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Downloaded from the Gene Expression Omnibus (GEO) Database 858 Original Article Identification of potential biomarkers and pathways in ulcerative colitis with combined public mRNA and miRNA expression microarray data analysis Lili Yang1,2#, Yaoyao Bian3#, Zhengjun Li4#, Yan Yan5, Junyi Li1, Wenlin Li2, Li Zeng1,2 1School of First Clinical Medicine, 2Jingwen Library, 3School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, China; 4Management School, Lancaster University, Lancaster, LA1 4YW, UK; 5Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China Contributions: (I) Conception and design: L Yang, Y Bian; (II) Administrative support: None; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: Y Bian; (V) Data analysis and interpretation: Z Li, J Li; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Li Zeng. School of First Clinical Medicine; Jingwen Library, Nanjing University of Chinese Medicine, Nanjing 210023, China. Email: [email protected]. Background: Ulcerative colitis (UC) is a chronic, relapsing and non-specific inflammatory disease, involving various genes and pathways in their pathogenesis. Increasing evidences have showed that microRNAs (miRNAs) act as key post-transcriptional regulators of gene expression in UC. This current study aimed to identify key miRNAs, potential target genes, and relevant pathways involved in UC to uncover their underlying molecular mechanisms by using bioinformatics analysis. Methods: The mRNA and miRNA expression profiles were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by using the R software package. Results: A total of 79 DEGs and 47 DEMIs were obtained. And a panel of miRNAs and their target mRNAs were identified. It showed that miR-1231 may be a key regulator for DUOX2 and TFF1. CCL11 may be potentially targeted by miR-625. MMP1 may play vital roles in the development of UC by regulating the miR-1228/PPAR signaling pathway. In addition, we validated the most significantly up/down-expressed miRNAs (miR-92b, miR-625) and two of their corresponding target mRNAs (AQP8 and TAGAP, CCL11 and CHI3L1) in colon tissues of UC models preliminarily. The results were consistent with the microarray analysis. Conclusions: These findings may provide new insights into representing key mechanisms associated with the development of UC. Keywords: Ulcerative colitis (UC); microRNAs (miRNAs); differentially expressed genes (DEGs); bioinformatics analysis; functional enrichment analysis Submitted Mar 06, 2019. Accepted for publication Jun 03, 2019. doi: 10.21037/jgo.2019.06.06 View this article at: http://dx.doi.org/10.21037/jgo.2019.06.06 © Journal of Gastrointestinal Oncology. All rights reserved. J Gastrointest Oncol 2019;10(5):847-858 | http://dx.doi.org/10.21037/jgo.2019.06.06 848 Yang et al. Potential biomarkers and pathways of UC Introduction In this study, we retrieved datasets of mRNA and miRNA expression microarrays from the Gene Expression Ulcerative colitis (UC), a subtype of inflammatory bowel Omnibus (GEO), and we identified a group of key miRNAs disease (IBD), is a chronic, relapsing and non-specific and potentially target genes involved in UC by using inflammatory disease, which is confined to the mucosa and bioinformatics analysis. In addition, the most significantly submucosa of the rectum or colon. It is characterized by two expressed miRNAs and their target genes were selected and periods of active disease and remission. The typical clinical conducted for preliminary validation by real-time qPCR manifestations are diarrhea, purulent stool and abdominal (qRT-PCR). The study aimed to suggest miRNA signatures pain (1). useful for active UC detection and diagnosis, as well as UC afflicts millions of people worldwide. The highest explore the underlying pathogenesis by identifying potential incidences of UC in North America and Northern Europe miRNA-targeted mRNAs at the molecular level. were 6–15.6 and 10–20.3 cases, respectively, per 100,000 annually (2). A recent review analysis of 44 studies that included 31,287 Asian patients found a 0.85% prevalence Methods rates of UC (3). Additionally, patient suffering from UC Collection and inclusion criteria of studies are at high risk of developing colorectal cancer (CRC) (4). UC has brought significant personal and societal burden. The mRNA microarray expression profile datasets were Furthermore, active UC can decrease the physical and retrieved and downloaded from the GEO database (available mental quality of life and increase psychological distress. online: http://www.ncbi.nlm.nih.gov/geo) by searching the MicroRNAs (miRNAs), a group of small non-coding following key words: ‘RNA’, ‘active ulcerative colitis’, and RNA molecules, act as key post-transcriptional regulators ‘Homo sapiens’ (organism). The inclusion criteria were as of gene expression. The dysregulated miRNA expression follows: (I) colon tissues from adult patients with active UC can drive the onset of various diseases and even contribute (not cells); (II) samples in UC group without receiving any to oncogenesis or tumor progression. miRNAs have interventions or treatments; and (III) both numbers of UC cytoprotective effect on tissue as protectomiRs (5), and play a and health control sample ≥12. Besides, the miRNA profile pivotal role in the polarization of tumor-related macrophages datasets were searched by using the keywords: ‘miRNA’, in inflammatory microenvironment (6). Moreover, miRNAs ‘active ulcerative colitis’, and ‘Homo sapiens’ (organism) function of inflammation and autoimmunity in regulating according to the following criteria: (I) colon tissues from the pathogenesis of UC (7). Mounting gene expression active UC patients (not cells) and (II) samples in UC group studies performed in UC both in colonic mucosa and without receiving any interventions or treatments. After peripheral blood have proposed various hypothesis, that is, screening, two mRNA expression datasets [GSE53306 (16) cell adhesion, immune response and tissue remodeling (8). and GSE65114] were selected, and one miRNA expression Numerous genes and pathways are correlated with dataset (GSE43009) was obtained for analysis. The the genesis and progression of UC. For example, high workflow of data-processing and analysis is presented in expression levels of Caspase 1 (CASP1) and lysozyme Figure 1. (LYZ) have been found in UC group (9). Various genes, such as CD177 Molecule (CD177), G protein-coupled Microarray data receptor 84 (GPR84) (10), interleukin 8 (IL-8), matrix metallopeptidase 9 (MMP-9) (11), and 8-Oxoguanine DNA In this study, the platform for GSE53306 was based on glycosylase (OGG1) (12), participate in molecular pathways the GPL14951 Illumina HumanHT-12 WG-DASL V4.0 of UC. Also, various signaling pathways, such as nuclear R2 expression beadchip, which consisted of 16 active UC transcription factor kappa B (NF-κB) pathway (13), nuclear samples and 12 controls. The platform for GSE65114 factor erythroid 2-related factor 2 (Nrf2) pathway (14), was GPL16686 (HuGene-2_0-st) Affymetrix human gene and JAK/STAT signaling pathway (15) are related to 2.0 ST array, which included 16 active UC samples and progress of UC. However, knowledge of the genome scale 12 controls. The platform for GSE43009 was based on of miRNAs and their target genes, as well as the potential GPL16384 Affymetrix multispecies miRNA-3 array, which biological function of UC, remains limited. consisted of five controls and five UC samples. © Journal of Gastrointestinal Oncology. All rights reserved. J Gastrointest Oncol 2019;10(5):847-858 | http://dx.doi.org/10.21037/jgo.2019.06.06 Journal of Gastrointestinal Oncology, Vol 10, No 5 October 2019 849 Data collection from GEO datasets mRNA datasets miRNA dataset GSE53306 GSE65114 GSE43009 R analysis the differential R analysis the differential R analysis the differential mRNAs (P<0.05) mRNAs (P<0.05) miRNAs (adj.P<0.05) Predict the target mRNAs The overlapping expressed mRNAs GO annotation and KEGG PPI construction and module Comprehensive analysis of enrichment analysis miRNAs and targeted-mRNAs Figure 1 Flow sheet of data-processing and analysis. Data-processing and differentially expressed genes (DEGs)/ enrichment analysis was performed for presenting the differentially expressed miRNAs (DEMI) identification systematic analysis, annotation, and visualization of gene functions. Both GO enrichment and KEGG pathway The raw data were downloaded from the GEO database analysis were conducted using the Database for Annotation, and then normalized and standardized by using the R Visualization and Integrated Discovery (DAVID) software package. Gene differential expression analysis was (available online: http://david.abcc.ncifcrf.gov/) to identify conducted through the limma packages in the Bioconductor the biological significance of genes, when P<0.05 was package (17) (available online: http://www.bioconductor. considered statistically significant. org/). The heat maps of the two mRNA datasets were mapped by using the gplots package in R to visualize the expression values of genes in the different samples. When Protein-protein interaction (PPI) network construction we selected the DEGs, P<0.05 and |log FC| >1 were and module selection considered as the cutoff values, where FC is fold change. The PPI network of DEGs was mapped using the Search Significant DEMIs
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