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Construction of an Mirna‑Gene Regulatory Network in Colorectal Cancer Through Integrated Analysis of Mrna and Mirna Microarrays

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Construction of an Mirna‑Gene Regulatory Network in Colorectal Cancer Through Integrated Analysis of Mrna and Mirna Microarrays MOLECULAR MEDICINE REPORTS 18: 5109-5116, 2018 Construction of an miRNA‑gene regulatory network in colorectal cancer through integrated analysis of mRNA and miRNA microarrays JUN HU, XIN YUE, JIANZHONG LIU and DALU KONG Department of Colorectal Cancer Surgery, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, P.R. China Received December 19, 2017; Accepted August 8, 2018 DOI: 10.3892/mmr.2018.9505 Abstract. The aim of the present study was to identify poten- Introduction tial biomarkers associated with colorectal cancer (CRC). The GSE32323 and GSE53592 mRNA and microRNA (miRNA) Colorectal cancer (CRC) is a common malignancy that ranks expression profiles were selected from the Gene Expression as the second leading cause of cancer-associated mortality in Omnibus database. The differentially expressed genes (DEGs) men and women in the USA (1). Despite improvements in CRC and differentially expressed miRNAs (DEMs) in CRC tissue therapy, CRC remains a major public health problem, and it is samples compared with surrounding control tissue samples estimated that there are 1,000,000 individuals suffering from (DEGs-CC), and DEGs in cells treated with 5-aza-2'-de- CRC worldwide, with the mortality rate is as high as ~50% in oxycitidine compared with untreated cells (DEGs-TC) certain developed countries (2). The tumor stage is the most were identified with the Limma package. The Database for important prognostic indicator for CRC. However, the tumors Annotation, Visualization and Integrated Discovery was used are often diagnosed at an intermediate or late stage, and the to conduct the functional and pathways enrichment analysis. pathological staging cannot accurately predict recurrence (3). Differential co-regulation networks were constructed using An immunochemical test has been used in CRC screening, the DCGL package of R. The targets of DEMs were identi- which is considered more useful than colonoscopy in the fied using TargetScan. The overlaps between DEGs and the Chinese population, and is less invasive and more accurate than targets were selected. The miRNA-gene regulatory network colonoscopy (4,5). The progression of CRC is a complex multi- of the overlaps was established. There were 145 DEMs, and gene, multistep, multistage process involving certain specific 1,284 DEGs in DEGs-CC, and 101 DEGs in DEGs-TC. molecular alterations. A number of genes and pathways have DEGs-CC were enriched in 196 Gene Ontology (GO) terms been revealed to be involved the occurrence and development and 23 Kyoto Encyclopedia of Genes and Genomes (KEGG) of CRC. For example, mutations of tissue inhibitor of metal- pathways. DEGs-TC were enriched in 46 GO terms and two loproteinases 2 and metalloproteinases are associated with the KEGG pathways. A differential co-regulation network of tumorigenesis and certain biological behaviors of CRC (6). the DEGs and a miRNA-gene regulatory network between The activation of Wnt/β-catenin signaling occurs in the DEMs and overlapped DEGs were respectively constructed. majority of cases of CRC, and activation of this pathway is an miR‑124‑3p, miR‑145‑5p and miR‑320a may be critical in CRC, early event in CRC tumorigenesis (7). However, the molecular and serum/glucocorticoid regulated kinase 1 and SRY-box 9 mechanisms associated with CRC require further investiga- may be potential biomarkers for CRC tumor progression. tion, and it is important to identify novel biomarkers that may guide the diagnosis and therapy of CRC. MicroRNAs (miRNAs) post-transcriptionally regulate the expression of numerous genes. miRNAs can silence gene expression by binding to the 3'untranslated regions (3'-UTRs) Correspondence to: Dr Dalu Kong, Department of Colorectal of a target mRNA, resulting in gene degradation or translation Cancer Surgery, National Clinical Research Center for Cancer, Key termination (8). Increasing evidence indicates that miRNAs Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University are crucial in several types of cancer. miRNAs can regulate Cancer Institute and Hospital, 24 Huan-Hu-Xi Road, Ti-Yuan-Bei, cell growth, cell death, cell proliferation and differentiation, He Xi, Tianjin 300060, P.R. China in addition to tumorigenesis. Several signaling pathways and E-mail: [email protected] genes are reported as regulatory targets of miRNAs in cancer, including B-cell lymphoma 2 apoptosis regulator and sirtuin 1 Key words: colorectal cancer, bioinformatics, microRNA-gene genes in breast cancer (9), and KRas and Notch pathways in regulatory network, differential co-regulation network pancreatic cancer (10). Furthermore, miRNAs may also be useful as cancer biomarkers and treatment targets. However, 5110 HU et al: IDENTIFICATION OF KEY miRNAs AND GENES REGULATING THE PROCESSES OF COLORECTAL CANCER the specific regulatory mechanism of miRNAs in CRC Table I. Top 20 differentially expressed miRs in colorectal remains to be fully elucidated. cancer tissue samples compared with surrounding control In the present study, bioinformatics methods were used tissue samples. to identify differentially expressed genes (DEGs) and differ- entially expressed miRNAs (DEMs) in CRC tissue samples miR ID P-value LogFC compared with non-cancerous samples. The construction of -05 the differential co-regulation network and miRNA-gene regu- miR-195-5p 3.62x10 -4.04 -04 latory network may assist in improving current understanding miR-302c-5p 1.52x10 3.22 of the regulatory mechanisms of miRNAs in CRC. miR-4328 2.71x10-04 -3.75 miR-28-3p 2.80x10-04 -4.90 Materials and methods miR-186-5p 3.11x10-04 -4.28 miR-320a 3.18x10-04 -2.85 Microarray data. The mRNA expression and miRNA profiles miR-30b-5p 3.99x10-04 -3.68 of the GSE32323 (11) and GSE53592 datasets were respec- miR-101-3p 5.02x10-04 -2.89 tively downloaded from the Gene Expression Omnibus (GEO; miR-30c-5p 5.30x10-04 -2.11 http://www.ncbi.nlm.nih.gov/geo/) database. GSE53592 miR-140-3p 6.33x10-04 -2.42 consisted of data from six samples, three CRC tissue samples miR-145-5p 7.54x10-04 -3.34 and three surrounding control tissue. The miRNA expres- -04 sion profile was detected using the GPL8786 [miRNA‑1_0] miR-143-3p 8.34x10 -5.10 -04 Affymetrix miRNA Array platform (Affymetrix, Inc., Santa miR-378e 8.40x10 -4.35 -04 Clara, CA, USA; http://www.affymetrix.com/analysis/index. miR-708-5p 9.01x10 4.26 affx). The mRNA dataset GSE32323 contained 44 samples, miR-125b-5p 9.56x10-04 -3.67 including 17 pairs of cancer and non-cancerous tissue samples miRPlus-C1066 1.08x10-03 3.12 from patients with CRC, five pairs treated with 5‑aza‑2'‑de- miR-320b 1.11x10-03 -2.08 oxycitidine and untreated cell line samples. Gene expression miR-3158-5p 1.29x10-03 2.26 profiles were measured using the GPL570 [HG‑U133_Plus_2] miR-1973 1.31x10-03 -2.18 Affymetrix Human Genome U133 Plus 2.0 Array (Affymetrix, miR-4748 1.49x10-03 3.47 Inc.; http://www.affymetrix.com) platform. miR, microRNA; FC, fold change. Identification of DEMs and DEGs.For the mRNA dataset, the raw data were background corrected and normalized using the affy package version 1.58.0 (https://bioconductor.org/pack- ages/release/bioc/html/affy.html) in R version 2.10.0 (12). genes were selected. P<0.05 was used as the threshold to select If more than one probe corresponded to only one gene, the the enriched GO terms and KEGG pathways. average expression value of these probes was considered as the expression value of the gene. The DEMs and DEGs in Construction of the differential co‑regulation network. DCGL the CRC tissue samples compared with surrounding control is an R package for screening differentially co-expressed tissue samples (DEGs-CC) and the DEGs in cell samples genes (DCGs) and differentially co-expressed links treated with 5-aza-2'-deoxycitidine compared with untreated (DCLs). It analyzes the expression correlation based on the samples (DEGs‑TC) were identified using the limma package exact co-expression changes to distinguish the significant (http://bioconductor.org/packages/release/bioc/html/limma. co-expression changes and relatively minor ones. In the html) of R. The DEMs were identified according to the following present study, differential co‑regulation pairs were identified criteria: false discovery rate (FDR) corrected P-value of via DCGL version 2.1.2 in the CRC tissue samples compared P<0.05 and |log2(fold change)| >1. The screening criteria for the with non-cancerous tissue samples, and the 5-aza-2'-deoxycit- DEGs was |log2(fold change)| >1 and Benjamini and Hochberg idine-treated cell line samples compared with the untreated corrected P-value of P<0.05. Hierarchical clustering analysis cell line samples, and a differential co-regulation network was of CRC tissue samples and non-cancerous samples based on constructed based on the data. the expression value of these DEGs was performed. The DEGs in cell samples treated with 5-aza-2'-deoxycitidine compared Construction of the miRNA‑gene regulatory network. The with untreated samples were identified via the limma package targets of the DEMs were identified based on the TargetScan with the criteria of P<0.05 and |log2(fold change)| >0.5. version 6.2 (http://www.targetscan.org/) database. The overlaps between the DEGs and the targets of DEMs were selected. The Functional and pathway enrichment analysis. The Database for miRNA-gene regulatory network was established and visual- Annotation, Visualization and Integrated Discovery (DAVID; ized using Cytoscape software version 3.1.0 (http://www. https://david.ncifcrf.gov/) is a widely used web-based tool for cytoscape.org/).
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