dd-9116-460b-bd40-e1da6092e975","added_by":"auto","created_at":"2020-12-22 00:12:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127576,"visible":true,"origin":"","legend":"Box plots of the gene expression data before normalization (A) and after normalization (B). 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(A1 – A36 = normal tissues samples; B1 – B36 = WT tissues samples)","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/0247cc47611baa76691c5b54.png"},{"id":4431206,"identity":"4969e7de-a7f5-45b8-aa98-cc719f470a3a","added_by":"auto","created_at":"2020-12-22 00:21:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170003,"visible":true,"origin":"","legend":"Heat map of down regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. 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Green nodes denotes up regulated genes.","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/be93f8df755c3367b3ecd255.png"},{"id":4431172,"identity":"f75d082c-db40-4f12-a94f-11442f025e57","added_by":"auto","created_at":"2020-12-22 00:15:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153331,"visible":true,"origin":"","legend":"Regression diagrams for up regulated genes. (A- Node degree; B- Betweenness centrality; C- Stress centrality ; D-Closeness centrality; E- Clustering coefficient)","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/f82aeb1e292d5d34a436cba3.png"},{"id":4431186,"identity":"69565ad0-42bb-40ce-a56e-742b9025746b","added_by":"auto","created_at":"2020-12-22 00:18:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":589715,"visible":true,"origin":"","legend":"Protein–protein interaction network of down regulated genes. 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The red circles nodes are the down regulated genes, and blue color diamond nodes are the miRNAs","description":"","filename":"F12.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/8401caa77e1ac321e64f6274.png"},{"id":4431111,"identity":"9d4ea5bd-c300-455b-b27f-40ce7c7428b6","added_by":"auto","created_at":"2020-12-22 00:12:39","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":744313,"visible":true,"origin":"","legend":"The network of up regulated genes and their related TFs. The green circles nodes are the up regulated genes, and blue color triangle nodes are the TFs","description":"","filename":"F13.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/ced67c53609148d1c9592683.png"},{"id":4431188,"identity":"b67a8fb6-07db-4cc1-b2ee-3c3d81b074c8","added_by":"auto","created_at":"2020-12-22 00:18:39","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":790775,"visible":true,"origin":"","legend":"The network of down regulated genes and their related TFs. The green circles nodes are the down regulated genes, and purple color triangle nodes are the TFs","description":"","filename":"F14.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/ed1bd51b77de2951d517cda9.png"},{"id":4431107,"identity":"1a3030ab-8085-4e18-a536-41f62a8647f7","added_by":"auto","created_at":"2020-12-22 00:12:39","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":77129,"visible":true,"origin":"","legend":"Overall survival analysis of hub genes. Overall survival analyses were performed using the UALCAN online platform. 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Red line denotes - high expression; Blue line denotes – low expression A) SIN3A B) MAGI1 C) GPRASP2 D) FXYD6 E) NFKBIA","description":"","filename":"F16.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/2c1e6ff0a35aae29bdcf186b.png"},{"id":4431183,"identity":"a39d4b7f-d673-443d-8ff7-fb376283e925","added_by":"auto","created_at":"2020-12-22 00:18:38","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":62649,"visible":true,"origin":"","legend":"Box plots (expression analysis) hub genes were produced using the UALCAN platform. A) UCHL1 B) FN1 C) AURKA D) TRIM41 E) TXNDC5 F) SIN3A G) MAGI1 H) GPRASP2 I) FXYD6 J) NFKBIA","description":"","filename":"F17.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/fad8d0a1dfb255d741e99dd0.png"},{"id":4431170,"identity":"1cf39759-7a1a-47c8-b40b-2f59e8c0afbd","added_by":"auto","created_at":"2020-12-22 00:15:38","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":83850,"visible":true,"origin":"","legend":"Box plots (stage analysis) of hub genes were produced using the UALCAN platform. 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A) UCHL1 B) FN1 C) AURKA D) TRIM41 E) TXNDC5 F) SIN3A G) MAGI1 H) GPRASP2 I) FXYD6 J) NFKBIA","description":"","filename":"F20.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/41234c27afcd27efb1da5940.png"},{"id":4431179,"identity":"04f863d5-ae6f-47c2-8256-6734f90a4210","added_by":"auto","created_at":"2020-12-22 00:15:39","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":59704,"visible":true,"origin":"","legend":"ROC curve validated the sensitivity, specificity of hub genes as a predictive biomarker for WT prognosis. A) UCHL1 B) FN1 C) AURKA D) TRIM41 E) TXNDC5 F) SIN3A G) MAGI1 H) GPRASP2 I) FXYD6 J) NFKBIA","description":"","filename":"F21.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/7f633587bbdc0c6432cd54ed.png"},{"id":4431118,"identity":"629d56cc-49a8-4701-8db3-c50e8ae96043","added_by":"auto","created_at":"2020-12-22 00:12:39","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":97436,"visible":true,"origin":"","legend":"Validation of hub genes (up and down regulated) by RT- PCR. A) UCHL1 B) FN1 C) AURKA D) TRIM41 E) TXNDC5 F) SIN3A G) MAGI1 H) GPRASP2 I) FXYD6 J) NFKBIA","description":"","filename":"F22.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/ec89861c8d1a448024ec0e32.png"},{"id":4431112,"identity":"f825b250-03a3-452c-97cf-48504844c7d7","added_by":"auto","created_at":"2020-12-22 00:12:39","extension":"png","order_by":23,"title":"Figure 23","display":"","copyAsset":false,"role":"figure","size":389261,"visible":true,"origin":"","legend":"Scatter plot for immune infiltration for hub genes (up and down regulated). A) UCHL1 B) FN1 C) AURKA D) TRIM41 E) TXNDC5 F) SIN3A G) MAGI1 H) GPRASP2 I) FXYD6 J) NFKBIA","description":"","filename":"F23.png","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/c9bea278b0be6ff5524faa70.png"},{"id":13638892,"identity":"c575634e-d800-4602-92a3-a3843fe0ac8a","added_by":"auto","created_at":"2021-09-17 08:52:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6223942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/6eb52315-97a6-44f4-832b-43de13015ee5.pdf"},{"id":4431101,"identity":"ebdf0fb6-4b73-4d46-b719-4d3c1e0500dc","added_by":"auto","created_at":"2020-12-22 00:12:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":427536,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-133323/v1/3dc1caa7b613b26edf1847f1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHub Genes and Key Pathway Identification in Wilms Tumor Based on Bioinformatics Analysis\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWilms tumor (WT) is the rare diagnosed pediatric tumor worldwide and is named as nephroblastoma [1]. WT is form of kidney cancer that mostly advances in children under age under 10 years [2]. Because of routine early screening and recent advances in treatment techniques, long-term survival rates have upgraded [3]. However, in developing countries, most WT patients are diagnosed at an end stage, with poor prognosis [4]. Therefore, further studies should still be emphasized for the early diagnoses, prognosis and targeted therapy of WT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic aberrations \u0026nbsp;and its related pathways have been reported to be significant factors contributing to the progression of WT. Genes \u0026nbsp;such as\u0026nbsp; IGF2 [5], WT1 [6], RASSF1A [7], PAF1 [8], and DROSHA and DICER1 [9] as well as signaling pathways such as WNT/\u0026beta;‐catenin pathway [10], IGF signaling pathway [11], \u0026nbsp;S1P/S1P1 signaling pathway [12], PTEN/PI3K/AKT signaling pathway [13] and VEGF‐C/VEGFR‐2 signaling pathway [14] were responsible for pathogenesis of WT.\u0026nbsp; Despite improvement and progress in WT diagnosis, prognosis and treatment, the underlying WT molecular mechanisms are not entirely clear and novel diagnosis, prognosis and treatment options are still needed for more effective control of WT development.\u003c/p\u003e\n\u003cp\u003eGene expression profile analysis is a high-throughput method for detecting messenger RNA expression in various cancer tissues or cell samples. By analyzing the different gene expression between cancer patients and normal controls, an improved understanding of the molecular mechanism of a various tumors can be obtained, facilitating the identification of the potential key genes and pathways for diagnostics markers, prognostics markers and targeted therapy [15-16].\u003c/p\u003e\n\u003cp\u003eThe current study aimed to explore the molecular pathogenesis of WT by a computational bioinformatics analysis of gene expression. Gene expression data from the Gene Expression Omnibus (GEO) database was extracted, and differentially expressed genes (DEGs) between WT and normal samples were identified. The possible functions of the DEGs were predicted using pathway and gene ontology (GO) enrichment analysis. Furthermore, protein-protein interaction (PPI) networks were constructed using mentha PPI database, and visualized and module analysis was conducted using Cytoscape software to search for essential hub genes that may be associated in the progression of WT. Dysregulation of microRNAs (miRNAs) and transcription factors (TFs) have been indicated to be associated with the pathogenesis of WT, the WT specific regulatory networks of target gene and miRNA, and target gene and TFs were constructed. Validation of the hub genes was performed to screen genes with prognostic and diagnostics significance in WT.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eMicroarray data\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eHuman gene expression microarray data of WT samples (n = 36) and normal samples (n = 36) were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) with an accession ID of GSE60850. The platform of GSE60850 is GPL19130 Breakthrough Human 17K 2.1.2.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eData preprocessing\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe raw data in GSE60850 were preprocessed using limma [17], an R software package and it implemented background correcting, quantile normalization and expression calculation automatically. Then, probe values were translated to gene-symbol values based on message associated in microarray platform, and probes without proper gene-symbols were excluded.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eDifferentially Expressed Genes\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eBased on the gene expression microarray data, DEGs between WT samples and normal samples were identified using limma [17], an R software package. The corresponding p-values were calculated using t-test provided by limma. The genes met the criteria of p-value\u0026lt;0.05 and |log2 fold change (FC)|\u0026ge;1.22 for up regulated genes and |log2 fold change (FC)|\u0026ge; -1.39 for down regulated genes were defined as significant DEGs between the two groups.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePathway enrichment analysis of DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eBIOCYC \u0026nbsp;(\u003ca style=\"color: #000000;\" href=\"https://biocyc.org/\"\u003ehttps://biocyc.org/\u003c/a\u003e) [18], Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) [19], Pathway Interaction Database (PID, http://pid.nci.nih.gov/) [20], Reactome (\u003ca style=\"color: #000000;\" href=\"https://reactome.org/PathwayBrowser/\"\u003ehttps://reactome.org/PathwayBrowser/\u003c/a\u003e) [21], Molecular signatures database (MSigDB, \u003ca style=\"color: #000000;\" href=\"http://software.broadinstitute.org/gsea/msigdb/\"\u003ehttp://software.broadinstitute.org/gsea/msigdb/\u003c/a\u003e) [22],\u0026nbsp; GenMAPP (http://www.genmapp.org/) [23], Pathway Ontology (https://bioportal.bioontology.org/ontologies/PW) [24] and PantherDB (http://www.pantherdb.org/) [25] \u0026nbsp;database are used to understand the high-level functions and utilities of the biological system. ToppGene (ToppFun)\u0026nbsp; (\u003ca style=\"color: #000000;\" href=\"https://toppgene.cchmc.org/enrichment.jsp\"\u003ehttps://toppgene.cchmc.org/enrichment.jsp\u003c/a\u003e) [26] is a comprehensive set of functional annotation tools for researchers to understand biological meaning behind large scale of genes. P \u0026lt; 0.05 was set as the cut-off criterion.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eGO enrichment analysis of DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eGO (http://www.geneontology.org/) [27] enrichment analysis is a universal genes analysis method, which can contribute functional classification for genomic data, including categories of BPs, cellular component (CC), and molecular function (MF).\u0026nbsp; ToppGene (ToppFun)\u0026nbsp; (\u003ca style=\"color: #000000;\" href=\"https://toppgene.cchmc.org/enrichment.jsp\"\u003ehttps://toppgene.cchmc.org/enrichment.jsp\u003c/a\u003e) [26] is an online tool for gene functional classification, which can systematic and integrative analysis of large gene lists. In this study, to analyze the functions of DEGs, GO enrichment analysis were conducted using the ToppGene online tool; p \u0026lt; 0.05 was set as the cutoff point.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePPI network construction and module analysis\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eMentha (\u003ca style=\"color: #000000;\" href=\"https://mentha.uniroma2.it/index.php\"\u003ehttps://mentha.uniroma2.it/index.php\u003c/a\u003e) [28] was an online biological tool which had a major role in the analysis of biological information and integrates different PPI database such as IntAct (https://www.ebi.ac.uk/intact/) [29], MINT (\u003ca style=\"color: #000000;\" href=\"https://mint.bio.uniroma2.it/\"\u003ehttps://mint.bio.uniroma2.it/\u003c/a\u003e) [30],\u0026nbsp; BioGRID (https://thebiogrid.org/) [31], DIP (\u003ca style=\"color: #000000;\" href=\"http://dip.doe-mbi.ucla.edu/dip/Main.cgi\"\u003ehttp://dip.doe-mbi.ucla.edu/dip/Main.cgi\u003c/a\u003e)\u0026nbsp; [32] and MatrixDB (\u003ca style=\"color: #000000;\" href=\"http://matrixdb.univ-lyon1.fr/\"\u003ehttp://matrixdb.univ-lyon1.fr/\u003c/a\u003e) [33]. As a result, based on the STRING database, a protein\u0026ndash;protein interaction (PPI) network of WT was built. PPIs of DEGs (up and down regulated genes) were selected with a combination score \u0026gt;0.9. Subsequently, the PPI network was input into Cytoscape (http://www.cytoscape.org/) (version: 3.7.2) [34]. Five topological methods (node degree, betweenness centrality, stress centrality,\u0026nbsp; closeness centrality and clustering coefficient ) using to rank and evaluated hub genes using network analyzer [35-39] and modules analysis were taken using PEWCC1\u0026nbsp; of Cytoscape plugin [40].\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target gene \u0026nbsp;- miRNA regulatory network\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe target genes - miRNA interactions were predicted with NetworkAnalyst (https://www.networkanalyst.ca/) [41], which involves two miRNA databases such as\u0026nbsp; DIANA-TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) [42] and miRTarBase (\u003ca style=\"color: #000000;\" href=\"http://mirtarbase.mbc.nctu.edu.tw/php/download.php\"\u003ehttp://mirtarbase.mbc.nctu.edu.tw/php/download.php\u003c/a\u003e) [43]. Subsequently, the target genes - miRNA regulatory network was input into Cytoscape\u0026nbsp; (version: 3.7.2) [34].\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target gene \u0026nbsp;- TF regulatory network\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eExperimentally-validated\u0026nbsp; target genes and their TFs were screened in one TF database ChEA database (http://amp.pharm.mssm.edu/lib/chea.jsp) [44]. \u0026nbsp;TFs that have a regulatory relationship with the target genes in the\u0026nbsp; constructed\u0026nbsp; network\u0026nbsp; were identified. The NetworkAnalyst (https://www.networkanalyst.ca/) [41] online tool \u0026nbsp;was used to predict TF-regulating genes in the network. Cytoscape (version: 3.7.2) [34], an open-source platform for visualizing complex networks, was used to visualize target genes - TF regulatory network.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eValidation of hub genes\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe survival probability study was implemented using Kaplan-Meier method to compare overall survival curves between high \u0026nbsp;and low expression gene groups UALCAN (https://ualcan.path.uab.edu/index.html) online dataset [45], which is a user-friendly, interactive web resource for the analysis of cancer transcriptome data.. P\u0026lt;0.05 was considered to indicate a statistically significant difference.\u0026nbsp; The expression analysis and stage analysis of hub genes were analyzed using UALCAN online dataset [45].\u0026nbsp; The mutation frequencies of up and down hub genes were inquired in cBioportal online database (http://www.cbioportal.org/) [46]. In addition, up and down regulated hub genes were further validated for their prognostic values (immunohistochemical (IHC) analysis in normal and cancer tissue) using The Cancer Genome Atlas database (\u003ca style=\"color: #000000;\" href=\"https://www.proteinatlas.org/\"\u003ehttps://www.proteinatlas.org/\u003c/a\u003e) [47]. Receiver operating characteristic (ROC) analyses are generally used to check out the conduct of disease diagnosis and prognosis. The area under the curve (AUC) was used to demonstrate the accuracy of an individual gene for predicting recurrence using R package\u0026ldquo;pROC\u0026rdquo; [48]. Reverse transcription polymerase chain\u0026nbsp; reaction (RT-PCR) was carried out for validation of up and down regulated hub genes. Total RNA was extracted from the WT tissue sample and normal kidney tissue samples using TRI Reagent\u0026reg; (Sigma, USA) according to the manufacturer's protocol.\u0026nbsp; A\u0026nbsp; RNA was reverse transcribed into cDNA using FastQuant RT kit (with gDNase; Tiangen Biotech Co., Ltd.), according to the manufacturer's protocol. The primer sequences (Genewiz, Inc.) used for RT-PCR are listed in Table 1. The mRNA expression levels of hub genes were measured by\u0026nbsp; Real time-PCR using the QuantStudio 7 Flex real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA) . The following reaction conditions were used for RT-PCR: Initial denaturation at 95˚C for 3 min followed by 40 cycles of denaturation at 95˚C for 10 sec and annealing and elongation at 60˚C for 30 sec. The relative expression levels of up and down regulated hub genes were determined using the 2 \u003csup\u003e-\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method [49] and normalized to the internal reference gene, \u0026beta;-actin. Immune infiltration analysis was performed using the TIMER (https://cistrome.shinyapps.io/timer/) [50] is a RNA-Seq expression profiling database from The Cancer Genome Atlas (TCGA) portal for up and down regulated hub genes, which is used to check the immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) across WT.\u003c/span\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter data, including 36 WT samples and 36 normal samples, was downloaded from GEO database and preprocessed. The results before and after normalization are shown Fig. 1A and Fig. 1B. 988 DEGs, including 486 up genes and 502 down genes were identified using limma packages on the basis of the cut off criteria (P\u0026lt;0.05 and |log2 fold change (FC)| \u0026gt; 1.39 for up regulated genes and |log2 fold change (FC)| \u0026lt; -1.22 for down regulated genes) in WT samples compared with normal samples (Table 1). The volcano plot showed the up regulated and down regulated genes in dataset GSE60850 is shown in Fig. 2. The details of up and down regulated gene expression heat map are shown in Fig. 3 and Fig. 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway enrichment analysis of the DEGs (up and down regulated genes) was performed using ToppGene. Pathways were identified for the up regulated genes, including the cholesterol biosynthesis III (via desmosterol), superpathway of methionine degradation, complement and coagulation cascades, ECM-receptor interaction, FOXA1 transcription factor network, direct p53 effectors, hemostasis, extracellular matrix organization, phenylalanine tyrosine and tryptophan biosynthesis, tyrosine metabolism, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, genes encoding enzymes and their regulators involved in the remodeling of the extracellular matrix, plasminogen activating cascade, blood coagulation, altered lipoprotein metabolic, gluconeogenesis pathway, phenylalanine and tyrosine metabolism.\u0026nbsp; Similarly, pathways were identified for the up regulated genes including the D-myo-inositol (3,4,5,6)-tetrakisphosphate biosynthesis, 1D-myo-inositol hexakisphosphate biosynthesis V (from Ins(1,3,4)P3), platelet activation, protein digestion and absorption, endothelins, alpha-synucleinsignaling, extracellular matrix organization, degradation of the extracellular matrix, MAP kinase kinase activity, glycolysis, gluconeogenesis, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, ensemble of genes encoding core extracellular matrix including ECM glycoproteins, collagens and proteoglycans, Wnt signaling pathway, integrin signalling pathway, activinsignalin, parkinson disease, quinapril pathway and diltiazem pathway. \u0026nbsp;The detailed results of pathway enrichment analysis for up and down regulated genes are presented in Table 2 and Table 3. \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO enrichment analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll up and down regulated genes were uploaded to the ToppGene software to identify GO function.\u0026nbsp; GO enrichment analysis results for up and down regulated genes are presented in Table 4 and Table 5. For biological processes (BP), the top GO terms of up and down regulated genes were significantly enriched in carboxylic acid metabolic process, oxoacid metabolic process, embryo development and animal organ morphogenesis, were included. For cell component (CC), top GO terms of up and down regulated genes\u0026nbsp; were significantly enriched in cell surface, endoplasmic reticulum, neuron projection and neuron part. For molecular function (MF), the top GO terms of up and down regulated genes were significantly enriched in \u0026nbsp;signaling receptor binding, identical protein binding, DNA-binding transcription factor activity \u0026nbsp;and calcium ion binding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and module analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mentha PPI database was used to construct PPI networks. The PPI network of the up regulated genes is illustrated in Fig. 5 with 7649 nodes and 17236 edges. The topology analysis (higest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustring coefficient) for up regulated genes showed that ESR1, FN1, AURKA, SMURF1, PDK1, NANOG, SLC25A5, NUDT21,\u0026nbsp; KCNQ3, ADM, CEL, CXCL3\u0026nbsp; and GABRA5 were the hub genes (Table. 6) and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are shown in Fig. 6A - 6E. \u0026nbsp;These identified hug genes were enriched in neuron part, ECM-receptor interaction, metabolism of proteins, negative regulation of response to stimulus, carboxylic acid metabolic process, response to oxygen-containing compound, programmed cell death, identical protein binding, cell surface, signaling receptor binding, metabolic pathways, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, and regulation of response to stress. Similarly, PPI network of the down regulated genes is illustrated in Fig. 7 with 7691 nodes and 16050 edges. The topology analysis (higest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustring coefficient) for down regulated genes showed that\u0026nbsp; VCAM1, DDIT4L, TCF4, PLK1, RB1, MEOX2, SYK, PLXDC1, TCF7L2, MAPK10, MAGI1 and MRPL15 were the hub genes (Table. 6) and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are shown in Fig. 8A - 8E.\u0026nbsp; These identified hug genes were enriched in cell adhesion molecules (CAMs), regulation of Wnt-mediated beta catenin signaling and target gene transcription, FoxO family signaling, regulation of retinoblastoma protein, embryo development, animal organ morphogenesis, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, pathways in cancer, innate immune system and ATP binding.\u003c/p\u003e\n\u003cp\u003eBased on the hub genes (up and down regulated) from the PPI module, pathway and GO terms for further analysis. We chose the four most significant modules (up regulated genes) for further analysis (Fig.9). Module 14 consisted of 174 nodes and 311 edges, module 24 consisted of 131 nodes and 144 edges, module 39 consisted of 113 nodes and 111 edges, and module 40 consisted of 97 nodes and 99 edges. Hub genes in these PPI modules were mainly enriched in the ECM-receptor interaction, metabolism of proteins, negative regulation of response to stimulus, programmed cell death, response to endogenous stimulus, neuron part, protein-containing complex binding, focal adhesion, proteoglycans in cancer, regulation of cell differentiation, signaling receptor binding, enzyme regulator activity, carboxylic acid metabolic process, regulation of response to stress, metabolism of amino acids and derivatives, metabolism of lipids and lipoproteins, cell motility and enzyme binding. Finally, we chose the four most significant modules (down regulated genes) for further analysis (Fig.10). Module 17 consisted of 145 nodes and 186 edges, module 24 consisted of 122 nodes and 188 edges, module 34 consisted of 100 nodes and 117 edges, and module 40 consisted of 93 nodes and 95 edges. Hub genes in these PPI modules were mainly enriched in the pathways including DNA-binding transcription factor activity, transcription regulatory region DNA binding, sequence-specific DNA binding, pathways in cancer, extracellular matrix organization, hemostasis, innate immune system, PDGFR-beta signaling pathway, cytokine signaling in immune system, signaling receptor binding, molecular function regulator, Wnt signaling pathway, embryo development, neurogenesis, regulation of cell differentiation, positive regulation of multicellular organismal process and cell surface.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of target gene - miRNA regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the interaction information of target genes and miRNAs in corresponding miRNA databases, the integrated regulatory network of target genes (up and down regulated) and relevant miRNAs were constructed (Fig. 11 and Fig. 12). We found that up regulated target genes such as\u0026nbsp; CCND1 can be targeted by 197 miRNAs (ex, hsa-mir-2392), SCD\u0026nbsp; can be targeted by 167 miRNAs (ex, hsa-mir-1269a), PTP4A1\u0026nbsp; can be targeted by 132 miRNAs (ex, hsa-mir-6731-5p), LDLR\u0026nbsp; can be targeted by 123 miRNAs (ex, hsa-mir-4295) and\u0026nbsp; RRM2\u0026nbsp; can be targeted by 102\u0026nbsp; miRNAs (ex, hsa-mir-4458) are listed in Table 7. These identified target genes were enriched in focal adhesion,\u0026nbsp; PPAR signaling pathway, cell motility, organic substance catabolic process and superpathway of purine nucleotide salvage. Similarly,\u0026nbsp; we found that down regulated target genes such as\u0026nbsp; ZNF703 can be targeted by 115 miRNAs (ex, hsa-mir-3938), ENPP5 can be targeted by 114 miRNAs (ex, hsa-mir-4768-3p), MYLIP can be targeted by 113 miRNAs (ex, hsa-mir-552-5p), ENAH can be targeted by 92 miRNAs (ex, hsa-mir-4282) and\u0026nbsp; ZBTB20 can be targeted by 85 miRNAs (ex, hsa-mir-4282) are listed in Table 7. These identified target genes were enriched in regulation of multicellular organismal development, integral component of plasma membrane, adaptive immune system, axon guidance and positive regulation of multicellular organismal process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of target gene - TF regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the interaction information of\u0026nbsp; target genes and TFs in corresponding\u0026nbsp; TF database, the integrated regulatory network of target genes (up and down regulated) and relevant TFs were constructed (Fig. 13 and Fig. 14). We found that up regulated target genes such as\u0026nbsp; MAGEC2 can be targeted by 207 TFs (ex, SOX2), TSPAN7 can be targeted by 173 TFs (ex, MYC), ESR1 can be targeted by 172 TFs (ex, HNF4A), PCSK6 can be targeted by 166 TFs (ex, EGR1) and LDLR can be targeted by 145 TFs (ex, TP63) are listed in Table 8. \u0026nbsp;These identified target genes were enriched in organic substance catabolic process, intrinsic component of plasma membrane, neuron part, golgi apparatus and identical protein binding.Similarly, we found that down regulated target genes such as PLEKHO1 can be targeted by 184 TFs (ex, SOX2), CACHD1 can be targeted by 151\u0026nbsp; TFs (ex, AR), CASD1 can be targeted by 139 TFs (ex, NANOG), GLIS3 can be targeted by 132 TFs (ex, STAT3) and\u0026nbsp; AFF3 can be targeted by 130 TFs (ex, TP53) are listed in Table 8. These identified target genes were enriched in cell projection part, regulation of transcription by RNA polymerase II and DNA-binding transcription factor activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTe overall survival rates of patients with high expression of UCHL1, FN1, AURKA, TRIM41 and TXNDC5 were all significantly lower than those of patients with low/medium expression (Fig. 15), while overall survival rates of patients with low expression of SIN3A, MAGI1, GPRASP2, FXYD6 and NFKBIA were all significantly lower than those of patients with high expression (Fig. 16). The box plots (expression analysis) showed\u0026nbsp; that the expression levels of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were significantly higher in primary tumor than those in the normal kidney for WT patients from TCGA (Fig. 17A -17E), while the expression levels of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were significantly lower in primary tumor than those in the normal kidney for WT patients from TCGA (Fig. 17F -17J). The box plot suggested (stage analysis) that the high expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 show significant distance in different pathological stages in KT compared to normal (Fig. 18A -18E), while low\u0026nbsp; expression level of\u0026nbsp; SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 show significant distance in different pathological stages in KT compared to normal (Fig. 18F -18J). Up and down regulated hub genes\u0026rsquo; alteration statuses in TCGA WT patients were analyzed using the CbioPortal database. FN1 altered (2%), and missense mutation, truncating mutation, amplification and deep dilation were the main type. AURKA altered (0%). TRIM41 altered (8%), and\u0026nbsp;\u0026nbsp; missense mutation and amplification were the main type. NFKBIA altered (0.3%), and amplification was the main type. TXNDC5 altered (0.7%), and\u0026nbsp; missense mutation and truncating mutation were the main type. SIN3A altered (0.3%), and\u0026nbsp;\u0026nbsp; missense mutation was the main type. MAGI1 altered (2.8%), and\u0026nbsp; inframe mutation, \u0026nbsp;amplification and deep dilation was the main type. GPRASP2 altered (2%), and\u0026nbsp;\u0026nbsp; truncating mutation and amplification were the main type. UCHL1 altered (0%). FXYD6 altered (0.3%), and\u0026nbsp;\u0026nbsp; amplification was the main type. The frequencies of alteration of each hub gene are shown in Fig. 19. The Human Protein Atlas database, which indicated the expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were higher in WT tissue compared to normal kidney tissues (Fig. 20A-20E), while expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were lower in WT tissue compared to normal kidney tissues (Fig. 20F-20J). The ROC curve defined an optimal threshold to predict the recurrence risk of WT, and the AUC values of the ROC for FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were 0.991, 0.998, 0.952, 0.939, 0.994, 0.954, 0.905, 0.947, 0.938 and 0.973, respectively (Fig. 21). RT-PCR result were consistent with the results of the database analysis, mRNA expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were significantly higher in WT tissues compared with normal kidney tissues (Fig. 22A - 22E), while\u0026nbsp; mRNA expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were significantly lower in WT tissues compared with normal kidney tissues (Fig. 22F \u0026ndash; 22J).\u0026nbsp; The Immune infiltration analysis of up and down\u0026nbsp; hub genes from the TIMER was investigated using TCGA database. The results demonstrated that the higher expression level of FN1, AURKA, TRIM41, NFKBIA and TXNDC5 were all negatively associated with tumor purity (Fig. 23A - 23E), while lower expression level of SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 positively associated with tumor purity (Fig. 23F - 23J).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo better uncover the molecular pathogenesis and develop new prognostic, diagnostics and therapeutic strategies for WT, we performed this integrated analysis between WT patients and normal controls.\u0026nbsp; A total of 988 genes across the studies were consistently differentially expressed in WT (502 up regulated and 486 down regulated) with FDR \u0026lt; 0.05. MEIS1 was identified with development of WT [51]. GUCY1A3 was linked with angiogenesis in glioma [52], but this gene may be involved in angiogenesis in WT. TFF1 was linked with progression of gastric cancer [53], but this gene may be involved in pathogenesis of WT. Elevated expression of genes such as FGG (fibrinogen gamma chain) [54] and CGA (glycoprotein hormones, alpha polypeptide) [55] were liable for advancement of hepatocellular carcinoma, but high expression of\u0026nbsp; these genes may be associated with pathogenesis of WT. Methylation inactivation of tumor suppressor genes such as HOXA11 [56] and MAPK10 [57] were associated with development of various cancers types, but loss of\u0026nbsp; these genes may be liable for progression of\u0026nbsp; WT. Genes such as COL3A1 [58], S100P [59] and MYO1B [60] were important for invasion of various cancer cells types,\u0026nbsp; but these genes may be linked with invasion of WT cells. \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn pathway enrichment analysis for up regulated genes were carried out.. High expression of enriched genes such as SERPINA1 [61], FGB (fibrinogen beta chain) [62], SCG3 [63], ITIH3 [64], FST (follistatin) [65], AMBP (alpha-1-microglobulin/bikunin precursor) [66], IGFBP1 [67], IGFBP6 [68] and PLOD3 [69] were responsible for advancement of various cancers types, but over expression of these genes may linked with pathogenesis of WT.\u0026nbsp; Enriched genes such as CLU (clusterin) \u0026nbsp;[70], VTN (vitronectin) [71], SERPINE1 [72], SERPINE2 [73], FN1 [74], SLC3A2 [75], ITGA2 [76], ITGA3 [77],\u0026nbsp; ITGA5 [78], DOCK2 [79], L1CAM\u0026nbsp; [80], CAV1 [81], TSPAN7 [82], CRLF1 [83], SRPX (sushi-repeat-containing protein, X-linked) [84], FGL1 [85], CCL20 [86], COL1A2 [87], SEMA3C [88], GDF15 [89], ANXA11 [90], SPP1 [91], LAMA1 [92], TDGF1 [93], CXCL3 [94], LGALS3 [95], SERPINB1 [96] and LUM (lumican) [97] were associated with invasion of various cancer cells types, but these genes may be liable for invasion of WT cells. Enriched genes such as \u0026nbsp;SERPINA5 [98], ENO2 [99] and CSTB (cystatin B (stefin B)) [100] were involved in development of various cancers types, but these genes may be responsible for advancement of WT. Alteration in genes such as ESR1 [101], FOXA1 [102] and PRSS1 [103] were important for development of various cancer types, but mutation in these genes may be liable for progression of WT. Enriched genes such as NKX3-1 [104], GATA2 [105], CEACAM1 [106], RAB27B [107], SCUBE2 [108] and THBS2 [109] were involved in advancement of various cancers types, but these genes may be responsible\u0026nbsp; for progression of WT. \u0026nbsp;Enriched polymorphic genes such as MMP1 [110], APOA1 [111], ITPR3 [112], MMP3 [110], IGFBP3 [113], CSH1 [114], SERPINA6 [115] and APOC2 [116] were associated with pathogenesis of various cancers types, but these polymorphic genes may be linked with development of WT.\u0026nbsp; Enriched genes such as VEGFC (vascular endothelial growth factor C) [117], GATA3 [118], CD44 [119] and S100A4 [120] were important for development of WT. Our study found that FDFT1, EBP (emopamil binding protein (sterol isomerase)), DHCR7, F5 (coagulation factor V (proaccelerin, labile factor)), SERPINC1, C4A, C4BPB, APOB (apolipoprotein B (including Ag(x) antigen)), PDE2A, DGKZ (diacylglycerol kinase, zeta 104kDa), APOH (apolipoprotein H (beta-2-glycoprotein I)), LRP8, HBE1, HBG1, GOT1, PAH (phenylalanine hydroxylase), FSTL3, AREG (amphiregulin (schwannoma-derived growth factor), ITIH2, COCH (coagulation factor C homolog, cochlin (limulus polyphemus)), CLEC2B, HGD (homogentisate 1,2-dioxygenase (homogentisate oxidase)) and YARS (tyrosyl-tRNAsynthetase) are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. In pathway enrichment analysis for down regulated genes were carried out.\u0026nbsp; FCER1G was associated with chronic inflammation in kidney cancer [121], but this gene may be important for pathogenesis of WT.\u0026nbsp; Enriched genes such as COL1A1 [122]\u0026nbsp; STIM1 [123], MFAP2 [124], EFEMP1 [125], MMP11 [126], VCAM1 [127], COL2A1 [128], COL4A2 [129], COL13A1 [130], FMOD\u0026nbsp; (fibromodulin) [131],\u0026nbsp; ITGA8 [132], CAPN6 [133], MGP (matrix Gla protein) [134], SPON1 [135], FGF7 [136], PLXNA2 [137], CXCL12 [138], PTN (pleiotrophin (heparin binding growth factor 8, neurite growth-promoting factor 1)) [139], FNDC1 [140], SERPING1 [141], PCSK6 [142], TCF7L2 [143] and TLE4 [144] were associated with invasion of various cancer cells types,\u0026nbsp; but these genes may be liable for invasion of WT cells.\u0026nbsp; Methylation inactivation of enriched tumor suppressor genes such as ADCY4 [145], FBLN1 [146], FBN2 [147], ADAMTS9 [148], NELL2 [149] and PCDH18 [150] were important for progression of various cancers such as breast cancer, colorectal cancer, nasopharyngeal cancer and kidney cancer, but loss of these genes may be linked with development of WT. Enriched genes such as VWF (Von Willebrand factor) [151] and ACVR2B [152] were identified with progression of WT. ITPR1 was linked with activation of autophagy in kidney cancer [153], but this gene may be responsible for induction of autophagy in WT.\u0026nbsp; SYK (spleen tyrosine kinase) was liable for cancer drug resistance in ovarian cancer [154], but this gene may be involved in chemo resistance in WT. Enriched polymorphic genes such as MMP7 [155] and C1QA [156] were answerable for progression of\u0026nbsp; various cancer types, but these polymorphic genes may be important for advancement of WT. Enriched genes such as S100A9 [157], SCUBE3 [158], PDGFC (platelet derived growth factor C) [159] and CTBP2 [160] were linked with development of various cancer types, but elevated expression of these genes may be responsible for progression of WT. Low expression of genes such as SPARCL1 [161], SEMA3F [162], PCDH9 [163], CDH11 [164] and NR3C2 [165] were liable for development various cancer types, but decrease expression of these genes may be associated with advancement of WT.\u0026nbsp; PLXDC1 was identified with angiogenesis in ovarian cancer [166], but this gene may be associated with angiogenesis in WT.\u0026nbsp; Our study found that ITPK1, ADCY2, MYLK (myosin, light polypeptide kinase), EDNRA (endothelin receptor type A), COL14A1, FBN3, ASPN (asporin (LRR class 1)), NCAM1, KLKB1, TLL1, F13A1, FGF14, C1QTNF7, PAPPA2, FRAS1, CILP (cartilage intermediate layer protein, nucleotide pyrophosphohydrolase), NRG3, FREM1, FGL2, BMPER (BMP binding endothelial regulator), RSPO3, PLXDC2, TNFSF8, DCHS1, MYCN (V-mycmyelocytomatosis viral related oncogene, neuroblastoma derived (avian)),\u0026nbsp; PCDHB14, CDH5, ACVR2A, KCNJ8 and\u0026nbsp; ABCC9 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target.\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis for up regulated genes were carried out. High expression of enriched genes such as PDK1 [167], GCLM (glutamate-cysteine ligase, modifier subunit) [168], SPHK1 [169], NQO1 [170], CBS (cystathionine-beta-synthase) [171], P4HB [172], PCK2 [173], ADA (adenosine deaminase) [174], ADM (adrenomedullin) [175], AGR2 [176], ULBP2 [177], PTPRZ1 [178], LDLR (low density lipoprotein receptor (familial hypercholesterolemia))\u0026nbsp; [179], CASK\u0026nbsp; (calcium/calmodulin-dependent serine protein kinase (MAGUK family)) [180], KRT17 [181], TRAP1 [182] and EMP2 [183] were liable for progression of various cancer types, but increase expression of these genes may be important for pathogenesis of WT. Enriched genes such as ACSL4 [184], TYRP1 [185], PPA1 [186], PSMB8 [187], STAT5B [188], ENPP1 [189], CLIC1 [190], STC2 [191], BCHE (butyrylcholinesterase)\u0026nbsp; [192], APOM (apolipoprotein M) [193], HMGA1 [194], ICAM3 [195], DNER (delta-notch-like EGF repeat-containing transmembrane) [196] and CAV2 [197] were linked with invasion of various cancer cells types, but these genes may be liable for invasion of WT cells. Enriched genes such as FBP1 [198], INSIG1 [199], IER3 [200], GLDC (glycine dehydrogenase (decarboxylating)) [201], KYNU (kynureninase (L-kynurenine hydrolase)) [202], PLA2G2A [203], DKK1 [204], AZGP1 [205], WFDC1 [206] and SOCS2 [207] were answerable for pathogenesis of various cancer \u0026nbsp;types, but low expression of these genes may be associated with development of WT.\u0026nbsp; Enriched polymorphic genes such as COMT (catechol-O-methyltransferase) [208], PTGS2 [209], UTS2 [210], TMBIM1 [211], TAP2 [212] and EFNA1 [213] were culpable for progression of various cancer types, but these polymorphic genes may be linked with development of WT. Enriched genes such as SLC27A2 [214] and KRT10 [215] were important for drug resistance in ovarian cancer, but these genes may be liable for chemo resistance in WT. Methylation inactivation of tumor suppressor CDH13 was associated with progression of breast cancer [216], but inactivation of this gene may be important for advancement of WT. \u0026nbsp;Our study found that CDH13, CEL (carboxyl ester lipase (bile salt-stimulated lipase)), MAT1A, ETFB (electron-transfer-flavoprotein, beta polypeptide), TYR (tyrosinase (oculocutaneous albinism IA)), MID1IP1, SCD (stearoyl-CoA desaturase (delta-9-desaturase)), PYCR2, PLP1, WARS (tryptophanyl-tRNAsynthetase), ERO1A, SLC7A2, DECR2, ALDH4A1, CTPS1, GALE (UDP-galactose-4-epimerase), CYP8B1, DCT (dopachrometautomerase (dopachrome delta-isomerase, tyrosine-related protein 2)), PSMD6, RGN (regucalcin (senescence marker protein-30)), OTC (ornithine carbamoyltransferase), MECR (mitochondrial trans-2-enoyl-CoA reductase), EHHADH (enoyl-Coenzyme A, hydratase/3-hydroxyacyl Coenzyme A dehydrogenase), UGT2A3, PCCB (propionyl coenzyme A carboxylase, beta polypeptide), NRN1, HIST1H2BG, HIST1H2BK, NLGN1, HAMP (hepcidin antimicrobial peptide), KLHL17, HIST1H2BJ, LCP1, KLRK1, IL1RAP, GABRA5, BCAS3, CD3G and TSPAN4 are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Similarly, GO enrichment analysis for down regulated genes was carried out. Enriched genes such as MEOX2 [217], SIX1 [218] and RARA (retinoic acid receptor, alpha) [219] were identified with development of WT. Methylation inactivation of enriched tumor suppressor genes such as HOXA5 [220], SALL2 [221], TCF21 [222], PAX1 [223], ZNF516 [224], EBF3 [225] and ZNF677 [226] were liable for advancement of various cancer types, but loss of these genes may be important for pathogenesis of WT. Low expression of enriched genes such as HOXB6 [227], TSHZ3 [228], SIN3A [229], CAMTA1 [230], TCF4 [231] and MEIS2 [232] were linked with progression of various cancer types, but decrease expression of these genes may be responsible for advancement of WT. Enriched genes such as HOXC10 [233], RECK (reversion-inducing-cysteine-rich protein with kazal motifs) [234], NRP1 [235], STOX2 [236], NR2F2 [237], GPR161 [238], PBX1 [239], KLF7 [240], TCF12 [241], TFAP4 [242], KLF12 [243] and ZBTB20 [244] were linked with invasion of various cancer cells types, but these genes may be responsible for invasion of WT cells. Enriched genes such as FGFR2 [245], RARB (retinoic acid receptor, beta) [246], EFNB1 [247], ABCG1 [248], AUTS2 [249], MLLT11 [250], GLIS3 [251], HIF3A [252], ELF1 [253], STAG1 [254], BCL11A [255] and TSC22D1 [256] were important for pathogenesis of various cancer types, but these genes may be linked with progression of WT. CNOT2 was linked with angiogenesis in breast cancer [257], but this gene may be associated with\u0026nbsp; angiogenesis in WT. AFF3 was important for drug resistance in breast cancer [258], but this gene may be involved with chemo resistance in WT. Our study found that PDGFRB (platelet-derived growth factor receptor, beta polypeptide), KIDINS220, CLIC5, PGAP1, FLRT3, SLC8A1, ENAH (enabled homolog (Drosophila)), SMO (smoothened homolog (Drosophila)), STOX1, NRK (nik related kinase), MAFB (V-mafmusculoaponeuroticfibrosarcoma oncogene homolog B (avian)), RB1, NR2F1, MED25, ZNF211, ZNF605, ZNF420, ZNF135, ZNF300, ZNF501 and ZNF532 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target.\u003c/p\u003e\n\u003cp\u003ePPI network was constructed and analyzed for up regulated genes. AURKA was important for pathogenesis WT [259]. SMURF1 was responsible for invasion of breast cancer cells [260], but this gene may be liable for invasion of WT cells. NUDT21 was involved in proliferation of glioblastoma cells [261], but this gene may be associated with proliferation WT cells.\u0026nbsp; Our study found that NANOG (nanoghomeobox), SLC25A5 and KCNQ3 are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target. Similarly, PPI network was constructed and analyzed for down regulated genes. PLK1 was associated with proliferation of kidney cancer cells [262], but this gene may be liable for proliferation of WT cells.\u0026nbsp; Low expression of MAGI1 was linked with progression of kidney cancer [263], but decrease expression of this gene may be responsible for pathogenesis of WT.\u0026nbsp; Our study found that DDIT4L and MRPL15 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target.\u003c/p\u003e\n\u003cp\u003eModule analysis was performed for up regulated genes. Genes such as IGF2BP1 [264] and PIR (Pirin (iron-binding nuclear protein)) [265] were linked with invasion of various cancer cells types, bur these genes may be involved in invasion of WT cells. Over expression of CCND1 was involved in pathogenesis of breast cancer [266], but high expression of this gene may be linked with progression of WT.\u0026nbsp; Our study found that\u0026nbsp; APRT, HBZ, EIF2S1, CUL7 and TKT are up regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target.\u0026nbsp; Similarly, module analysis was performed for down regulated genes. FANCC (fanconianemia, complementation group C) was important for advancement of WT [267].\u003c/p\u003e\n\u003cp\u003eTarget gene ‐ miRNA network was constructed and analyzed for up regulated genes.\u0026nbsp; PTP4A1 was important for invasion of breast cancer cells [268], but this gene may be linked with invasion of WT cells.\u0026nbsp;\u0026nbsp; High expression RRM2 of\u0026nbsp; was\u0026nbsp; involved in advancement of cervical cancer [269], but elevated expression of this gene may be associated with development of WT. Similarly, target gene ‐ miRNA network was constructed and analyzed for down regulated genes. ZNF703 was liable for invasion of colorectal cancer cells [270], but this gene may be responsible for invasion of WT cells.\u003c/p\u003e\n\u003cp\u003eTarget gene ‐ TF network was constructed and analyzed for up regulated genes. MAGEC2 was linked with invasion of breast cancer cells [271], but this gene may be involved in invasion of WT cells. Similarly, target gene ‐ TF network was constructed and analyzed for down regulated genes. Methylation inactivation of tumor suppressor PLEKHO1 was responsible for advancement of gastric cancer [272], but loss of this gene may be important for pathogenesis of WT. Our study found that CACHD1 and CASD1 are down regulated in WT and has potential as a novel diagnostic and prognostic biomarker, and therapeutic target.\u003c/p\u003e\n\u003cp\u003eIn the current investigation, the DEGs \u0026nbsp;between WT and normal tissue samples in the GSE60850 dataset were determined, and the up and down regulated hub genes among the DEGs were demonstrated to be associated with the prognosis and diagonsis of patients with WT. Furthermore, FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were identified as possible candidate biomarkers for patients with WT. High FN1, AURKA, TRIM41, NFKBIA, TXNDC5 mRNA expression levels \u0026nbsp;and\u0026nbsp; low SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 mRNA expression levels \u0026nbsp;were validated by TCGA database, human protein atlas database and subsequent ROC analysis and RT‑qPCR analysis, which may preliminarily discover the pathophysiological role of these hub genes in WT at the molecular level.\u003c/p\u003e\n\u003cp\u003eIn conclusion, 988 DEGs and 10 hub genes were identified as potential diagnostic or prognostic biomarkers of WT. The current investigation identified several genes which had not been already associated with WT and implemented evidence that these genes were associated with this disease. Encourage examines are recommended to authenticate these results and to more precisely analyze the associations between these genes and WT. Overall, the current investigation highlights possibly new targets for more individualized treatment of patients with WT.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eI thank Richard Dafydd Williams, UCL Institute of Child Health, Developmental Biology and Cancer, 30 Guilford Street, London, United Kingdom, very much, the author who deposited their microarray dataset, GSE60850, into the public GEO database.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe authors declare that they have no conflict of interest.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eNo informed consent because this study does not contain human or animals participants.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE60850) (\u003ca style=\"color: #000000;\" href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119063\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE60850\u003c/a\u003e)]\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eNot applicable.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe authors declare that they have no competing interests.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eB. V. - Writing original draft, and review and editing\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eC. V. - Software and investigation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eI. K. - Supervision and resources\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eBasavaraj Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: 0000-0003-2202-7637\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eChanabasayya\u0026nbsp; Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: 0000-0003-3615-4450\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eIranna\u0026nbsp; Kotturshetti\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;ORCID ID: 0000-0003-1988-7345\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFord K, Gunawardana S, Manirambona E Philipoh GS Mukama B Kanyamuhunga A Cartledge P, Nyoni MJ, Mwaipaya D, Mpwaga J et al. 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Oncotarget. 2017;8(32):52211-52224. doi:18632/oncotarget.11075\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, Tables 1-8 are only available as a download in the supplementary files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"protein–protein interaction, gene expression, Wilms tumor, pathways, differentially expressed genes","lastPublishedDoi":"10.21203/rs.3.rs-133323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-133323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWilms tumor (WT) is a childhood kidney cancer with unknown etiology. Gene expression analysis has become very essential in WT.\u0026nbsp;Thus, we performed an integrated analysis of gene expression data to identify new molecular mechanisms and key functional genes in WT. Gene expression (GSE60850) dataset was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using limma. Pathway and Gene Ontology (GO) enrichment analyses were performed for the DEGs by ToppGene database. Then, protein–protein interaction (PPI) networks \u0026nbsp;and modules were established by the Mentha database and PEWCC1, \u0026nbsp;and visualized by Cytoscape software. Target gene\u0026nbsp;- miRNA regulatory network and target gene\u0026nbsp;- TF regulatory network were established by the Network Analyst database and visualized by Cytoscape software. Finally,\u0026nbsp;survival analysis, expression analysis,\u0026nbsp;stage analysis, mutation analysis,\u0026nbsp;immunohistochemical (IHC) analysis,\u0026nbsp;receiver operating characteristic (ROC), reverse transcription polymerase chain\u0026nbsp;reaction (RT-PCR) and immune infiltration analysis of hub genes was performed. We identified 988 DEGs ultimately including 502 up regulated genes and 486 down regulated genes. Pathway\u0026nbsp;and GO enrichment analysis revealed that DEGs were mainly enriched in D-myo-inositol (3,4,5,6)-tetrakisphosphate biosynthesis, platelet activation, cholesterol biosynthesis III, and complement, coagulation cascades, embryo development, cell surface, DNA-binding transcription factor activity, carboxylic acid metabolic process, extracellular space and signaling receptor binding. FN1, AURKA, TRIM41, NFKBIA, TXNDC5, SIN3A, MAGI1, GPRASP2, UCHL1 and FXYD6 were filtrated as the hub genes. These identified DEGs and hub genes facilitate our knowledge of the underlying molecular mechanism of WT and have the potential to be used as diagnostic and prognostic biomarkers or therapeutic targets for WT.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"Hub Genes and Key Pathway Identification in Wilms Tumor Based on Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-22 00:12:36","doi":"10.21203/rs.3.rs-133323/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":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"December 22nd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1569012,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2020-12-22T00:12:36+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-22 00:12:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-133323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-133323","identity":"rs-133323","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}