Hindawi BioMed Research International Volume 2018, Article ID 2617432, 14 pages https://doi.org/10.1155/2018/2617432

Research Article Aberrantly Expressed and miRNAs in Slow Transit Constipation Based on RNA-Seq Analysis

Shipeng Zhao ,QiangChen , Xianwu Kang, Bin Kong, and Zhuo Wang

Department of Second Anorectal Section, Te Tird Hospital of Hebei Medical University, Shijiazhuang 050051, Hebei, China

Correspondence should be addressed to Shipeng Zhao; zhaoshipeng [email protected] and Qiang Chen; [email protected]

Received 12 April 2018; Revised 26 June 2018; Accepted 18 July 2018; Published 14 August 2018

Academic Editor: Koichiro Wada

Copyright © 2018 Shipeng Zhao et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Tis study aims to identify the key genes and miRNAs in slow transit constipation (STC). Methods.MRNAand miRNA expression profling were obtained. Diferentially expressed genes (DEGs) and miRNAs were identifed followed by the regulatory network construction. Functional annotation analysis and -protein interaction (PPI) network were conducted. Te electronic validation was performed. Results. Hsa-miR-2116-3p, hsa-miR-3622a-5p, hsa-miR-424-5p, and hsa-miR-1273-3p covered most DEGs. HLA-DRB1, HLA-DRB5, C3, and ICAM were signifcantly involved in staphylococcus aureus infection. Te PPI network generated several hub including ZBTB16, FBN1, CCNF, and CDK1. Electronic validation of HLA-DRB1, PTGDR, MKI67, BIRC5, CCNF, and CDK1 was consistent with the RNA-sequencing analysis. Conclusion. Our study might be helpful in understanding the pathology of STC at the molecular level.

1. Introduction treatment failure is a common phenomenon [9]. Terefore, exploring the underlying pathology mechanism is urgent. In Constipation is a gastrointestinal disorder characterized physiological conditions, colon motor activity increases afer by infrequent bowel movements, defecation difculty, and meals and wake up and decreases during sleep [10]. And STC incomplete bowel evacuation sensation [1–3]. Constipation is characterized by prolonged transit time of the stool through is more widespread in women and is ofen increased with the colon on account of the reduced colonic transit rate [11]. age [4, 5]. It is reported that constipation is associated Maybe, altered colonic function plays an important role in with infammatory bowel disease, irritable bowel syndrome, patients with STC. and relevant comorbidities [6]. Additionally, medications, To our knowledge, expression profling of mRNAs and mobility loss, gut aging alterations, and rectal sensory-motor miRNAs in STC colon tissue through RNA-sequencing has dysfunction can cause constipation. It is worth mentioning not been reported. In this study, we used high-throughput that slow transit constipation (STC) is the prototype of RNA-sequencing to study the mRNA and miRNA expression functional constipation and frequently appears in general profling and investigated the functional signifcance of aber- medicine and clinical practice (such as gastroenterology and rantly expressed genes and miRNAs in STC. geriatrics). Bharucha AE et al. found that STC was related to endocrine and metabolic disorders including hypercalcemia, hypothyroidism, or diabetes mellitus [7]. 2. Methods Recently, the underlying pathophysiology of STC is poor to predict and physiological assessment of gastrointestinal 2.1. Patients and Samples. In this study, we selected patients tract transit is the common diagnostic method. Taking with slow transit constipation (STC) strictly according to laxative remains the primary treatment option in severe international or domestic standard. Tose patients with STC [3, 8] and patients may take laxative for many years drug-induced constipation, other diseases (parkinson, type in many instances. Despite advances in therapy of STC, 1 diabetes, rheumatoid arthritis, systemic sclerosis, systemic 2 BioMed Research International

Table 1: Te basic characteristic of patients with STC and control individuals.

Preoperative Patient/Control Age Gender Time (years) Histological feature defecate frequency (time/day) 1 (Patient) 59 Female 30 STC 1/4 2 (Patient) 48 Female 30 STC 1/5-15 3 (Patient) 61 Male 1 STC 1/4 4 (Control) 69 Male N/A Normal 3-4/1 5 (Control) 47 Male N/A Normal 5-6/1 6 (Control) 64 Female N/A Normal 6-7/1 lupus erythematosus) induced STC, large intestine organic RNA-sequencing was translated into raw FASTQ sequence disease, giant colon or colorectal neoplasms, constipation data by using Base-Calling. Quality control of FASTQ data patients with defecation disorder, normal transit, and mixed (Read QC) was performed by using FastQC v0.11.4. To obtain types were excluded. For normal control group, only those the high quality clean data, the low quality sequences (adaptor individuals with no defecation disorder, anus and rectum sequences, sequences with a quality score <20, and sequences organic disease, history of constipation, or the laxative use with N base rate of raw reads >10%) were removed from were included. Finally, 3 patients (2 women and 1 man) sequencing data of mRNAs and miRNAs by using cutadapt were diagnosed as slow transit constipation (STC) and 3 v1.9.1. patients with colon cancer were enrolled into our study. Te mean age of 3 STC patients was 56 years (range: 2.4. Identifcation of DEGs in STC. According to human 48-61). Te lef half colon tissue of in STC patients and UCSC genome reference annotation, the alignment between lef half paracarcinoma colon tissue in patients with colon cleaned mRNA sequencing reads was aligned to the human cancer were obtained through surgical resection. Te clinical genome (GRCh38.p7 assembly) via Tophat (http://tophat information of patients with STC and control individuals was .cbcb.umd.edu/). Te fragment was performed to assemble shown in Table 1. and relative expression of the reads with the normalized In addition, our study was approved by the ethics RNA-sequencing fragment was counted by using Cufinks committee of the local hospital and informed written (http://cufinks.cbcb.umd.edu/). Fragments per kilobase of consent was obtained from all patients. And colon tissue of exon per million mapped reads (FPKM) were calculated STC patients and paracarcinoma colon tissue of colon cancer by Cufdif (http://cufinks.cbcb.umd.edu/) to determine patients were obtained based on the Helsinki Declaration. the transcription abundance of mRNAs. Paired t-tests were performed to test the expression between STC and controls. 2.2. RNA Isolation and Sequencing. Total RNA was isolated DEGs in STC compared to controls were identifed with from samples by using the Trizol reagent (Invitrogen, Carls- � value < 0.05 and abs (log2 (fold change)) > 1. Te heat bad, CA, USA). Te Nanodrop ND-2000 spectrophotometer map of top 100 DEGs in STC was obtained by heatmap.2 (Termo Scientifc, Wilmington, DE, USA) was used to (http://www.bioconductor.org/packages/release/bioc/html/ check the RNA concentration and purity. 1.5% agarose gel heatmaps.html). electrophoresis was applied to test the integrity of RNA. Ten, RIN value (RIN>7) was obtained through an Agilent 2100 2.5. Identifcation of Diferentially Expressed miRNAs in Bioanalyzer. Te messenger RNA (mRNA) was purifed by STC. Based on UCSC reference annotation, oligo-d (T) probes for polyA selection. And the purifed the alignment between cleaned miRNA sequencing reads mRNA was fragmented into size of 200bp by TruSeq RNA was aligned to the human genome (GRCh38.p7 assem- library preparation kit (Illumina, San Diego, CA, USA). Te bly) via bowtie (bowtie-bio.sourceforge.net). And miRD- complementary DNA (cDNA) was generated. End repair eep2 (https://www.mdc-berlin.de/8551903/en/) was applied and adapter ligation were performed afer purifcation by to determine the transcription abundance of miRNAs. Te QIAquick PCR purifcation kit. Te gel purifcation (2% TAE) diferentially expressed miRNAs in STC compared to controls was performed to isolate 300 nt fragments. Te mRNA library were calculated by using DESeq2 (http://bioconductor.org/ was constructed by QIAquick PCR and 18-30 nt RNA was packages/DEGseq/) and the threshold was defned as � value obtained from the total RNA. Adapter ligation and reverse < 0.05 and abs (log2 (fold change)) > 1. Te heat map of transcription PCR were performed to obtain the cDNA by diferentially expressed miRNAs in STC was obtained by TruseqTM Small RNA sample prep kit. Finally, sequencing heatmap.2 (http://www.bioconductor.org/packages/release/ was performed by A HiSeqTM 2500/Miseq platform (Illu- bioc/html/heatmaps.html). mina). 2.6. Network Construction of Diferentially Expressed miRNA 2.3. Quality Control of Raw Sequence Data. Te raw image Targets. Identifying target genes is an important step in data of mRNAs and miRNAs derived from high-throughput studying the function of miRNA in tissues. In this study, BioMed Research International 3

Table 2: Top 20 DEGs in STC.

Gene log2.fold change. test stat p value Up down CD248 1.90069 2.44353 5.00E-05 up GPC3 2.85031 2.85269 5.00E-05 up HBA2 2.94208 2.90606 5.00E-05 up MEDAG 2.96147 2.76909 5.00E-05 up NNMT 1.94212 2.35541 5.00E-05 up P2RX2 3.80911 1.88888 5.00E-05 up PCOLCE2 2.54104 2.50303 5.00E-05 up PI16 3.85702 3.77055 5.00E-05 up SLC6A19 2.55631 3.30198 5.00E-05 up ZBTB16 1.99184 2.33881 5.00E-05 up HLA-DRB5 -5.37709 -4.12938 5.00E-05 down YBX2 -2.14855 -2.67695 5.00E-05 down OLFM4 -2.49538 -2.20077 0.0001 down HLA-DRB1 -2.0477 -2.40945 0.0003 down FOS -1.63902 -1.96379 0.0005 down MKI67 -1.70928 -2.14918 0.00075 down MT1G -1.36954 -1.9624 0.00075 down HELLS -1.84715 -1.66191 0.00085 down PTGDR -1.73283 -1.82581 0.0013 down BIRC5 -1.59705 -1.83121 0.0015 down

the DEGs whose expression was inverse with correspond- identifed DEGs and diferentially expressed miRNAs in STC, ing diferentially expressed miRNAs were obtained and the Expression Omnibus (GEO, http://www.ncbi.nlm checked by six bioinformatic algorithms (RNA22, miRanda, .nih.gov/geo) database was used for electronic validation. miRDB, miRWalk, PICTAR2, and Targetscan). Tose targets We compared expression levels of selected DEGs and recorded by more than 4 algorithms were selected. And hsa-miRNAs between STC cases and normal controls and verifed targets were also obtained by miRWalk (http://www the diference of expression levels was showed by box- .umm.uni-heidelberg.de/apps/zmf/mirwalk/). According to plots. the diferentially expressed miRNA-target pairs, the STC- specifc diferentially expressed miRNA-targets interaction network was established by Cytoscape sofware (http://www 3. Results .cytoscape.org/). 3.1. DEGs and Diferentially Expressed miRNAs in STC. A total of 464 DEGs (357 upregulated and 107 upregulated 2.7. Functional Annotation of Diferentially Expressed miRNA genes) and 10 diferentially expressed miRNAs (4 upregulated Targets. In order to study the biological function of difer- and 6 downregulated miRNAs) were obtained. Heap maps of entially expressed miRNA-targets, the (GO) top 100 DEGs and all diferentially expressed miRNAs were and Kyoto Encyclopedia of Genes and Genomes (KEGG) displayed in Figures 1 and 2, respectively. Te top 20 DEGs pathway analysis were performed by using the online sof- ware GeneCodis (http://genecodis.cnb.csic.es/analysis). And were shown in Table 2. the threshold of FDR<0.05 was set as the criteria of statistical signifcance. 3.2. Diferentially Expressed miRNAs-Targets Network. A total of 87 diferentially expressed miRNAs-target pairs 2.8. Protein-Protein Interaction Analysis. In order to under- including 25 upregulated miRNA-downregulated target pairs stand the protein interaction between DEGs in STC, and 47 downregulated miRNA-upregulated target pairs BioGRID database was utilized to select interacting protein were obtained. Among which, there were 17 diferen- pairs. And the protein-protein network (PPI) was visualized tially expressed miRNAs-target pairs that have been con- by Cytoscape sofware (http://cytoscape.org/). In the net- frmed by miRWalk. Based on the STC-specifc diferen- work, nodes represent proteins and edges represent interac- tially expressed miRNAs-target interaction network (Fig- tion between two proteins. ure 3), hsa-miR-2116-3p (degree=27), hsa-miR-3622a-5p (degree=16), hsa-miR-424-5p (degree=15), and hsa-miR- 2.9. Validation the Expression of DEGs and Diferentially 1273-3p (degree=10) were the top four diferentially expressed Expressed miRNAs by GEO. In order to validate the miRNAs covered most DEGs. 4 BioMed Research International

mRNA_sampleLabel 2 mRNA_sampleLabel normal PPL SLC11A1 case CD248 1.5 P2RX2 ACKR3 FOSL1 SFRP2 1 SERPINE1 CILP SFRP1 KRT24 0.5 MEDAG VIT NOTCH3 MRC2 0 KLF15 GPC3 NTN1 PCOLCE2 −0.5 SERPINE2 PI16 IGFBP6 SHISA3 −1 SFRP4 ARHGAP23 IL6 CRABP2 −1.5 HBA2 HBA1 HBB EFEMP1 SNCAIP CPZ NNMT FBLN2 ZBTB16 ADAMTS1 CRISPLD2 CAPN6 DOC2B MDGA1 SCARA5 DUOX2 ADRA1B SLC6A19 SCNN1G CCL18 GPNMB HOXD13 DDIT4 FOS LCN2 CEL OLFM4 NR1H4 ASPG CYP2D6 HLA−DRB5 HLA−DRB1 DMBT1 MT1G MT1F ANKRD36 TTPA C1orf228 GRAPL LT B CD22 CKMT1A MYBL2 HELLS CLSPN TOP2A NUSAP1 MKI67 PRC1 MELK CENPF NUF2 PTGDR RRM2 IQGAP3 YBX2 SAMD5 PARPBP CENPK KIF11 CLDN15 KIAA0101 POC1A BIRC5 CDCA5 CDC20 UBE2C CDT1 AURKB CENPM BLM PRR11 NEK2 Figure 1: Hierarchical clustering analysis based on the expression profle of the top 100 DEGs in STC compared to normal tissues. Orange and light blue color mean the group of STC and normal tissues, respectively. Red color represents the relative expression level of genes which was higher than mean, and green color represents the relative expression of genes which was lower than mean.

3.3. Functional Annotation of Diferentially Expressed miR- and binding (FDR=0.00940741) were the most signifcantly NAs Targets. According to the GO enrichment analy- enriched GO terms in STC. Te enriched GO terms of sis, mitosis (FDR=0.0000884705), regulation of cell adhe- target DEGs were shown in Figures 4, 5, and 6, respectively. sion (FDR=0.00882985), negative regulation of smooth Cell adhesion molecules (FDR=0.00216279) involving four muscle cell-matrix adhesion (FDR=0.0228017), extracellu- DEGs (SDC2, HLA-DRB5, ICAM1, and HLA-DRB1), staphy- lar space (FDR=0.0000117125), centriole (FDR=0.00225184), lococcus aureus infection (FDR=0.000207533) involving four extracellular region (FDR=0.0000276714), receptor activ- DEGs (C3, HLA-DRB5, ICAM1, and HLA-DRB1), and ity (FDR=0.0343115), protein binding (FDR=0.00758689), neuroactive ligand-receptor interaction (FDR=0.00059726) BioMed Research International 5

2 miRNA_sampleLabel miRNA_sampleLabel normal hsa−miR−1291|hsa−mir−1291 1.5 case hsa−miR−98−3p|hsa−mir−98 1 hsa−miR−424−5p|hsa−mir−424 0.5 hsa−miR−939−5p|hsa−mir−939 0 hsa−miR−3622a−5p|hsa−mir−3622a hsa−miR−1273g−3p|hsa−mir−1273g −0.5

hsa−miR−4792|hsa−mir−4792 −1 hsa−miR−5100|hsa−mir−5100 hsa−miR−2116−3p|hsa−mir−2116 hsa−miR−4473|hsa−mir−4473

Figure 2: Hierarchical clustering analysis based on the expression profle of all diferentially expressed miRNAs in STC compared to normal tissues. Orange and light blue color mean the group of STC and normal tissues, respectively. Red color represents the relative expression level of genes which was higher than mean, and green color represents the relative expression of genes which was lower than mean.

HLA-DRB5 LCN2

GTSE1 RNF157 ADRA1D CHST3 MCAM MFAP5 HLA-DRB1 hsa-miR-939-5p CREB5 IGF2 PTGDR NTN1 CLEC2D NT5DC3 CLSPN hsa-miR-3622a-5p SAPCD2 MKI67 C4orf36 LPL B3GNT6 IQGAP3 BIRC5 MMP2 MN1 hsa-miR-424-5p CCNF hsa-miR-1291 hsa-miR-4473 GRIK3 ITGA11 MYBL2 CEP55 CDK1 EMP1 ZCCHC24 ANKRD36 KIF18B LTB PRLR FBN1 CDT1 CDCA5 BCL11B PDZD4 VAT1 S1PR1 FNDC5 hsa-miR-5100 GAS7 TIMP2 ARL4D NXPH3 C3 SDC2 SERPINE1 hsa-miR-98-3p hsa-miR-2116-3p ICAM1 CCR1 CBX6 CRISPLD2 MYADM AOX1 MRC2 SYNGR1 PALD1 THBD F2RL3 PCSK2 SERPING1 PEA15 WIPF3 hsa-miR-1273g-3p KIF11 SFRP1 C7orf41 RND2 FKBP5 OR51E2 CCDC80 RBFOX3 PACSIN3 COL12A1

Figure 3: STC-specifc diferentially expressed miRNA-targets interaction network. Ellipses were used to represent the DEGs and red and green color were used to represent up- and downregulation in STC, respectively. Rectangles were used to represent diferentially expressed miRNAs and rose and blue color were used to represent up- and downregulation, respectively.

involving six DEGs (F2RL3, S1PR1, PRLR, GRIK3, ADRA1D, proteins which had the high connectivity with other proteins and PTGDR) were three signifcantly enriched pathways in were hub proteins. In the PPI network, CDK1 (degree=16), STC (Figure 7). CDC20 (degree=13), TK1 (degree=8), BAG3 (degree=8), CCNB1 (degree=7), AURKB (degree=7), BUB1B (degree=6), 3.4. Protein-Protein Interaction Network. PPI network of all FBN1 (degree=5), ELN (degree=5), CCNF (degree=5), DEGs in STC was determined by Cytoscape sofware. Te ZBTB16 (degree=5), and NUF2 (degree=5) were the hub 6 BioMed Research International

GO Biological Process

enkephalin processing (BP) cellular response to prostaglandin D stimulus (BP) negative regulation of BMP signaling pathway by extracellular sequestering of BMP (BP) cellular response to gravity (BP) negative regulation of vascular wound healing (BP) positive regulation of leukotriene production involved in infammatory response (BP) negative regulation of smooth muscle cell−matrix adhesion (BP) G1/S transition of mitotic cell cycle (BP) negative regulation of gene expression (BP) cell division (BP) regulation of cell adhesion (BP) intramembranous ossifcation (BP) bone trabecula formation (BP) cell cycle (BP) mitosis (BP)

01234 −log(FDR)

Figure 4: Signifcantly enriched biological function of target DEGs in STC.

proteins (Figure 8). Among which, we also performed the has been demonstrated that MKI67 plays an important role PPI network of several DEGS including C3, HLA-DRB5, in colon cancer [13]. Herein, we also found that MKI67 ICAM1 and HLA-DRB1, ITGAM, and CFD that involved was one of target genes of hsa-miR-1291 in STC. It has in the staphylococcus aureus infection signaling pathway been indicated that hsa-miR-1291 can promote apoptosis in (Figure 9). esophageal squamous cell carcinoma by regulating mucin1 [14]. Additionally, it is reported that hsa-miR-1291 is upregu- lated in colon cancer [15]. Baculoviral IAP repeat containing 3.5. Validation the Expression of DEGs and Diferentially 5 (BIRC5, also called survivin) is a proliferation marker and Expressed miRNAs by GEO. Based on the RNA-sequencing belongs to the members of the IAP family of proteins [16, 17]. data, six downregulated DEGs (HLA-DRB1, PTGDR, MKI67, BIRC5 controls a number of cellular processes including cell BIRC5, CCNF, and CDK1) in STC were selected to perform apoptosis [18]. It is found that BIRC5 is highly expressed the expression validation by GEO dataset (Figure 10). Difer- in colon cancer cell lines [19]. And increased expression of ent expression levels of these genes and miRNAs were ana- BIRC5 is related to poor clinical outcome in patients with lyzed and depicted through box-plots. HLA-DRB1, PTGDR, colon cancer patients [20]. In addition, it is diferentially MKI67, BIRC5, CCNF, and CDK1 were signifcantly down- expressed in irritable bowel syndrome rectosigmoid mucosa regulated in the case group compared to the normal control [21]. In our study, we found downregulated expression of group, which was consistent with our RNA-sequencing data. cell proliferation markers of BIRC5 and MKI67 in the colon tissue of STC, which indicated that BIRC5 and MKI67 may 4. Discussion be involved in the cell proliferation and apoptosis in the development of STC. STC is an intestine disease that infuences the health and life Microbiota infection is common in diferent intestine quality of patients. Terefore, understanding the pathology disease. Postinfectious irritable bowel syndrome (PI-IBS) mechanism will make a contribution to reducing the inci- presents a bout of gastroenteritis caused by viral, parasitic, dencerateofSTC.Inourstudy,weidentifedseveralDEGs or bacterial infections [22]. It is worth mentioning that and diferentially expressed miRNAs in STC, which may play staphylococcus aureus infection is a potential pathogenic roles in the molecular level of STC. factor in PI-IBS [23–26]. It is reported that infammatory In top 20 DEGs, we found two cell proliferation-related bowel disease causes infammatory obstruction of the gas- genes including MKI67 and BIRC5 in colon tissue of STC. trointestinal tract, resulting in symptoms such as constipation Marker of proliferation Ki-67 (MKI67) is a cellular prolif- [27]. Tis suggested the correlation between infammation eration marker that is detectable in the cell nucleus during and constipation. In this study, we found that staphylococcus active phases of the cell cycle except resting cells [12]. It aureus infection was the most signifcantly enriched pathway BioMed Research International 7

GO Cellular Component

cytoplasmic microtubule (CC)

integral to plasma membrane (CC)

proteinaceous extracellular matrix (CC)

MHC class II protein complex (CC)

integral to membrane (CC)

midbody (CC)

cell surface (CC)

centriole (CC)

microfbril (CC)

spindle microtubule (CC)

basement membrane (CC)

plasma membrane (CC)

extracellular region (CC)

extracellular matrix (CC)

extracellular space (CC)

012345 −log(FDR)

Figure 5: Signifcantly enriched cellular component of target DEGs in STC.

GO Molecular Function

proteoglycan sulfotransferase activity (MF) sphingolipid binding (MF) receptor activator activity (MF) aldehyde oxidase activity (MF) xanthine dehydrogenase activity (MF) triglyceride binding (MF) beta−1,3−galactosyl−O−glycosyl−glycoprotein beta−1,3−N−acetylglucosaminyltransferase activity (MF) chondroitin 6−sulfotransferase activity (MF) prostaglandin D receptor activity (MF) prostaglandin J receptor activity (MF) chemokine (C−C motif) ligand 5 binding (MF) adenylate cyclase inhibiting G−protein coupled activity (MF) receptor activity (MF) binding (MF) protein binding (MF)

0.0 0.5 1.0 1.5 2.0 −log(FDR)

Figure 6: Signifcantly enriched molecule function of target DEGs in STC. 8 BioMed Research International

KEGG Pathways

Type I diabetes mellitus

Allograf rejection

Graf−versus−host disease

Asthma

Systemic lupus erythematosus

Tuberculosis

Leishmaniasis

Viral myocarditis

p53 signaling pathway

Phagosome

Cell adhesion molecules (CAMs)

Neuroactive ligand−receptor interaction

Rheumatoid arthritis

Complement and coagulation cascades

Staphylococcus aureus infection

01234 −log(FDR)

Figure 7: Signifcantly enriched KEGG pathways of target DEGs in STC.

of DEGs in STC. And four DEGs including HLA-DRB1, HLA- of the rat with constipation and decreased afer the laxative DRB5, C3, and ICAM1 were involved in this signal pathway. treatment [36]. Intercellular adhesion molecule 1 (ICAM1) is Major histocompatibility complex, class II, DR beta 1 found to be involved in the colon cancer cell proliferation (HLA-DRB1) has been demonstrated to be associated with [37]. In this study, we found upregulated expression of both ulcerative colitis and Crohn’s disease [28, 29]. Furthermore, C3 and ICAM in STC. In addition, the PPI network of HLA-DRB1 is diferentially expressed in precancerous col- HLA-DRB1, HLA-DRB5, C3, and ICAM1 showed that these orectal adenomas mucosa [30]. Another major histocom- genes had a signifcant association with proteins encoded patibility complex family member, major histocompatibility by other diferentially expressed genes. Our result suggested complex, class II, DR beta 5 (HLA-DRB5) is found signif- thatHLA-DRB1,HLA-DRB5,C3,andICAM1mayplaya icantly upregulated in human colorectal cancer epithelial proinfammatory role in the development of STC. cell and has prognostic value in the prediction of colon In addition, we found that C3 and ICAM were commonly cancer metastasis [31, 32]. Herein, we found downregulated regulated by hsa-miR-1273 g-3p in STC. Hsa-miR-1273 has expression of both HLA-DRB1 and HLA-DRB5 in STC. Inter- been regarded as a functionally uncharacteristic miRNAs estingly, they were under the regulation of hsa-miR-939-5p. It in colon cancer exosomes [38]. In addition, another two is reported that low expression of hsa-miR-939 is signifcantly upregulated DEGs including F2R like or associated with shorter distant metastasis-free survival of receptor 3 (F2RL3) and syndecan 2 (SDC2) were also target colon cancer and has a prognostic value in the development genes of hsa-miR-1273 g-3p in STC. F2RL3 (also called PAR4) of colon cancer [33]. It is demonstrated that complement is a protease that plays roles in antinociceptive efects in C3 (C3) is locally synthesized and secreted in the human visceral sensitivity through protease-activated receptor [39]. intestine [34]. It is frst found that the C3 mRNA expression is Te expression of F2RL3 is downregulated in the colon of signifcantly upregulated in the infamed mucosa of patients patients with irritable bowel syndrome patients [40, 41], with infammatory bowel disease [35]. It is worth mentioning while, it is signifcantly upregulated in patients with colon that the expression of C3 is downregulated in the distal colon cancer and ulcerative colitis [42, 43]. SDC2 functions as BioMed Research International 9

SPG30 AXL

TBC1D166 C1-TEN CSDA3 ARMD7 SLA

PER1 PDGFRBB CT139 CR3A CENP-MM eNOS S1-5 RPRP5-1163J11.21 P3.58 P33 S4 CD363 XPCT

CENPH SPBC24 C10orf3 MSN PTH1R PIX2 CT106 C20orf1 FXR ECM1 PYGM ROD SLC12A44 API4 R2 RRPRP3-357D1133.2323 PPP1R48P MYBL2 MMRN11 PACSIN33 UNNNQ326/PRRO386R BM-037 SSK1 Sph1 CLSPN MAD2M PIPOX FBX1 PARK1 UNNNQ602/PRORO1188ROO1 8 NLK1N PTTG FKBP54 SSERPINA33 BCL2L5 HLA-DRBH B5 CKSHS2 TPPP1 CDC20 PP9099 CCNB1 TOP2A BS ADH2 BOKL SS1 TACC3 RIS2R ASE1 TSPYL2T YL2 SGK MX1 C14orf766 TTU12B1-TTYT KAP1 RGCC FST KNSL1 P34CDC2P 2 ENX-1 TK2 DLG7 CLEC3B TNFA1P22 NGAL ZBTB16 KIA RPPP13-364K223.12 MOM1 SSORORINN CPNE6 MONA DTS KKIAA1731N1NNLN CAIR-1 CRIP PGII NOTCH33 K222TA22 SOX2 MST161 CL-K1-IIaIaa hcp-1 HHSPDE9AA2A PP38BETA22 UBE2C FIBL1 FBLN2 BCUR1 SVAS RAIN IQGAP3 FOSL1 p55 GHAP ARA SFRP4 ACMICDD BDE PGI

CRE-BPAA HU-2 NF-I MFAP2 JOAG1 RRPRP11-536C5C55.8 bbeta-globinbinin IGFBP6 RPPP11-710A111.11 PEDF C3orf7 MASA HBA-T3 C11orf433 NXPH3

FECD

Figure 8: Te protein-protein interaction networks of all DEGs in STC. Red and green rectangle nodes represent up- and downregulated DEGs, respectively. Te solid line means the interaction correlation between proteins.

a and plays roles in interacting with an extracellular matrix glycoprotein and high methylation in extracellular matrix components and cell-to-cell signaling colonic tissue of patients with colon cancer [45]. Te genetic molecules [44]. It is found that SDC2 is low methylated in mutation of FBN1 is also associated with colon cancer [47]. It peripheral blood DNA of colon cancer and serves as a blood- is noted that FBN1 has been identifed as a promising early based diagnostic marker of colon cancer [45]. In a word, our detection biomarker of colon cancer and colon adenomas result suggested that the expression of C3, ICAM1, F2RL3, [48]. In this study, the expressions of ZBTB16 and FBN1 were and SDC2 under the regulation of hsa-miR-1273 g-3p may be upregulated and under the regulation of hsa-miR-4473 in important molecules in the process of STC. STC. Our result suggested that both ZBTB16 and FBN1 could In the PPI network, we found several high degree proteins be involved in the growth regulatory of STC. encoded by DEGs, such as zinc fnger and BTB domain con- CCNF contributes to the G2 to M phase transition in taining 16 (ZBTB16), fbrillin 1 (FBN1), cyclin F (CCNF), and the cell cycle. It is a key gene in colorectal adenoma-to- cyclin dependent kinase 1 (CDK1). Promyelocytic leukemia cancer progression [49]. It is reported that CCNF is expressed zinc fnger protein (PLZF), encoded by ZBTB16 gene, is in colon cancer tissue samples and overexpressed as an involved in diferent growth regulatory and diferentiation epigenetic clock gene in colon adenoma tissue samples [50, pathways. It is signifcantly upregulated from normal colonic 51]. It is reported that the interaction between CDK1/cyclin mucosa to aberrant crypt foci and colon cancer [46]. FBN1 is B1 complex and ZNFX1 antisense RNA 1 (ZFAS1) leads 10 BioMed Research International

EIF3S5E bbA366E1313.113 S30 RPS15R STAT1 L13A L35A RRP11-4877I5.67I H3FN VIL2 MMP44 SUPT16H1 H H3/d ZNF650Z 0 HSPA5 ALY/REFF RRP11-513M3MM16.6 PURAU RPS16R CHK1 SRBR HLAA BBC1 RPL8 H1F1 L19 EIF3E SMD1MD EEIF3S111 HHLA-DRBLA-DRB5 EIF3S3E FF3 IBMPFDIB PFDD2D2 RPL7AA MSNMSN eIeIF3-p11eIFI -pp11011010 IFNGIP1FN P11 HNRPHH1 PABPL1 P0P RRP11-354A4A16.14A RPP1PP11-369J2P11 69J29J2J21.10-010J22 0-010 FSGSFSSG RPRPP11-152N15 N13.13-004N 3 3-3 044 FLUPFL P RRP1-3J17RP1-3J17.-3J 7.27. CD20 HSPA8H OK/SW-cl.103K/SW-cl.10W CSDA3CSDA GIT1GIT1 ATP1BA 1B CLIC4 AARFGEFRFR G F2F2 PAK1PAAKK L17L1 RPL5 CTHBPCT P SSigma3ASiSigg a3A3AA Sm-B/BB RPL4RPPL H2B.2H 2 ALFYAALL CIDE-ACIDC DEE-AA H2B/f2B//f MLRWM W D6S182D6D RPL355 P22 HSP90NN AMOXAAMMOXADAD PAPPAKgammPAKAK aammmmammmma H2BJ RPL30L3300 RPPP11-536K7P11-53 7.17 1 YWHAEYWHAAEE CTDNEPP1 TUBB4 S3AA RRP3-527FRPP3--527F7FF8.4F BBDPLT15DPLT OTULINOTTUULILINN LFA-1 SQLE H3.4H ARR H2-ALPHH2--AALPPHPHAHA FKHL5FKH ALURBPA RBPP CAP-RfAP-Rf BTF2 TUBB6TTU B66 HIST2H2BFHHIST2H2BHISTHIIST PDE3BDE3B GIT2GIGIITT2 KU70 CCN5 EIF3S4EIF3SS4 AAC1AAAAACC1 A6A6 CPGL2 RRP11-13GRP11 133GG14GG14 PIXBIXIXBXBB H2BFH2BFHH HHLA-DRBLA-DRB1 SSNRNP22SNNRNP2P2222020 CSBPCSBBPP RNF1499 EMC6 DBPA L27L XEDARXXEEDDAARAR MYH3M H33 PIG61 CXX1CXCXXXX1XX RRP11-535CRP 535C5CC21.1C21C FNDC4FNDC44 H2BFJH2BFH22BFBFJ GPAPPGPAG SLRR3AA SMX4 MZT2BMZMZTT2T22BB TUBB5 PP791 HEL-220HEEL-220 NPM1NNPPMMM11 H2B/e TRATRA SSASS CN1AN1A1A S3S M-SEMAA SET RRP3-477HRPP333--44477777H23.1HH22233.3..11 TUBB8TUTUBBUBB8UB RPRRP11-223J3J15.33J ACDP1 MARCHM CH1H1 COBP HHIST1H22BN2 ANALBABAA CT1.33 HIP33 MDA20200 PHHHOSPHOO1O1 PEK KU800 CSNK2AA1 HHLA-DMADMA HIRIP22 RPS4XR Sm-D3 NANAALADLL2L2 ITGA6BB ABC1 CFD RPL26P1RPLR 1 HLA-DRRB3 TUBE H2B/S H1F4 U21.121 1 FAM118B8B eIF3-p36eIFeIF3eIF3-peIeIF3-pF3-pF3-p3-p36p366 AUF1AU AAP EIF3CLL JJAM-2 AUP1 eIF3-p46eIF3-eIF3eIF3-p46p46466 CISKISK RPL31RPL LLREP3LLLLRERERREPEP33 L18L 8 RPL36 CANCAA H3F3BH3F ZWS1 RPRPP11-269F19P11 - 699F 9.399.3 HKE3HHK H1s-1H s- SNRPA1SNS RPAPA1A11 RPS25RPS S4 PPSEC0216166 ITGAM IRAK HSD3B7HSSD B7 H1s-2s- EAPAP L27AL27A IBMPFDD3D NSEP1N S13 URKR TSH2BTSH2B.1TSH .1 CCT3CCCT3 LCAMBB FLN-A RPL12RPPLP NOS2ANOS2AA HRAD21211 FBRNPBRRN EEIF3-P111616 RPS9RP 9 DDX17 H2B/c PPS1TP5BPTPP BP1BP SHSHUJUN-UNN-2NN- MED200 GDPD11 TUBB7PP L26 SRp20SR 20 MY0141 MMGLL HHEL-S-31-S 3110 S23 C23 PRO1400P 0 MFD1 G3PD CPNC N RPS14R DDX19BD X1 B DBA1 RRP11-90MM2.2M 2 HSPC029H 9 AALS2CR7R77 CGI-18 RPS28R BFD C3 pp9p97MAPAPKAP FHR4 CARP RRP11-32DD17.3D AMBP1MBP11 RRP4-580ORP4 O19.1O CIG BFDBF SLEB6SLE FHR3 MED23D23 PPPP1R255 RRP5-1054A4A22.24A KAF C5b

Figure 9: Te protein-protein interaction networks of DEGs involved in the staphylococcus aureus infection signaling pathway. Green rectangle nodes represent DEGs. Te solid line means the interaction correlation between proteins.

to cell cycle progression and cell apoptosis suppression of STC. Furthermore, sphingosine-1-phosphate receptor 1 in the progression of colon cancer [52]. It is found that (S1PR1) and serpin family E member 1 (SERPINE1) were also CDK1 is remarkably downregulated in colon cancer cell lines target genes of hsa-miR-2116-3p. In mice, genetic deletion [53]. Signifcantly, it is regarded as a promising prognostic of S1pr1 will increase vascular permeability in the colon and biomarker of the metastasis risk in stage II colon cancer enhance bleeding in colitis [60]. Additionally, it is found that [54]. In this study, CCNF and CDK1 were downregulated targeting SphK1/S1P/S1PR1 may be a potential therapeutic and regulated by hsa-miR-424-5p in STC. It is found that option to prevent the development from colitis to colon hsa-miR-424 is signifcantly upregulated in colon cancer [55]. cancer[61].SERPINE1isaninhibitorofplasminactionand In addition, hsa-miR-424 is a promising biomarker for the has been indicated to promote cancer invasion [62]. It is early diagnosis of colon cancer [56]. Beside CCNF and CDK1, found that SERPINE1 is upregulated in colon cancer and prostaglandin D2 receptor (PTGDR) is also the target gene has been considered as a biomarker associated with poor of hsa-miR-424 in STC. It is found that the expression of prognosis in colon cancer [63]. Hamford et al. frst found PTGDR is lower in colon cancer [57]. It is proposed that the expression of hsa-miR-3622a-5p in colon cancer tissues hypermethylation of PTGDR may make a contribution to [64]. In this study, we found downregulated expression of reducing gene expression and developing colon cancer [58]. hsa-miR-3622a-5p in colon tissue of STC. And adrenoceptor In a word, our study suggested that CCNF, CDK1, and alpha (ADRA1D) was one of target genes of hsa-miR-3622a- PTGDR may be involved in the cell cycle progression of 5p in STC. It is found that ADRA1D is diferentially expressed STC. in stage IV compared to stages II and III colon cancer [65]. It It is reported that hsa-miR-2116 presents remarkable is reported that hsa-miR-98 is upregulated in active ulcerative diferential expression when there are responses to bacterial colitis [66]. Furthermore, the upregulation of hsa-miR-98 infection in human host cells [59]. Herein, we found down- could be considered as the molecular marker for the colon in regulated expression of hsa-miR-2116-3p in the colon tissue active ulcerative colitis patients [66]. Our result indicated that BioMed Research International 11 500 1000 1500 2000 2500 0 100 200 300 400 500 0 200 400 600 800 control IBS−C control IBS−C control IBS−C HLA−DRB1 PTGDR MKI67 50 100 150 200 250 0 5 10 15 20 25 200 400 600 800 1000 1200

control IBS−C control IBS−C control IBS−C CCNF CDK1 BIRC5 Figure 10: Te electronic validation box-plots of DEGs and diferentially expressed miRNA in the GEO dataset.

these hsa-miRNAs and target genes may play an important for exploration of gene expression profling in colon tissue role in the development of STC. of STC. Our fndings may provide the fundamental work for Beside the abovementioned hsa-miRNAs, hsa-miR-5100 understanding the molecular pathology of STC. However, and hsa-miR-4792 were also found in the colon tissue of there are limitations to our study. Firstly, the sample size in STC. However, there were no previous reports about the rela- the RNA-sequencing was small and large numbers of samples tionship between hsa-miR-5100, hsa-miR-4792, and intestine of STC are needed for further research. Secondly, these dys- disease including STC. Terefore, further research is needed regulated genes, miRNAs, and related signaling pathways in to study the function of hsa-miR-5100 and hsa-miR-4792 in STC were identifed and the potential molecular mechanism STC. was not studied. Terefore, in vivo and in vitro experiments including animal model and cell culture were necessary to 5. Conclusion elucidate the underlying pathological mechanism of STC in the future work. Tirdly, qRT-PCR is needed to further In summary, we identifed 464 DEGs and 10 diferentially validate the expression of several diferentially expressed expressed miRNAs in STC compared to normal tissues. mRNAs and miRNAs. PPI network generated some hub proteins such as ZBTB16, FBN1, CCNF, and CDK1. Target DEGs of diferentially expressed miRNAs were signifcantly enriched in staphylo- Data Availability coccus aureus infection involving four DEGs (HLA-DRB1, HLA-DRB5, C3, and ICAM), which suggested that infam- Te data used to support the fndings of this study are mation may be a factor for STC. Our study was the frst article included within the article. 12 BioMed Research International

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