Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy | 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 Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy Harish Joshi, Basavaraj Vastrad, Nidhi Joshi, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-132705/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Objectives The underlying molecular mechanisms of diabetic nephropathy (DN) have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of DN. Methods We downloaded next generation sequencing (NGS) dataset GSE142025 from Gene Expression Omnibus (GEO) database having 28 DN samples and 9 normal control samples. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed. Biological function analysis of the DEGs was enriched by GO and REACTOME pathway. Then we established the protein-protein interaction (PPI) network, modules, miRNA-DEG regulatory network and TF-DEG regulatory network. Hub genes were validated by using receiver operating characteristic (ROC) curve analysis. Results A total of 549 DEGs were detected including 275 up regulated and 274 down regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in cell activation, integral component of plasma membrane, lipid binding and biological oxidations. Analyzing the PPI network, miRNA-DEG regulatory network and TF-DEG regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 by the Cytoscape software. The ROC curve analysis confirmed that hub genes were of diagnostic value. Conclusions Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in DN, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for DN. Bioinformatics bioinformatics analysis protein-protein interaction network differentially expressed genes diabetic nephropathy novel biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Diabetic nephropathy (DN) is a common and devastating microvascular complication of the kidneys induced by diabetes mellitus 1 . The incidence of DN is reported to be 30% to 40% patients with diabetes 2 and is the main cause of end - stage renal disease throughout the world in both developed and developing countries 3 . Numerous risk factors may affect DN progression 4 ; however, how these factors affect the development of DN requires further study and no effective method has been developed. Despite important developments toward an understanding of the pathophysiology of DN, early diagnosis, therapeutic interference, and underlying molecular pathogenesis hover a require 5 . Therefore, enlighten the rare nature belonging to DN is predominant in expand therapies to improve patient outcome. DN remains end - stage renal disease worldwide because of its complicated molecular mechanisms and cellular heterogeneity, and its prevalence raise every year 6 . Therefore, recognition of DN might offer clinicians novel tools that can be used to treat the disease. Extensive genomic investigations showing the effects of genes have accepted noticeable attention. Many key genes must be identified to develop the clinical outcome for DN patients. However, the number of biomarkers that can be used to show therapeutic effects is still limited, and prognostic factors are essential for the treatment of DN patients. Therefore, it is necessary to elucidate the detailed molecular mechanisms that are independently associated with DN. The exact mechanisms of DN are still unknown. A number of investigations have reported possible roles of some genes and pathways such as UCP1-3 7 and JAK/STAT3 signaling pathway 8 in the development of DN. However, these reports only concentrated on any certain molecule, gene or pathway, ignoring that the development process involves aberrant expression of a variety of genes and pathways, among which some proteins might interact with other proteins and thus play an essential role in the DN 9 . Hub genes might act as prognostic or diagnostic biomarkers or treatment targets for DN 10-12 . Therefore, it is urgent to search new biomarkers for DN with a powerful genome-wide technology. High-throughput platform next generation sequencing (NGS) data increasingly valued for the analysis of gene expression in DN 13 . A high-quality NGS data could potentially link molecular biomarkers to the development, diagnosis, and treatment of DN. We downloaded NGS dataset GGSE142025 14 from Gene Expression Omnibus database (GEO) (http://www.ncbi.nlm.nih.gov/geo) 15 , which contain gene expression data from DN samples and normal control sample . We then performed deep bioinformatics analysis, including identifying differential expressed genes (DEGs), gene ontology (GO) and pathway enrichment analysis and protein-protein interaction (PPI) network and module analysis, miRNA-DEG regulatory network and TF-DEG regulatory network. The findings were further validated by receiver operating characteristic (ROC) curve analysis. The aim of this investigation was to identify key genes and pathways, and to explore potential candidate biomarkers for the diagnosis and therapeutic targets in DN. Materials And Methods Data source The DN NGS dataset GSE142025 14 was downloaded from the NCBI GEO database. The dataset GSE142025 was based on the GPL20301 platform (Illumina HiSeq 4000 (Homo sapiens)), including 28 DN samples and 9 normal control samples. NGS data GSE142025 has generated massive genomic data, which facilitated understanding the molecular mechanisms involved in the DN. Identification of DEGs Identify a gene that are differentially expressed across experimental conditions, was used to identify DEGs in the GSE142025 dataset with the limma package of R language. Adjusted P-value was retrieved by implement the Benjamini-Hochberg false discovery rate (FDR) correction on the original P-value, and a fold change threshold was preferred based on our plan to target on statistically significant DEGs 16 . Only genes with a fold change > 1.35 for up regulated genes and fold change < -1.24 for down regulated genes, and adjusted P-value <0.05 were considered as statistically significant DEGs. A volcano plot and heat map of the identified DEGs was also constructed, using an R package. Gene ontology (GO) and pathway enrichment analysis of DEGs In the current investigation, the significant enrichment analysis of DEGs was assessed based on the Gene Ontology (GO) and REACTOME using the ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) 17 , an online tool for functional annotation analysis. GO analysis (http://geneontology.org/) 18 is a accepted, effective method for annotating genes and gene products, and for identifying unique biological aspect of high-throughput genome or transcriptome data, including 3 categories: biological process (BP), cellular component (CC) and molecular function (MF). REACTOME database (https://reactome.org/) 19 is a well-known pathway database for precise analysis of gene functions in biological signaling pathways, which links genomic information with higher-order functional information. To analyze the function of the identified DEGs, biologic analyses were performed using GO enrichment and REACTOME enrichment pathway analysis via ToppGene online database. P < .05 as the cutoff criterion considered statistically significant. Construction of protein-protein interaction (PPI) network The online database InnateDB interactome (https://www.innatedb.com/) 20 was used to construct PPI network and module analysis. Cytoscape (version 3.8.1) (www.cytoscape.org/) 21 was used to visualize the PPI network of DEGs. Next, the Network Analyzer plugin for Cytoscape was applied to calculate node degree 22 , betweenness centrality 23 , stress centrality 24 and closeness centrality 25 . The plug-in PEWCC1 26 of Cytoscape was applied to detect densely connected regions in PPI networks. The PPI networks were constructed using Cytoscape and the most significant module in the PPI networks was selected using PEWCC1. Integrated regulatory network construction The integrated regulatory network of miRNAs (microRNAs) and TFs (transcription factors) was constructed based on standardized integration of numerous high-throughput datasets. It granted a plan including a set of hub genes, miRNAs and TFs for analyzing multi-level regulation in DN. The miRNAs associated with DEGs were selected from miRNet ( https://www.mirnet.ca/ ) 27 Database (The integration database of TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0), and TFs associated with DEGs were selected from NetworkAnalyst database (https://www.networkanalyst.ca/) 28 Database (The integration database of JASPAR). The miRNA-DEG regulatory network and TF-DEG regulatory network were constructed by using Cytoscape software 21 , which is open source software for visualizing complex networks. Receiver operating characteristic (ROC) curve analysis of the hub genes The diagnostic value of validated hub genes was assessed using receiver operating characteristic (ROC) curve analysis using the pROC in R with GLM prediction model 29 . An area under the curve (AUC) value was determined and used to nominate the ROC effect. Results Identification of DEGs DN and normal control samples (28 and 9, respectively) were first analyzed. Limma was used to identify the DEGs. Following analysis of NGS dataset GSE142025, 549 DEGs (275 up regulated and 274 down regulated) genes were identified and are listed in Table 1. The volcano plot and heatmap are shown in Fig. 1 and Fig. 2, respectively. Gene ontology and pathway enrichment analysis of DEGs The identified DEGs were uploaded to the online software ToppGene for GO and REACTOME pathway enrichment analyses and results are listed in Table 2 and Table 3. The results of the GO analysis revealed that up regulated genes were significantly enriched in BP, including cell activation and regulation of immune system process, whereas down regulated genes were significantly enriched in response to hormone and ion transport. In terms of CC, the up regulated genes were enriched in cell surface and intrinsic component of plasma membrane, whereas down regulated genes were enriched in integral component of plasma membrane and nuclear chromatin. In terms of MF, the up regulated genes were enriched in signaling receptor binding and identical protein binding, whereas down regulated genes were enriched in lipid binding and transporter activity. REACTOME pathway analysis revealed that the up regulated genes were highly associated with pathways including immunoregulatory interactions between a lymphoid and a non-lymphoid cells, and innate immune system, whereas down regulated genes were significantly enriched in biological oxidations and GPCR ligand binding. Construction of protein-protein interaction (PPI) network The DEG expression profiles in DN were constructed according to the information in the InnateDB interactome database. The PPI network of DEGs is consisted of 2718 nodes and 4477 edges (Fig. 3). There are 10 genes selected as hub genes, such as MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 are listed in Table 4. A two significant modules were obtained from PPI network of DEGs using PEWCC1, including 15 nodes and 36 edges (Fig. 4A) and 7 nodes and 12 edges (Fig. 4B). Gene ontology and pathway enrichment analysis revealed that genes in these modules were mainly involved in innate immune system, tmmunoregulatory interactions between a lymphoid and a non-lymphoid cell, cell activation, regulation of immune system process, cell surface, response to hormone, cytokine signaling in immune system, metabolism of proteins and nuclear chromatin. Integrated regulatory network construction The miRNA-DEG regulatory network had 8997 interactions (involving 1973 miRNAs and 248 DEGs) (Fig. 5). Moreover, COL1A1 was targeted by 178 miRNAs (ex, hsa-mir-4492), IRF4 was targeted by 140 miRNAs (ex, hsa-mir-4319), MYBL2 was targeted by 83 miRNAs (ex, hsa-mir-637), PRKCB was targeted by 81 miRNAs (ex, hsa-mir-1261), IL2RB was targeted by 54 miRNAs (ex, hsa-mir-4300), JUN was targeted by 144 miRNAs (ex, hsa-mir-3943), EGR1 was targeted by 132 miRNAs (ex, hsa-mir-548e-3p), ZFP36 was targeted by 130 miRNAs (ex, hsa-mir-6077), FOS was targeted by 105 miRNAs (ex, hsa-mir-5586-5p) and DUSP1 was targeted by 97 miRNAs (ex, hsa-mir-4458) are listed in Table 5. The TF-DEG regulatory network had 1954 interactions (involving 81 TFs and 250 DEGs) (Fig. 6). Moreover, IRF4 was targeted by 10 TFs (ex, NFATC2), LCK was targeted by 10 TFs (ex, YY1), RET was targeted by 10 TFs (ex, NR2C2), MAP1LC3C was targeted by 10 TFs (ex, MAX), IL2RB was targeted by 8 TFs (ex, PDX1), ATF3 was targeted by 19 TFs (ex, TP53), EGR1 was targeted by 16 TFs (ex, ARID3A), JUNB was targeted by 15 TFs (ex, SRF), FOS was targeted by 13 TFs (ex, CREB1) and PTPRO was targeted by 13 TFs (ex, NR3C1) are listed in Table 5. Receiver operating characteristic (ROC) curve analysis of the hub genes Ten hub genes are prominently expressed in DN; we performed a ROC curve analysis to evaluate their sensitivity and specificity for the diagnosis of DN. As shown in Fig. 7, MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 achieved an AUC value of >0.8, demonstrating that these hub genes have high sensitivity and specificity for DN diagnosis. The results suggested that MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 can be used as biomarkers for the diagnosis of DN. Discussion DN affects millions of people all over the world 30 . DN occurs when the uncontrolled diabetes, which is a worldwide complaint. The aim of this investigation was to screen and verify hub genes involved in DN as well as to explore potential molecular mechanisms. In the present study, NGS data from GSE142025 was extracted to identify the DEGs between DN and normal control. In this investigation, we identified 549 DEGs, which includes 275 up regulated and 274 down regulated genes. CFHR1 31 and RGS1 32 have been reported to altered expression in nephropathy. Studies have showed that GREM1 is linked with progression of DN 33 . Existing evidence has reported that CCL19 34 is responsible for renal inflammation and fibrosis in DN. COL6A5 35 is associated with neuropathic chronic itch. Reports indicate that CIDEC (cell death inducing DFFA like effector c) 36 was found in progression of obesity. NR4A1 drives DN growth through mitochondrial fission and mitophagy 37 . Recent study has reported that expression of NR4A2 is associated with myocardial infarction 38 . EGR1 is required for fibrosis and inflammatory response in DN 39 . ATF3 expression has been implicated in DN 40 . Altered expression of NR4A3 contribute to type 2 diabetes mellitus progression 41 . A previous study showed that KLK1 gene is involved in DN 42 . A series of DEGs were discovered to be enriched in the GO functions and pathways. Previous investigation have shown a signaling pathway includes innate immune system 43 , extracellular matrix organization 44 , hemostasis 45 , cytokine signaling in immune system 46 and metabolism of proteins 47 were associated with DN development. Previous studies had reported that expression of SERPINA3 48 , IKZF1 49 , BTK (Bruton tyrosine kinase) 50 , C1QA 51 , CD1C 52 and CCL13 53 were correlated with lupus nephritis. Recent studies have demonstrated that expression of TNFSF14 54 , ITGAL (integrin subunit alpha L) 55 , PLAC8 56 , ADRA2A 57 , CCL21 58 , ALOX5 59 , CNR2 60 , COL1A1 61 , WNT7A 62 , SLAMF1 63 , CD3D 64 , LTF (lactotransferrin) 65 , MIR27B 66 , PDK4 67 , UCN3 68 , PCK1 69 , CEL (carboxyl ester lipase) 70 , TRPM6 71 , MTTP (microsomal triglyceride transfer protein) 72 , CYP2C8 73 and CYP3A4 74 are associated with progression of type 2 diabetes mellitus. Recent studies have proposed that the altered expression of MZB1 75 , LAIR1 76 , MIR142 77 and FAP (fibroblast activation protein alpha) 78 have been shown to be a meaningful advance factor for myocardial infarction. IRF4 plays a key role in the obesity-induced insulin resistance 79 . Accumulating evidence showed that altered expression of genes such as MDK (midkine) 80 , CCR2 81 , SAA1 82 , C3 83 , CD19 84 , CCR5 85 , CXCR3 86 , FABP4 87 , GDF15 88 , IGF2 89 , IGFBP1 90 and IL6 91 are important in the progression of DN. A previous study has shown that UBASH3A 92 , SIRPG (signal regulatory protein gamma) 93 , IKZF3 94 , CD1D 95 , CD2 96 , CD48 97 , CD247 98 and CYP27B1 99 are liable for progression of type 1 diabetes mellitus. The studies have shown that expression of SIT1 100 , JAML (junction adhesion molecule like) 101 , TIMP1 102 , PRKCB (protein kinase C beta) 103 , MMP7 104 , WNT7B 105 , WNT10A 106 , DUSP1 107 , WT1 108 , APOC3 109 , ERRFI1 110 , HCN2 111 , MME (membrane metalloendopeptidase) 112 , STRA6 113 , SLC12A3 114 and GC (GC vitamin D binding protein) 115 expedites epithelial to mesenchymal transition and renal fibrosis in DN. Previous studies have found CFD (complement factor D) 116 , DOCK2 117 , LYZ (lysozyme) 118 , CD5L 119 , SCARA5 120 , VCAN (versican) 121 , GDF5 122 , SFRP2 123 , BTG2 124 , ZFP36 125 , GPR3 126 , OLR1 127 , PM20D1 128 and UGT2B7 129 to be expressed in obesity. A study has confirmed that altered expression of FCRL3 130 , FCGR2B 131 , COMP (cartilage oligomeric matrix protein) 132 , ERFE (erythroferrone) 133 and NPHS1 134 are involved in progression of nephropathy. The expression of COL1A2 135 , LCK (LCK proto-oncogene, Src family tyrosine kinase) 136 , LCN2 137 and APOB (apolipoprotein B) 138 are key for progression of diabetic retinopathy. Researchers showed that altered expression of COL3A1 139 , PER1 140 , JUN (Jun proto-oncogene, AP-1 transcription factor subunit) 141 , SLC26A4 142 , F2RL3 143 , CYP4A11 144 and CYP4F2 145 play an important role in the hypertension. Collectively, results of enriched GO and REACTOME pathway enrichment analysis were positively correlated with experimental findings. However, further investigations are needed to explore and confirm the potentially significant pathways for DN and to achieve a comprehensive understanding of this process. Based on the PPI network and module analysis, we obtained top hub genes in the whole network. ALB (albumin) 146 has been shown as a promising biomarker in DN. In our study, correlations of MDFI (MyoD family inhibitor) and FOS (Fos proto-oncogene, AP-1 transcription factor subunit) with patient prognosis highlight the importance of these genes as novel biomarkers to stratify DN patients as well as potential therapeutic targets, but concrete roles of these genes need further investigation. Based on the miRNA-DEG regulatory network and TF-DEG regulatory network, we obtained target in the whole network. Recent investigation reported that the altered expression of MYBL2 was associated with myocardial infarction progression 147 , but this gene might be novel target for DN. Many investigation have reported that expression of hsa-mir-637 148 and NR3C1 149 were linked with progression of hypertension, but these genes might be novel target for DN. Hsa-mir-1261 150 has been shown to have an important role in DN. Hsa-mir-4458 151 has been found to be differentially expressed in myocardial infarction, but this gene might be novel target for DN. The recent studies have reported that expression of NFATC2 152 , PDX1 153 and CREB1 154 were involved in the progression of type 2 diabetes, but these genes might be novel target for DN. Previous studies have demonstrated that expression of YY1 155 , TP53 156 and SRF (serum-response factor) 157 played a key role in progression of DN. IL2RB, hsa-mir-4492, hsa-mir-4319, hsa-mir-4300, hsa-mir-3943, hsa-mir-548e-3p, hsa-mir-6077, hsa-mir-5586-5p, RET (ret proto-oncogene), MAP1LC3C, PTPRO (protein tyrosine phosphatase receptor type O), NR2C2, MAX (myc-associated factor X) and ARID3A might be novel diagnostic biomarkers associated with the progression of DN, which remains to be verified based on a larger sample. Besides, this investigation is purely a bioinformatics analysis without any in vivo and in vitro data. There are some limitations in our investigation. The lack of molecular and cellular evidences in wet-lab experiment imposes restrictions on the conclusions that can be drawn from our results. Conclusions In sum, based on a series of bioinformatics methods and a retrospective analysis, our identified 10 hub genes (MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1), which showed an intimate correlation with DN advancement and prognosis and had the potential acting as therapeutic targets and prognostic indicators. Further experiments are required to confirm the expression and potential functions of the identified key genes in DN. Declarations Acknowledgement I thank Weijia Zhang, Icahn School of Medicine at Mount Sinai, Renal, New York, USA, very much, the author who deposited their expression profiling by high throughput sequencing dataset, GSE142025, into the public GEO database. We sincerely thank the preprint research square for providing information online; it is our pleasure to acknowledge their contributions. 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. [(GSE142025) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142025] Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. Author Contributions H.J - Methodology and validation B.V - Writing original draft, and review and editing N .J - Software and resources C.V- Investigation and resources Authors Harish Joshi ORCID ID: 0000-0002-3817-5194 Basavaraj Vastrad ORCID ID: 0000-0003-2202-7637 Nidhi Joshi ORCID ID: 0000-0001-8067-3448 Chanabasayya Vastrad ORCID ID: 0000-0003-3615-4450 References Papadopoulou-Marketou N, Chrousos GP, Kanaka-Gantenbein C. Diabetic nephropathy in type 1 diabetes: a review of early natural history, pathogenesis, and diagnosis. Diabetes Metab Res Rev. 2017;33(2):10.1002/dmrr.2841. doi:10.1002/dmrr.2841 Umanath K, Lewis JB. Update on Diabetic Nephropathy: Core Curriculum 2018. Am J Kidney Dis. 2018;71(6):884-895. doi:10.1053/j.ajkd.2017.10.026 Qi C, Mao X, Zhang Z, Wu H. Classification and Differential Diagnosis of Diabetic Nephropathy. J Diabetes Res. 2017;2017:8637138. doi:10.1155/2017/8637138 Wang G, Ouyang J, Li S, Wang H, Lian B, Liu Z, Xie L. 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Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis. Biomed Res Int. 2019;2019:5742608. doi:10.1155/2019/5742608 Keller MP, Paul PK, Rabaglia ME, Stapleton DS, Schueler KL, Broman AT, Ye SI, Leng N, Brandon CJ, Neto EC, et al. The Transcription Factor Nfatc2 Regulates β-Cell Proliferation and Genes Associated with Type 2 Diabetes in Mouse and Human Islets. PLoS Genet. 2016;12(12):e1006466. doi:10.1371/journal.pgen.1006466 Fujimoto K, Chen Y, Polonsky KS, Dorn GW 2nd. Targeting cyclophilin D and the mitochondrial permeability transition enhances beta-cell survival and prevents diabetes in Pdx1 deficiency. Proc Natl Acad Sci U S A. 2010;107(22):10214-10219. doi:10.1073/pnas.0914209107 Xu Y, Song R, Long W, Guo H, Shi W, Yuan S, Xu G, Zhang T. CREB1 functional polymorphisms modulating promoter transcriptional activity are associated with type 2 diabetes mellitus risk in Chinese population. 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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-132705","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":6896406,"identity":"8f39d072-4b3c-4f48-b3f2-a248d009ef47","order_by":0,"name":"Harish Joshi","email":"","orcid":"https://orcid.org/0000-0002-3817-5194","institution":"Endocrine and Diabetes Care Center, Hubbali, Karnataka 580029, India.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Harish","middleName":"","lastName":"Joshi","suffix":""},{"id":6896407,"identity":"d63dcb8d-9787-4b59-909e-ac4d1fc2ce48","order_by":1,"name":"Basavaraj Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Pharmaceutics, SET`S College of Pharmacy, Dharwad, Karnataka, 580002, India.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":6896408,"identity":"30705a5c-138a-4f44-b321-39918c8ab907","order_by":2,"name":"Nidhi Joshi","email":"","orcid":"https://orcid.org/0000-0001-8067-3448","institution":"Dr. D. Y. Patil Medical College, Kolhapur, 416006, Maharashtra, India.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Joshi","suffix":""},{"id":6896410,"identity":"6fd2f96b-96b5-480c-aa5b-7bfca7c3c008","order_by":3,"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, Karanataka, 580001, India.","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""}],"badges":[],"createdAt":"2020-12-20 15:21:27","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-132705/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-132705/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":26662519,"identity":"516706b9-8e27-4649-95af-03a33d5c55e2","added_by":"auto","created_at":"2022-09-19 17:33:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126321,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/bd4c3c1fa64414894df1104a.jpg"},{"id":26662521,"identity":"f333bf7b-3cf8-4710-95b6-70d0e579e602","added_by":"auto","created_at":"2022-09-19 17:33:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62021,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/5fa9a8432052b7933477a8cf.jpg"},{"id":26662695,"identity":"01edbe38-ce51-452e-a647-8989186fa53a","added_by":"auto","created_at":"2022-09-19 17:38:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109417,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. \u0026nbsp;(A1 – A28 = DN samples; B1 – B9 = \u0026nbsp;normal control samples)\u003c/p\u003e","description":"","filename":"f3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/5e00f90e705ae4a6577459b6.jpg"},{"id":26662696,"identity":"a45d5bd5-e7dc-4a1b-bc88-ca94dfcd52eb","added_by":"auto","created_at":"2022-09-19 17:38:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92840,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs . The PPI network of DEGs was constructed using Cytoscape. Up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"f4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/2b7e4d893eadf4a079ae80de.jpg"},{"id":26662697,"identity":"d942415f-989b-4426-b0e6-55d07e1071ab","added_by":"auto","created_at":"2022-09-19 17:38:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40778,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant modules of DEGs. (A) The most significant module was obtained from PPI network with 15 nodes and 36 edges for up regulated genes (B) The most significant module was obtained from PPI network with 7 nodes and 12 edges for up regulated genes.. Up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"f5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/f1dcdabe7d289589325aa86b.jpg"},{"id":26662524,"identity":"be037bfb-b596-47d2-9ec1-a2fc6f68a0e9","added_by":"auto","created_at":"2022-09-19 17:33:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":132958,"visible":true,"origin":"","legend":"\u003cp\u003eTarget gene - miRNA regulatory network between target genes and miRNAs. Up regulated genes are marked in green; down regulated genes are marked in red; The blue color diamond nodes represent the key miRNAs\u003c/p\u003e","description":"","filename":"f6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/54f5790bc5b6603ea1f06d73.jpg"},{"id":26662525,"identity":"3bcc2a1c-62bc-4a7e-8334-a41ff66b0aec","added_by":"auto","created_at":"2022-09-19 17:33:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93446,"visible":true,"origin":"","legend":"\u003cp\u003eTarget gene - TF regulatory network between target genes and TFs. \u0026nbsp;\u0026nbsp;Up regulated genes are marked in green; down regulated genes are marked in red; The blue color diamond nodes represent the key miRNAs; the yellow color triangle nodes represent the key TFs\u003c/p\u003e","description":"","filename":"f7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/2fb658c6da851f78b7775c90.jpg"},{"id":26662798,"identity":"90cd5975-5021-40d0-afa0-eb8ff51e2b8b","added_by":"auto","created_at":"2022-09-19 17:43:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":44091,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve validated the sensitivity, specificity of hub genes as a predictive biomarker for DN prognosis. A) MDFI B) LCK C) BTK D) IRF4 E) PRKCB F) EGR1G) JUN H) FOS I) ALB J) NR4A1\u003c/p\u003e","description":"","filename":"f8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/62ccc8733619bd6928b47b63.jpg"},{"id":26662701,"identity":"54d36af1-f7e2-42a5-83cf-e550013f7cc7","added_by":"auto","created_at":"2022-09-19 17:38:45","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":49427,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of hub genes by RT- PCR. A) MDFI B) LCK C) BTK D) IRF4 E) PRKCB F) EGR1G) JUN H) FOS I) ALB J) NR4A1\u003c/p\u003e","description":"","filename":"f9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/a3759bb5caf0f9251dfedbd8.jpg"},{"id":26663126,"identity":"a58c79fe-cf5c-4f09-89df-6216c72d8a04","added_by":"auto","created_at":"2022-09-19 17:48:45","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":48356,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of designed molecules\u003c/p\u003e","description":"","filename":"f10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/c55989b239fe9caa882ffe1f.jpg"},{"id":26662698,"identity":"952025f4-b9d7-4f06-b80e-7cc87c79ced5","added_by":"auto","created_at":"2022-09-19 17:38:45","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":49738,"visible":true,"origin":"","legend":"\u003cp\u003eI) A) SALISO 2 B) SALPYR 12 are the molecules with good binding score II) 3D binding of molecule A) SALISO 2 B) SALPYR 12 with 5UTZ and 5T5T III) 2D Binding of Molecule A) SALISO 2 B) SALPYR 12 with 5UTZ and 5T5T\u003c/p\u003e","description":"","filename":"f11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/1a0ebb0e699870bff21b7d55.jpg"},{"id":26663127,"identity":"1e66f277-94c2-47f1-85ff-b6dd0da42a45","added_by":"auto","created_at":"2022-09-19 17:48:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/a0602d4e-7e1b-4589-befa-e1105b2321cd.pdf"},{"id":26662529,"identity":"221a791f-8d10-45cc-9d71-3431ca3ecbaf","added_by":"auto","created_at":"2022-09-19 17:33:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":154928,"visible":true,"origin":"","legend":"\u003cp\u003eTables\u003c/p\u003e","description":"","filename":"RenalFailureTablesRevesion77.docx","url":"https://assets-eu.researchsquare.com/files/rs-132705/v2/ca7199869b2df310cc4eff1c.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic nephropathy (DN) is a common and devastating microvascular complication of the kidneys induced by diabetes mellitus\u003csup\u003e1\u003c/sup\u003e. The incidence of DN is reported to be 30% to 40% patients with diabetes\u003csup\u003e2\u003c/sup\u003e and is the main cause of end - stage renal disease throughout the world in both developed and developing countries \u003csup\u003e3\u003c/sup\u003e. Numerous risk factors may affect DN progression\u003csup\u003e4\u003c/sup\u003e; however, how these factors affect the development of DN requires further study and no effective method has been developed. Despite important developments toward an understanding of the pathophysiology of DN, early diagnosis, therapeutic interference, and underlying molecular pathogenesis hover a require\u003csup\u003e5\u003c/sup\u003e. Therefore, enlighten the rare nature belonging to DN is predominant in expand therapies to improve patient outcome.\u003c/p\u003e\n\u003cp\u003eDN remains end - stage renal disease worldwide because of its complicated molecular mechanisms and cellular heterogeneity, and its prevalence raise every year\u003csup\u003e6\u003c/sup\u003e. Therefore, recognition of DN might offer clinicians novel tools that can be used to treat the disease. Extensive genomic investigations showing the effects of genes have accepted noticeable attention. Many key genes must be identified to develop the clinical outcome for DN patients. However, the number of biomarkers that can be used to show therapeutic effects is still limited, and prognostic factors are essential for the treatment of DN patients. Therefore, it is necessary to elucidate the detailed molecular mechanisms that are independently associated with DN.\u003c/p\u003e\n\u003cp\u003eThe exact mechanisms of DN are still unknown. A number of investigations have reported possible roles of some genes and pathways such as UCP1-3\u003csup\u003e7\u003c/sup\u003e and JAK/STAT3 signaling pathway\u003csup\u003e8\u003c/sup\u003e in the development of DN. However, these reports only concentrated on any certain molecule, gene or pathway, ignoring that the development process involves aberrant expression of a variety of genes and pathways, among which some proteins might interact with other proteins and thus play an essential role in the DN\u003csup\u003e9\u003c/sup\u003e. Hub genes might act as prognostic or diagnostic biomarkers or treatment targets for DN\u003csup\u003e10-12\u003c/sup\u003e. Therefore, it is urgent to search new biomarkers for DN with a powerful genome-wide technology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High-throughput platform next generation sequencing (NGS) data increasingly valued for the analysis of gene expression in DN\u003csup\u003e13\u003c/sup\u003e. A high-quality NGS data could potentially link molecular biomarkers to the development, diagnosis, and treatment of DN.\u003c/p\u003e\n\u003cp\u003eWe downloaded NGS dataset \u0026nbsp;GGSE142025\u003csup\u003e14\u003c/sup\u003e from Gene Expression Omnibus database (GEO) (http://www.ncbi.nlm.nih.gov/geo)\u003csup\u003e15\u003c/sup\u003e, which contain gene expression data from DN samples and \u0026nbsp;normal control sample . We then performed deep bioinformatics analysis, including identifying differential expressed genes (DEGs), gene ontology (GO) and pathway enrichment analysis and protein-protein interaction (PPI) network and module analysis, miRNA-DEG regulatory network and TF-DEG regulatory network. The findings were further validated by receiver operating characteristic (ROC) curve analysis. The aim of this investigation was to identify key genes and pathways, and to explore potential candidate biomarkers for the diagnosis and therapeutic targets in DN.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DN NGS dataset GSE142025\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e was downloaded from the NCBI GEO database. The dataset GSE142025 was based on the GPL20301 platform (Illumina HiSeq 4000 (Homo sapiens)), including 28 DN samples and 9 normal control samples. NGS data GSE142025 has generated massive genomic data, which facilitated understanding the molecular mechanisms involved in the DN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentify a gene that are differentially expressed across experimental conditions, was used to identify DEGs in the GSE142025 dataset with the limma package of R language. Adjusted P-value was retrieved by implement the Benjamini-Hochberg false discovery rate (FDR) correction on the original P-value, and a fold change threshold was preferred based on our plan to target on statistically significant DEGs\u003csup\u003e16\u003c/sup\u003e. Only genes with a fold change \u0026gt; 1.35 for up regulated genes and fold change \u0026lt; -1.24 for down regulated genes, and adjusted P-value \u0026lt;0.05 were considered as statistically significant DEGs. A volcano plot and heat map of the identified DEGs was also constructed, using an R package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology (GO) and pathway enrichment analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the current investigation, the significant enrichment analysis of DEGs was assessed based on the Gene Ontology (GO) and REACTOME using the ToppGene (ToppFun) \u0026nbsp;(https://toppgene.cchmc.org/enrichment.jsp)\u003csup\u003e17\u003c/sup\u003e, an online tool for functional annotation analysis. GO analysis (http://geneontology.org/)\u003csup\u003e18\u003c/sup\u003e is a accepted, effective method for annotating genes and gene products, and for identifying unique biological aspect of high-throughput genome or transcriptome data, including 3 categories: biological process (BP), cellular component (CC) and molecular function (MF). REACTOME database (https://reactome.org/)\u003csup\u003e19\u003c/sup\u003e is a well-known pathway database for precise analysis of gene functions in biological signaling pathways, which links genomic information with higher-order functional information. To analyze the function of the identified DEGs, biologic analyses were performed using GO enrichment and REACTOME enrichment pathway analysis via ToppGene online database. P \u0026lt; .05 as the cutoff criterion considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of protein-protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online database InnateDB interactome (https://www.innatedb.com/)\u003csup\u003e20\u003c/sup\u003e was used to construct \u0026nbsp;PPI network and module analysis. Cytoscape (version 3.8.1) (www.cytoscape.org/) \u003csup\u003e21\u003c/sup\u003e was used to visualize the PPI network of DEGs. Next, the Network Analyzer plugin for Cytoscape was applied to calculate node degree\u003csup\u003e22\u003c/sup\u003e, betweenness centrality\u003csup\u003e23\u003c/sup\u003e, stress centrality\u003csup\u003e24\u003c/sup\u003e and closeness centrality\u003csup\u003e25\u003c/sup\u003e. The plug-in PEWCC1\u003csup\u003e26\u003c/sup\u003e of Cytoscape was applied to detect densely connected regions in PPI networks. The PPI networks were constructed using Cytoscape and the most significant module in the PPI networks was selected using PEWCC1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated regulatory network construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integrated regulatory network of miRNAs (microRNAs) and TFs (transcription factors) was constructed based on standardized integration of numerous high-throughput datasets. It granted a plan including a set of hub genes, miRNAs and TFs for analyzing multi-level regulation in DN. The miRNAs associated with DEGs were selected from miRNet (\u003ca href=\"https://www.mirnet.ca/\"\u003ehttps://www.mirnet.ca/\u003c/a\u003e)\u003csup\u003e27\u0026nbsp;\u003c/sup\u003eDatabase (The integration database of TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0), and TFs associated with DEGs were selected from NetworkAnalyst database (https://www.networkanalyst.ca/)\u003csup\u003e28\u003c/sup\u003e Database (The integration database of\u0026nbsp; JASPAR). The miRNA-DEG regulatory network and TF-DEG regulatory network were constructed by using Cytoscape software\u003csup\u003e21\u003c/sup\u003e, which is open source software for visualizing complex networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis of the hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic value of validated hub genes was assessed using receiver operating characteristic (ROC) curve analysis using the pROC in R with GLM prediction model\u003csup\u003e29\u003c/sup\u003e. An area under the curve (AUC) value was determined and used to nominate the ROC effect.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDN and normal control samples (28 and 9, respectively) were first analyzed. Limma was used to identify the DEGs. Following analysis of NGS dataset GSE142025, 549 DEGs (275 up regulated and 274 down regulated) genes were identified and are listed in Table 1. The volcano plot and heatmap are shown in Fig. 1 and Fig. 2, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology and pathway enrichment analysis of DEGs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe identified DEGs were uploaded to the online software ToppGene for GO and REACTOME pathway enrichment analyses and results are listed in Table 2 and Table 3. The results of the GO analysis revealed that up regulated genes were significantly enriched in BP, including cell activation and regulation of immune system process, whereas down regulated genes were significantly enriched in response to hormone and ion transport. In terms of CC, the up regulated genes were enriched in cell surface and intrinsic component of plasma membrane, whereas down regulated genes were enriched in integral component of plasma membrane and nuclear chromatin. In terms of MF, the up regulated genes were enriched in signaling receptor binding and identical protein binding, whereas down regulated genes were enriched in lipid binding and transporter activity. REACTOME pathway analysis revealed that the up regulated genes were highly associated with pathways including immunoregulatory interactions between a lymphoid and a non-lymphoid cells, and innate immune system, whereas down regulated genes were significantly enriched in biological oxidations and GPCR ligand binding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of protein-protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEG expression profiles in DN were constructed according to the information in the InnateDB interactome database. The PPI network of DEGs is consisted of 2718 nodes and 4477 edges (Fig. 3). There are 10 genes selected as hub genes, such as MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 are listed in Table 4. A two significant modules were obtained from PPI network of DEGs using PEWCC1, including 15 nodes and 36 edges \u0026nbsp;(Fig. 4A) and \u0026nbsp;7 nodes and 12 edges (Fig.\u0026nbsp;4B). Gene ontology and pathway enrichment analysis\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003erevealed that genes in these modules were mainly involved in innate immune system, tmmunoregulatory interactions between a lymphoid and a non-lymphoid cell, cell activation, regulation of immune system process, cell surface, response to hormone, cytokine signaling in immune system, metabolism of proteins and nuclear chromatin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated regulatory network construction\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe miRNA-DEG regulatory network had 8997 interactions (involving 1973 miRNAs and 248 DEGs) (Fig. 5). Moreover,\u0026nbsp;COL1A1\u0026nbsp;was targeted by 178 miRNAs (ex, hsa-mir-4492), \u0026nbsp; IRF4 was targeted by 140 miRNAs (ex, hsa-mir-4319), MYBL2 was targeted by 83 miRNAs (ex, hsa-mir-637), PRKCB was targeted by 81 miRNAs (ex, hsa-mir-1261), IL2RB was targeted by 54 miRNAs (ex, hsa-mir-4300), JUN was targeted by 144 miRNAs (ex, hsa-mir-3943), EGR1 was targeted by 132 miRNAs (ex, hsa-mir-548e-3p), ZFP36 was targeted by 130 miRNAs (ex, hsa-mir-6077), FOS was targeted by 105 miRNAs (ex, hsa-mir-5586-5p) and DUSP1 was targeted by 97 miRNAs (ex, hsa-mir-4458) are listed in Table 5. The TF-DEG regulatory network had 1954 interactions (involving 81 TFs and 250 DEGs) (Fig. 6). Moreover,\u0026nbsp;IRF4\u0026nbsp;was targeted by 10 TFs (ex, NFATC2),\u0026nbsp;LCK\u0026nbsp;was targeted by 10 TFs (ex, YY1),\u0026nbsp;RET\u0026nbsp;was targeted by 10 TFs (ex, NR2C2),\u0026nbsp;MAP1LC3C\u0026nbsp;was targeted by 10 TFs (ex, MAX),\u0026nbsp;IL2RB\u0026nbsp;was targeted by 8 TFs (ex, PDX1),\u0026nbsp;ATF3\u0026nbsp;was targeted by 19 TFs (ex, TP53),\u0026nbsp;EGR1\u0026nbsp;was targeted by 16 TFs (ex, ARID3A),\u0026nbsp;JUNB\u0026nbsp;was targeted by 15 TFs (ex, SRF),\u0026nbsp;FOS\u0026nbsp;was targeted by 13 TFs (ex, CREB1) and\u0026nbsp;PTPRO\u0026nbsp;was targeted by 13 TFs (ex, NR3C1) are listed in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis of the hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen hub genes are prominently expressed in DN; we performed a ROC curve analysis to evaluate their sensitivity and specificity for the diagnosis of DN. As shown in Fig. 7, MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 achieved an AUC value of \u0026gt;0.8, demonstrating that these hub genes have high sensitivity and specificity for DN diagnosis. The results suggested that MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 can be used as biomarkers for the diagnosis of DN.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDN affects millions of people all over the world\u003csup\u003e30\u003c/sup\u003e. DN occurs when the uncontrolled diabetes, which is a worldwide complaint. The aim of this investigation was to screen and verify hub genes involved in DN as well as to explore potential molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In the present study, NGS data from GSE142025 was extracted to identify the DEGs between DN and normal control. In this investigation, we identified 549 DEGs, which includes 275 up regulated and 274 down regulated genes. CFHR1\u003csup\u003e31\u003c/sup\u003e and RGS1\u003csup\u003e32\u003c/sup\u003e have been reported to altered expression in nephropathy. \u0026nbsp;Studies have showed that GREM1 is linked with progression of DN\u003csup\u003e33\u003c/sup\u003e.\u0026nbsp;Existing evidence has reported that\u0026nbsp;CCL19\u003csup\u003e34\u003c/sup\u003e is responsible for renal inflammation and fibrosis in DN. COL6A5\u003csup\u003e35\u003c/sup\u003e is associated with neuropathic chronic itch. Reports indicate that CIDEC (cell death inducing DFFA like effector c)\u003csup\u003e36\u003c/sup\u003e was found in progression of obesity. NR4A1 drives DN growth through mitochondrial fission and mitophagy\u003csup\u003e37\u003c/sup\u003e. Recent study has reported that expression of NR4A2 is associated with myocardial infarction\u003csup\u003e38\u003c/sup\u003e. EGR1 is required for fibrosis and inflammatory response in DN\u003csup\u003e39\u003c/sup\u003e. ATF3 expression has been implicated in DN\u003csup\u003e40\u003c/sup\u003e. Altered expression of NR4A3 contribute to type 2 diabetes mellitus progression\u003csup\u003e41\u003c/sup\u003e. A previous study showed that KLK1 gene is involved in \u0026nbsp;DN\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;A series of DEGs were discovered to be enriched in the GO functions and pathways. Previous investigation have shown a signaling pathway includes innate immune system\u003csup\u003e43\u003c/sup\u003e, extracellular matrix organization\u003csup\u003e44\u003c/sup\u003e,\u0026nbsp;\u0026nbsp;hemostasis\u003csup\u003e45\u003c/sup\u003e,\u0026nbsp;cytokine signaling in immune system\u003csup\u003e46\u003c/sup\u003e and metabolism of proteins\u003csup\u003e47\u003c/sup\u003e were associated with DN development.\u0026nbsp;Previous studies had reported that expression of SERPINA3\u003csup\u003e48\u003c/sup\u003e, IKZF1\u003csup\u003e49\u003c/sup\u003e, BTK (Bruton tyrosine kinase)\u003csup\u003e50\u003c/sup\u003e,\u0026nbsp;C1QA\u003csup\u003e51\u003c/sup\u003e, CD1C\u003csup\u003e52\u003c/sup\u003e and CCL13\u003csup\u003e53\u003c/sup\u003e were correlated with lupus nephritis. Recent studies have demonstrated that expression of TNFSF14\u003csup\u003e54\u003c/sup\u003e, ITGAL (integrin subunit alpha L)\u003csup\u003e55\u003c/sup\u003e,\u0026nbsp;PLAC8\u003csup\u003e56\u003c/sup\u003e, ADRA2A\u003csup\u003e57\u003c/sup\u003e, CCL21\u003csup\u003e58\u003c/sup\u003e, ALOX5\u003csup\u003e59\u003c/sup\u003e, CNR2\u003csup\u003e60\u003c/sup\u003e, COL1A1\u003csup\u003e61\u003c/sup\u003e, WNT7A\u003csup\u003e62\u003c/sup\u003e, SLAMF1\u003csup\u003e63\u003c/sup\u003e, CD3D\u003csup\u003e64\u003c/sup\u003e, LTF (lactotransferrin)\u003csup\u003e65\u003c/sup\u003e, MIR27B\u003csup\u003e66\u003c/sup\u003e, PDK4\u003csup\u003e67\u003c/sup\u003e, UCN3\u003csup\u003e68\u003c/sup\u003e, PCK1\u003csup\u003e69\u003c/sup\u003e, CEL (carboxyl ester lipase)\u003csup\u003e70\u003c/sup\u003e, TRPM6\u003csup\u003e71\u003c/sup\u003e, MTTP (microsomal triglyceride transfer protein)\u003csup\u003e72\u003c/sup\u003e, CYP2C8\u003csup\u003e73\u003c/sup\u003e and CYP3A4\u003csup\u003e74\u003c/sup\u003e are associated with progression of type 2 diabetes mellitus. Recent studies have proposed that the altered expression of MZB1\u003csup\u003e75\u003c/sup\u003e, LAIR1\u003csup\u003e76\u003c/sup\u003e, MIR142\u003csup\u003e77\u003c/sup\u003e and FAP (fibroblast activation protein alpha)\u003csup\u003e78\u003c/sup\u003e\u0026nbsp; have been shown to be a meaningful advance factor for myocardial infarction. IRF4 plays a key role in the obesity-induced insulin resistance\u003csup\u003e79\u003c/sup\u003e. Accumulating evidence showed that altered expression of \u0026nbsp;genes such as MDK (midkine)\u003csup\u003e80\u003c/sup\u003e, CCR2\u003csup\u003e81\u003c/sup\u003e, SAA1\u003csup\u003e82\u003c/sup\u003e, C3\u003csup\u003e83\u003c/sup\u003e, CD19\u003csup\u003e84\u003c/sup\u003e, CCR5\u003csup\u003e85\u003c/sup\u003e, CXCR3\u003csup\u003e86\u003c/sup\u003e, FABP4\u003csup\u003e87\u003c/sup\u003e, \u0026nbsp;GDF15\u003csup\u003e88\u003c/sup\u003e, IGF2\u003csup\u003e89\u003c/sup\u003e, IGFBP1\u003csup\u003e90\u003c/sup\u003e and IL6\u003csup\u003e91\u003c/sup\u003e are important in the progression of DN. A previous study has shown that UBASH3A\u003csup\u003e92\u003c/sup\u003e, SIRPG (signal regulatory protein gamma)\u003csup\u003e93\u003c/sup\u003e, IKZF3\u003csup\u003e94\u003c/sup\u003e, CD1D\u003csup\u003e95\u003c/sup\u003e, CD2\u003csup\u003e96\u003c/sup\u003e, CD48\u003csup\u003e97\u003c/sup\u003e, CD247\u003csup\u003e98\u003c/sup\u003e and CYP27B1\u003csup\u003e99\u003c/sup\u003e are liable for progression of type 1 diabetes mellitus. The studies have shown that expression of SIT1\u003csup\u003e100\u003c/sup\u003e, JAML (junction adhesion molecule like)\u003csup\u003e101\u003c/sup\u003e, TIMP1\u003csup\u003e102\u003c/sup\u003e, PRKCB (protein kinase C beta)\u003csup\u003e103\u003c/sup\u003e, MMP7\u003csup\u003e104\u003c/sup\u003e, \u0026nbsp;WNT7B\u003csup\u003e105\u003c/sup\u003e, WNT10A\u003csup\u003e106\u003c/sup\u003e, DUSP1\u003csup\u003e107\u003c/sup\u003e, WT1\u003csup\u003e108\u003c/sup\u003e, APOC3\u003csup\u003e109\u003c/sup\u003e, ERRFI1\u003csup\u003e110\u003c/sup\u003e, HCN2\u003csup\u003e111\u003c/sup\u003e, MME (membrane metalloendopeptidase)\u003csup\u003e112\u003c/sup\u003e, STRA6\u003csup\u003e113\u003c/sup\u003e, SLC12A3\u003csup\u003e114\u003c/sup\u003e and GC (GC vitamin D binding protein)\u003csup\u003e115\u003c/sup\u003e expedites epithelial to mesenchymal transition and renal fibrosis in DN. \u0026nbsp; Previous studies have found CFD (complement factor D)\u003csup\u003e116\u003c/sup\u003e, DOCK2\u003csup\u003e117\u003c/sup\u003e, LYZ (lysozyme)\u003csup\u003e118\u003c/sup\u003e, CD5L\u003csup\u003e119\u003c/sup\u003e, SCARA5\u003csup\u003e120\u003c/sup\u003e, VCAN (versican)\u003csup\u003e121\u003c/sup\u003e, GDF5\u003csup\u003e122\u003c/sup\u003e, SFRP2\u003csup\u003e123\u003c/sup\u003e, BTG2\u003csup\u003e124\u003c/sup\u003e, ZFP36\u003csup\u003e125\u003c/sup\u003e, GPR3\u003csup\u003e126\u003c/sup\u003e, OLR1\u003csup\u003e127\u003c/sup\u003e, PM20D1\u003csup\u003e128\u003c/sup\u003e and UGT2B7\u003csup\u003e129\u003c/sup\u003e to be expressed in obesity. A study has confirmed that altered expression of FCRL3\u003csup\u003e130\u003c/sup\u003e, FCGR2B\u003csup\u003e131\u003c/sup\u003e, COMP (cartilage oligomeric matrix protein)\u003csup\u003e132\u003c/sup\u003e, ERFE (erythroferrone)\u003csup\u003e133\u003c/sup\u003e and NPHS1\u003csup\u003e134\u003c/sup\u003e are involved in progression of nephropathy. \u0026nbsp;The expression of COL1A2\u003csup\u003e135\u003c/sup\u003e, LCK (LCK proto-oncogene, Src family tyrosine kinase)\u003csup\u003e136\u003c/sup\u003e, LCN2\u003csup\u003e137\u003c/sup\u003e and APOB (apolipoprotein B)\u003csup\u003e138\u003c/sup\u003e are key for progression of diabetic retinopathy. Researchers showed that\u0026nbsp;altered expression of \u0026nbsp;COL3A1\u003csup\u003e139\u003c/sup\u003e, PER1\u003csup\u003e140\u003c/sup\u003e, JUN (Jun proto-oncogene, AP-1 transcription factor subunit)\u003csup\u003e141\u003c/sup\u003e, SLC26A4\u003csup\u003e142\u003c/sup\u003e, F2RL3\u003csup\u003e143\u003c/sup\u003e, CYP4A11\u003csup\u003e144\u003c/sup\u003e and CYP4F2\u003csup\u003e145\u003c/sup\u003e play an important role in the hypertension. Collectively, results of enriched GO and REACTOME pathway enrichment analysis were positively correlated with experimental findings. However, further investigations are needed to explore and confirm the potentially significant pathways for DN and to achieve a comprehensive understanding of this process.\u003c/p\u003e\n\u003cp\u003eBased on the PPI network and module analysis, we obtained top hub genes in the whole network. \u0026nbsp;ALB (albumin)\u003csup\u003e146\u003c/sup\u003e\u0026nbsp; \u0026nbsp;has been shown as a promising biomarker in DN. In our study, correlations of MDFI (MyoD family inhibitor) and FOS (Fos proto-oncogene, AP-1 transcription factor subunit) with patient prognosis highlight the importance of these genes as novel biomarkers to stratify DN patients as well as potential therapeutic targets, but concrete roles of these genes need further investigation.\u003c/p\u003e\n\u003cp\u003eBased on the miRNA-DEG regulatory network and TF-DEG regulatory network, we obtained target in the whole network. \u0026nbsp;Recent investigation reported that the altered expression of MYBL2 was associated with myocardial infarction progression\u003csup\u003e147\u003c/sup\u003e, but this gene might be novel target for DN. Many investigation have reported that expression of \u0026nbsp;hsa-mir-637\u003csup\u003e148\u003c/sup\u003e and NR3C1\u003csup\u003e149\u003c/sup\u003e were linked with progression of hypertension, but these genes might be novel target for DN. Hsa-mir-1261\u003csup\u003e150\u0026nbsp;\u003c/sup\u003ehas been shown to have an important role in DN. Hsa-mir-4458\u003csup\u003e151\u003c/sup\u003e has been found to be differentially expressed in myocardial infarction, but this gene might be novel target for DN. The recent studies have reported that expression of \u0026nbsp;NFATC2\u003csup\u003e152\u003c/sup\u003e, PDX1\u003csup\u003e153\u003c/sup\u003e and CREB1\u003csup\u003e154\u003c/sup\u003e were involved in the progression of type 2 diabetes, but these genes might be novel target for DN. \u0026nbsp;Previous studies have demonstrated that expression of YY1\u003csup\u003e155\u003c/sup\u003e, TP53\u003csup\u003e156\u003c/sup\u003e\u0026nbsp; \u0026nbsp;and SRF (serum-response factor)\u003csup\u003e157\u003c/sup\u003e played a key role in progression \u0026nbsp;of DN. IL2RB, hsa-mir-4492, hsa-mir-4319, hsa-mir-4300, hsa-mir-3943, hsa-mir-548e-3p, hsa-mir-6077, hsa-mir-5586-5p, RET (ret proto-oncogene),\u0026nbsp;MAP1LC3C,\u0026nbsp;PTPRO (protein tyrosine phosphatase receptor type O), NR2C2, MAX (myc-associated factor X) and ARID3A might be novel diagnostic biomarkers associated with the progression of DN, which remains to be verified based on a larger sample.\u003c/p\u003e\n\u003cp\u003eBesides, this investigation is purely a bioinformatics analysis without any in vivo and in vitro data. There are some limitations in our investigation. The lack of molecular and cellular evidences in wet-lab experiment imposes restrictions on the conclusions that can be drawn from our results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn sum, based on a series of bioinformatics methods and a retrospective analysis, our identified 10 hub genes (MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1), which showed an intimate correlation with DN advancement and prognosis and had the potential acting as therapeutic targets and prognostic indicators. Further experiments are required to confirm the expression and potential functions of the identified key genes in DN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thank Weijia Zhang, Icahn School of Medicine at Mount Sinai, Renal, New York, USA, very much, the author who deposited their expression profiling by high throughput sequencing dataset, GSE142025, into the public GEO database. We sincerely thank the preprint research square for providing information online; it is our pleasure to acknowledge their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo informed consent because this study does not contain human or animals participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE142025) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142025]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.J - Methodology and validation\u003c/p\u003e\n\u003cp\u003eB.V - Writing original draft, and review and editing \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN .J - Software and resources \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.V- Investigation and resources\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarish Joshi \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ORCID ID:\u0026nbsp;0000-0002-3817-5194\u003c/p\u003e\n\u003cp\u003eBasavaraj Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ORCID ID:\u0026nbsp;\u003ca href=\"http://orcid.org/0000-0003-2202-7637?lang=en\" target=\"_blank\"\u003e0000-0003-2202-7637\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNidhi Joshi \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ORCID ID: 0000-0001-8067-3448\u003c/p\u003e\n\u003cp\u003eChanabasayya \u0026nbsp; Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ORCID ID:\u0026nbsp;\u003ca href=\"http://orcid.org/0000-0003-3615-4450\" target=\"_blank\"\u003e0000-0003-3615-4450\u003c/a\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePapadopoulou-Marketou N, Chrousos GP, Kanaka-Gantenbein C. 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Physiol Genomics. 2016;48(8):580-588. doi:10.1152/physiolgenomics.00058.2016\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the supplementary files.\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":"bioinformatics analysis,protein-protein interaction network,differentially expressed genes, diabetic nephropathy, novel biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-132705/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-132705/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe underlying molecular mechanisms of diabetic nephropathy (DN) have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of DN.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe downloaded next generation sequencing (NGS) dataset GSE142025 from Gene Expression Omnibus (GEO) database having 28 DN samples and 9 normal control samples. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed. Biological function analysis of the DEGs was enriched by GO and REACTOME pathway. Then we established the protein-protein interaction (PPI) network, modules, miRNA-DEG regulatory network and TF-DEG regulatory network. Hub genes were validated by using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 549 DEGs were detected including 275 up regulated and 274 down regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in cell activation, integral component of plasma membrane, lipid binding and biological oxidations. Analyzing the PPI network, miRNA-DEG regulatory network and TF-DEG regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 by the Cytoscape software. The ROC curve analysis confirmed that hub genes were of diagnostic value.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTaken above, using integrated bioinformatics analysis, we have identified key genes and pathways in DN, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for DN.\u003c/p\u003e","manuscriptTitle":"Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-09-19 17:33:43","doi":"10.21203/rs.3.rs-132705/v2","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}},{"code":1,"date":"2020-12-22 00:06:42","doi":"10.21203/rs.3.rs-132705/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":"September 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":15622395,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2020-12-22T00:06:43+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-19 17:33:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-132705","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-132705","identity":"rs-132705","version":["v2"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}