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Bioinformatic Gene Analysis for Potential Therapeutic Targets Of Xiang et al. J Transl Med (2020) 18:388 https://doi.org/10.1186/s12967-020-02549-9 Journal of Translational Medicine RESEARCH Open Access Bioinformatic gene analysis for potential therapeutic targets of Huntington’s disease in pre-symptomatic and symptomatic stage Chunchen Xiang1†, Shengri Cong1†, Bin Liang2 and Shuyan Cong1* Abstract Background: Huntington’s disease (HD) is a neurodegenerative disorder characterized by psychiatric symptoms, serious motor and cognitive defcits. Certain pathological changes can already be observed in pre-symptomatic HD (pre-HD) patients; however, the underlying molecular pathogenesis is still uncertain and no efective treatments are available until now. Here, we reanalyzed HD-related diferentially expressed genes from the GEO database between symptomatic HD patients, pre-HD individuals, and healthy controls using bioinformatics analysis, hoping to get more insight in the pathogenesis of both pre-HD and HD, and shed a light in the potential therapeutic targets of the disease. Methods: Pre-HD and symptomatic HD diferentially expressed genes (DEGs) were screened by bioinformatics analy- sis Gene Expression Omnibus (GEO) dataset GSE1751. A protein–protein interaction (PPI) network was used to select hub genes. Subsequently, Gene Ontology (GO) enrichment analysis of DEGs and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of hub genes were applied. Dataset GSE24250 was downloaded to verify our hub genes by the Kaplan–Meier method using Graphpad Prism 5.0. Finally, target miRNAs of intersected hub genes involved in pre- HD and symptomatic HD were predicted. Results: A total of 37 and 985 DEGs were identifed in pre-HD and symptomatic HD, respectively. The hub genes, SIRT1, SUZ12, and PSMC6, may be implicated in pre-HD, and the hub genes, FIS1, SIRT1, CCNH, SUZ12, and 10 others, may be implicated in symptomatic HD. The intersected hub genes, SIRT1 and SUZ12, and their predicted target miR- NAs, in particular miR-22-3p and miR-19b, may be signifcantly associated with pre-HD. Conclusion: The PSMC6, SIRT1, and SUZ12 genes and their related ubiquitin-mediated proteolysis, transcriptional dysregulation, and histone metabolism are signifcantly associated with pre-HD. FIS1, CCNH, and their related mito- chondrial disruption and transcriptional dysregulation processes are related to symptomatic HD, which might shed a light on the elucidation of potential therapeutic targets in HD. Keywords: Bioinformatics analysis, Biomarkers, miRNAs, Pre-symptomatic HD Background Huntington’s disease (HD) is an inherited neurodegen- erative disorder characterized by progressive motor and *Correspondence: [email protected] cognitive defcits [1]. Te pathogenesis of HD is associ- †Chunchen Xiang and Shengri Cong contributed equally to this work ated with the abnormal accumulation of mutant hunting- 1 Department of Neurology, Shengjing Hospital of China Medical tin (mHtt), a protein with expansion of CAG repeats at University, 36 Sanhao Street, Heping District, Shenyang 110004, Liaoning, People’s Republic of China the amino-terminus of wild-type huntingtin (Htt) [2]. Full list of author information is available at the end of the article Currently, efective therapies are lacking for HD [3]. In © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Xiang et al. J Transl Med (2020) 18:388 Page 2 of 10 addition, neuronal degeneration can be observed in pre- was used to analyze the GSE1751 raw expression data. symptomatic patients as early as 20 years prior to diagno- Background correction, quantile normalization, and sis [4, 5], indicating that therapeutic intervention could log2-transformation were performed to create a robust occur several years before the manifestation of clinical multi-array average (RMA) and a log-transformed per- symptoms. fect match. Te Benjamini–Hochberg method was used Prediction results of gene expression profling and to adjust original p-values, and the false discovery rate bioinformatics analysis have become increasingly useful (FDR) procedure was used to calculate fold changes in the diagnosis and therapy of various diseases [6–8], (FC). Te DEGs between symptomatic HD (GSM30530 including those of the nervous system [9]. In addition to to GSM30541) and healthy control (GSM30580 to being used to identify the functional connections among GSM30593) groups and between pre-symptomatic HD genes in an unbiased manner for the in-depth study of (GSM30542 to GSM30546) and healthy control groups biological processes, it can also be used to predict up- were evaluated. Te diferential gene expression thresh- and downstream genes and to explore the relationship old was log2 fold change > 2 and p-value < 0.05. between gene expression and disease phenotypes [10, 11]. Integration of the protein–protein interaction (PPI) MicroRNAs (miRNAs, 19–24 nucleotides in length) network and screening of the hub genes are the most abundant and representative small non- Te Search Tool for the Retrieval of Interacting Gene coding RNAs, which negatively regulate messenger RNA (STRING) database [18] was used to construct the PPI (mRNA) levels by binding to the 3′-untranslated region network of the DEGs in the two groups. Subsequently, [12, 13]. Numerous studies have described dysregulation the Cytoscape [19] software was used to build a PPI of miRNAs in neurodegenerative diseases, indicating that network, employing the Molecular Complex Detection miRNAs may useful in many diseases, including HD [14, (MCODE) plug-in to screen PPI network modules for 15]. these two groups of DEGs. Te cluster method (No. 14) In the present study, the gene expression profle, was used to cluster the modules, and the genes with an GSE1751, from the GEO database was reanalyzed with MCODE score ≥ 2 were selected as hub genes. respect to HD-related diferentially expressed genes (DEGs) in whole blood samples between symptomatic Gene Ontology and KEGG pathway analysis of DEGs HD patients, pre-symptomatic HD (pre-HD) individu- Database for Annotation, Visualization, and Integrated als, and healthy controls using bioinformatics analysis. Discovery (DAVID) (https ://david .abcc.ncifc rf.gov/) [20] Hub genes were selected for deeper functional analysis by is a gene functional annotation tool that is helpful for constructing a protein–protein interaction (PPI) network understanding biological functions. GO (Gene Ontol- and used to predict their related miRNAs. As a result, ogy) function enrichment analysis of the DEGs in the two sets of genes and miRNAs relating to the pathogenesis groups was performed, and those with a p-value < 0.05 of HD were identifed, increasing our understanding of were considered signifcantly enriched. Subsequently, the disease process and shedding light on markers for the the Kyoto Encyclopedia of Genes and Genomes (KEGG) treatment of HD. database was employed to elucidate the KEGG pathways of the hub genes in the two groups. Methods Data acquisition Prediction of the miRNAs of the intersected hub genes Te gene expression profle, GSE1751, from the GEO and their validation database was downloaded (https ://www.ncbi.nlm.nih. Finally, these hub genes were input into Targetscan [21] gov/geo, May 18, 2017). GSE1751 is based on the GPL96 (https ://www.targe tscan .org/vert_71/) and miRDB [22] platform (Afymetrix Human Genome U133 Plus 2.0 (https ://www.mirdb .org/) to predict their possible miR- Array) and contains a total of 31 peripheral whole blood NAs. Te intersection portion of the two databases was samples, including 12 symptomatic HD cases, 5 pre- selected as our predicted miRNAs. Moreover, based on symptomatic HD cases, and 14 healthy controls [16]. the information in the individual MCODE modules, the People who are carriers of the HD mutation but have no node with the highest score was selected as the hub gene clinically present signs or symptoms are considered pre- in GSE1751. Each hub gene was also found in the inde- symptomatic cases [3]. pendent datasets (dataset GSE24250, HD samples n = 8, and HC samples n = 6) based on the downloaded raw Screening of diferentially expressed genes (DEGs) data fles, including the gene expression level, survival Te R package, “limma” (https ://cran.r-proje ct.org/), time, and survival state. Expression levels were divided obtained from https ://www.bioco nduct or.org [17], into two groups, the HD group and the control group, Xiang et al. J Transl Med (2020) 18:388 Page 3 of 10 according to X-tile [23]. Te Kaplan–Meier method was p-value: 6.01E−06), and viral transcription (Fold Enrich-
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