Identification of Potential Gene and Microrna Biomarkers for Colon Cancer by an Integrated Bioinformatical Approach

Original Article

Identification of potential gene and microRNA biomarkers for colon cancer by an integrated bioinformatical approach

Qingxia Ma1*, Jingyi Song2*, Zhen Chen2, Lei Li2, Bin Wang2, Ningning He2

1Department of Pharmacology, School of Pharmacy, 2School of Basic Medical Sciences, Qingdao University, Qingdao 266021, China Contributions: (I) Conception and design: N He, B Wang; (II) Administrative support: B Wang; (III) Provision of study materials or patients: Q Ma, J Song, Z Chen; (IV) Collection and assembly of data: Q Ma, J Song, L Li; (V) Data analysis and interpretation: N He, B Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. *These authors contributed equally to this work. Correspondence to: Bin Wang. School of Basic Medical Sciences, Qingdao University, 38 Dengzhou Road, Qingdao, 266021, China. Email: [email protected]; Ningning He. School of Basic Medical Sciences, Qingdao University, 38 Dengzhou Road, Qingdao, 266021, China. Email: [email protected].

Background: A unique resource for genomic studies is the NCI-60 cancer cell line panel, which provides various publicly available datasets, including gene and microRNA (miRNA) transcripts, whole-exome sequencing, DNA copy number, and protein levels against 59 cancer cell lines. In this study, we aimed to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) for colon cancers by analyzing NCI-60 gene and miRNA expression profiles. Methods: The real-time quantitative RT-PCR (qRT-PCR) was used to validate the identified candidate genes and miRNAs. The functional enrichment analysis and pathway analysis were performed for DEGs and the network analysis was performed for predicted miRNA targets of DEMs. Results: Finally, we integrated DEGs and DEMs and constructed a network, revealing the relationship for 5 miRNAs and 104 gene probes. The identified specific genes, miRNAs and integrated network for colon might provide new clues on possible mechanisms for colon cancer. Conclusions: In conclusion, integrated bioinformatics approaches on publicly available datasets play critical roles for finding candidate biomarkers and potential mechanisms for cancers.

Keywords: NCI-60; differentially expressed genes (DEGs); differentially expressed miRNAs (DEMs); colon; network analysis

Submitted Aug 25, 2017. Accepted for publication Dec 05, 2017. doi: 10.21037/tcr.2017.12.09 View this article at: http://dx.doi.org/10.21037/tcr.2017.12.09

Introduction mutation, functional, and pharmacological levels (2-5). Now NCI-60 has been widely used in cancer research and The NCI-60 is a panel of 60 human tumor cell lines which bioinformatics for analysis cell phenotypes and pathway isolated from diverse histologies and 9 different tissues relationships (6). of origin. It mainly contains cancers of colorectal (CO), Microarray is a powerful tool for detecting the ovarian (OV), melanomas (ME), lung (LC), breast (BR), and expression pattern of RNA levels, including those of mRNA central nervous system (CNS) origin (1). The NCI-60 panel and microRNAs (miRNA) (7,8). Due to the wide use of was used by the Developmental Therapeutics Program microarray, a large number of microarray data have been (DTP) of the National Cancer Institute (NCI) of the USA collected and used to discover mechanism for tumorigenesis, in 1990. It is reported that NCI-60 is a comprehensively development and therapy. Bioinformatical approaches panel of diverse cell types at the DNA, RNA, protein, are crucial for discovering more valuable information

© Translational Cancer Research. All rights reserved. tcr.amegroups.com Transl Cancer Res 2018;7(1):17-29 18 Ma et al. Identification of biomarkers for colon cancer included in these datasets, particularly signaling pathways, Analysis of miRNA-target mRNA network complex biological processes and the interaction network The selected DEMs were imported to TargetScan (Release of differentially expressed genes (DEGs) and differentially 7.1, http://www.targetscan.org/) which can search the expressed miRNAs (DEMs) (9,10). target genes based on the conserved sites matching the Colon cancer is the fourth-leading cause of cancer- seed regions of miRNAs. Cumulative weighted context++ related death in the world. Despite after surgical resection of score −0.4 were set as the threshold. The miRNA-target colon cancer, more than 50% of patients die of developing ≤ mRNA network was constructed by Cytoscape (http://www. recurrence and disease relapse after several months (11,12). Clinical diagnosis of colon cancer relatively lagged behind cytoscape.org/) (15). dues to a serious threat to human health. Therefore, it is necessary to discover more effective biomarkers and reveal Constructing gene-miRNA network prediction molecular mechanisms of clinical significance of biomarkers. PCC and its P value between genes and miRNAs were Recently, construction of biophysics network models calculated using gene and miRNA expression data from of numerous components has contributed to our NCI-60 cell lines. The criteria of PCC >0.7 or PCC <−0.7 understanding of the relationship between different types and P value <0.01 were applied to select gene-micorRNA of biological molecules (13). In this study, we used the relationship for further analysis. Finally, the network was simple Pearson correlation coefficient (PCC) of the gene constructed for links between compounds and genes using and miRNA expression profile over diverse NCI-60 cell Cytoscape (http://www.cytoscape.org/) (15). The force- lines. It is possible to obtain a certain number of significant layout algorithm was applied to optimize the topology of correlations between gene expression and miRNAs on the network. subsets of cancer samples. In this research, our goal is to identify the DEGs and Cell culture DEMs for colon cancer, functional enrichment analysis for DEGs, construct miRNA network with their target genes HCT-116, HCT-8, A549 and H460 cell lines were and gene-miRNA correlation network, which provide new all purchased from Cell Bank of Shanghai Institute of clues on possible mechanisms for colon cancer. Biochemistry. HCT-116 and HCT-8 cells were cultured in DMEM/High Glucose (HyClone) medium containing 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 mg/L Methods streptomycin. A549 and H460 cells were cultured in RPMI Acquisition of microarray data 1640 medium (HyClone) with 10% FBS, 100 U/mL Microarray gene expression and miRNA expression data penicillin, and 100 mg/L streptomycin. All the cells were were obtained from the NCBI Gene Expression Omnibus cultured under 37 and 5% CO2. (GEO) ftp (https://www.ncbi.nlm.nih.gov/geo/). NCI-60 ℃ gene expression data (GSE32474) and miRNA expression Quantitative real-time PCR data (GSE26375) were retrieved. Gene probes or miRNAs with a fold change of >4 and P<0.01 for colon cancer over Total RNA was extracted from cells and tissues using the median value were selected for the further analysis. TRI Reagent RT (Invitrogen, Carlsbad, CA, USA). Total RNA solution was stored at −70 . One μg total RNA was converted to cDNA using Stem-loop RT primers for ℃ Functional analysis and pathway enrichment analysis miRNA and using anchored oligo (dT) primers for the DAVID (http://david.ncifcrf.gov/) is an online tool that can gene. Following the manufacturer’s instructions, 2 μL ® be utilized to perform a functional analysis and pathway cDNA was performed to real-time qPCR using SYBR enrichment analysis for discovering the relationships among Green PCR Kit. Beta-actin was used as a loading control the selected gene sets (14). Gene Ontology (GO) analysis for gene while U6 for miRNA. The sequences of primers and Kyoto Encyclopedia of Genes and Genomes (KEGG) used in qRT-PCR were as showed in Table 1. All reactions pathway enrichment analysis was performed by the DAVID had three replicates. The data were analyzed using the online program. comparative 2− CT method. ΔΔ

Table 1 Primer sequences of DEMs and DEGs used in qRT-PCR Gene Forward primer Reverse primer

RNA

KLK6 5′-TGGTGCTGAGTCTGATTGCT-3′ 5′-CGCCATGCACCAACTTATT-3′

S100A14 5′-ACTCTCACCAAA GGACCAGACAC-3′ 5′-CAG GTG CAG GCT AGG GTA CAG-3′

ANO1 5′-CTGATGCCGAGTGCAAGTATG-3′ 5′-AGGGCCTCTTGTGATGGTACA-3′

LYZ 5′-CTTGTCCTCCTTTCTGTTACGG-3 5′-CCCCTGTAGCCATCCATTCC-3′

ESRP1 5′-GCCAAGCTAGGCTCGGATG-3′ 5′-CAGTCCTCCGTCAGTTCCAAC-3′

ACSL5 5′-CTCAACCCGTCTTACCTCTTCT-3′ 5′-GCAGCAACTTGTTAGGTCATTG-3′

FOXQ1 5′-CACGCAGCAAGCCATATACG-3′ 5′-CGTTGAGCGAAAGGTTGTGG-3′ Micro-RNA

U6 5′-CTCGCTTCGGCAGCACA-3′ 5′-AACGCTTCACGAATTTGCGT-3′

Mir-7 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-GGGGTGGAAGACTAGTGATTTT-3′ ACTGGATACGACACAACAA-3′

Mir-18b 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-TAAGGTGCATCTAGTGCA-3′ ACTGGATACGACCTAACTG-3′

Mir-590-3p 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-GGGGTAATTTTATGTATAAG-3′ ACTGGATACGACACTAGCT-3′

Mir-20a 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-TAAAGTGCTTATAGTGCA-3′ ACTGGATACGACCTACCTG-3′

Mir-429 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-GGGGTAATACTGTCTGGTAAA-3′ ACTGGATACGACACGGTTT-3′

Mir-200a 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-GGCATCTTACCGGACAGTG-3′ ACTGGATACGACTCCAGCA-3′

Mir-200b 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGC 5′-GGCATCTTACTGGGCAGCA-3′ ACTGGATACGACTCCAATG-3′

Mir-378 5′-GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATT 5′-CTCCTGACTCCAGGTCC-3′ GCACTGGATACGACACACAGG-3′ DEMs, differentially expressed miRNAs; DEGs, differentially expressed genes; qRT-PCR, quantitative reverse transcriptase-polymerase chain reaction.

Software support and statistical analysis DNA microarray of the NCI-60 cell line panel (GSE32474) was analyzed. A total of 560 gene probes were identified Hierarchical clustering of gene expression profile was with significant differential expression in colon cancers. carried out using QCanvas (16). All images were formatted Among them, 285 gene probes with higher expression level for optimal presentation using Adobe Illustrator CS4 (Adobe and 265 gene probes with lower expression levels in colon Systems, San Jose, CA, USA). To determine statistical cancer cells [log (fold change) >2 or log (fold change) significance, P value from t-statistic was calculated. 2 2 <−2, P<0.01]. The up-regulated and down-regulated genes (DEGs) were used to generate the heatmap profile Results (Figure 1A). The differential expression pattern for two down-regulated genes (SPG20 and NR3C1) and two up- Identification of DEGs regulated genes (S100A14 and KLK6) were showed in To identify novel gene biomarkers for colon cancer, the Figure 1B.

(A) A B Colon -8 0 10 log2(gene expression) log log2(gene expression)

log 212526_at: SPG20 201865_x_at: NR3C1 2 2 2 3

Colon Colon Others Others -6 -6

0 60 0 60 Cell lines Cell lines log2(gene expression) log log2(gene expression) 218677_at: S100A14 log 207433_at: KLK6 2 2 7 8 Colon Colon Others Others

-1 -1

0 60 0 60 Cell lines Cell lines

Figure 1 Expression profile of the DEGs for colon cancer cell lines across NCI-60 cancer cell lines. (A) A heatmap showed 285 upregulated and 265 downregulated genes. Red, upregulation; green, downregulation; (B) SPG20, NR3C1, S100A14 and KLK6 showed a colon-specific expression pattern. DEGs, differentially expressed genes.

GO function and KEGG pathway analyses biomarker candidates, including KLK6, S100A14, ANO1, LYZ, ESRP1, ACSLC5 and FOXQ1, and their roles in To explore the functions of those DEGs in depth, the colon cancer have not been clearly addressed before. The biological process, pathway annotation and molecular previous study has been showed that KLK6 may represent function were revealed using DAVID Gene Functional a potential unfavorable prognostic biomarker for colon Classification Tool. The GO analysis revealed that the cancer (17). Overexpression of S100A14 in colon tumors DEGs were significantly involved in the gap junction may have a potentially important function in malignant assembly, neural tube closure, positive regulation of transformation (18). These seven genes were further cell migration, wound healing and extracellular matrix verified by qRT-PCR. As showed in Figure 3, the expression organization (the top 5 biological process) (Figure 2A). level of ANO1, LYZ, ESRP1, ACSLC5, FOXQ1, KLK6 Furthermore, the KEGG pathway was used to determine and S100A14 were significant highly expressed in colon the pathways involved in colon cancers. The result cancer cells and colon tissues. showed that DEGs were primarily involved in pathways in cancer, PI3K-AKT signaling pathways, cell adhesion, focal adhesion, tight junction and etc. (Figure 2B). The Identification of DEMs and miRNA-target mRNA network results collectively indicated that genes involved in the cell There were 27 miRNA probes were identified from

proliferation and migration were significantly associated GSE26375 for colon cancers [log2 (fold change) >2 or

with colon cancers. log2 (fold change) <−2, P<0.01] (Figure 4A). Especially, five up-regulated miRNAs (miR-7, miR-200a, miR-200b, miR-200c, miR-141) were identified. Those five miRNAs Experimental validation of the key DEMs were further verified by qRT-PCR (Figure 4B,C,D,E,F). According to the transcriptomic data and bioinformatics Interestingly, miR-200a, miR-200b, miR-200c, and miR- analysis, seven genes were selected as potential gene 141 were members of miR-200 protein family (19). Those

A GOTERM_MF_identical protein binding GOTERM_MF_insulin-like growth factor binding

GOTERM_MF_integrin binding

GOTERM_MF_structural constituent of cytoskeleton

GOTERM_MF_protein binding

GOTERM_CC_extracellular space

GOTERM_CC_plasma membrane

GOTERM_CC_bicellular tight junction

GOTERM_CC_apical plasma membrane GOTERM_CC_extracellular

exosome GOTERM_BP_gap junction assembly

GOTERM_BP_neural tube closure

GOTERM_BP_positive regulation of cell migration

GOTERM_BP_wound healing RT

GOTERM_BP_extracellular matrix organization

0 1 2 3 4 5 6 7 8 9 10

GO term -log(P-value)

B Glutathione metabolism Cocaine addiction * Chemical carcinogenesis * Melanoma * Drug metabolism - cytochrome P450 * Amoebiasis * Gap junction * * Metabolism of xenobiotics by cytochrome P450 Arrhythmogenic right ventricular cardiomyopathy (ARVC) * * Tight junction Osteoclast differentiation * Dopaminergic synapse * * Regulation of actin cytoskeleton * Focal adhesion * Cell adhesion molecules (CAMs) ** Proteoglycans in cancer ** Hippo signaling pathway ** PI3K-Akt signaling pathway * Pathways in cancer * 0 5 10 15 KEGG pathways Counts

Figure 2 GO and KEGG analyses of DEGs for colon cancer cell lines. (A) The top five terms of molecular functions, cell components and biological processes of GO enrichment analysis in DEGs; (B) KEGG pathway analysis of the DEGs. *, P<0.05; **, P<0.01. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

five DEMs were imported to TargetScan (Release 7.1, Construction of gene-miRNA network http://www.targetscan.org/) which can search the target genes based on the conserved sites matching the seed To further explore the interactions of DEGs and DEMs, regions of miRNAs. Cumulative weighted context++ score the PCC and its P value between genes and miRNAs were ≤−0.4 were set as the threshold. The miRNA-target mRNA calculated using gene expression and miRNA expression network was constructed by Cytoscape (Figure 5). data on 59 NCI-60 cell lines. The criteria of absolute PCC

A * B *** mRNA relative expression mRNA relative expression 20.3 22.8 8 4 LYZ ANO1 6 3 4 2 2 1

0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

Colon Lung Colon Lung * mRNA relative expression

C mRNA relative expression D 7.5 5.8 19.9 5.7 5 5 ACSL5 ESRP1 4 4 3 3 2 2 1 1

0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

Colon Lung Colon Lung mRNA relative expression E F mRNA relative expression * 5 5.3 25.3 10.2 FOXQ1 5 4 4 KLK6 3 3 2 2 1 1 0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

Colon Lung Colon Lung

mRNA relative expression ** G

5 8.4 6.4 4 S100A14 3 2 1

0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

Colon Lung

Figure 3 Experimental validation of the key DEGs. The qPCR analysis of ANO1 (A), LYZ (B), ESRP1 (C), ACSL5 (D), FOXQ1 (E), KLK6 (F) and S100A14 (G) expression level in colon cancer cells (HCT-8 and HCT-116), colon cancer tissue samples (CO-tissue 1 and CO-tissue 2), lung cancer cells (H460 and A549) and lung cancer tissue samples (LC-tissue 1 and LC-tissue 2). The data represent mean ± SEM (n=3). *, P<0.05; **, P<0.01; ***, P<0.001. DEGs, differentially expressed genes; qPCR, quantitative polymerase chain reaction; CO, colorectal; LC, lung cancer; SEM, standard error of measurement.

A 25 B 11.8 4 miR-7 miRNA relative expression 20 miR-7 3 miR-7 miR-7 -log10(Pvalue) −log 2 15 10

(P value) 1

miR-200a

10 0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2 miR-200a miR-200b miR-200b 5 miR-200c miR-141

Colon Lung

-8 -4 0 4 8

log2(Foldlog2 change)

C ** D *

10 5 8.6 miRNA relative expression miRNA relative expression 8 4 miR-200b miR-200a 6 3

4 2

2 1 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2 0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2 0

Colon Lung Colon Lung

41.7 5.3 E 4 F 4 miRNA relative expression miRNA relative expression miR-141 miR-200c 3 3

2 2

1 1 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2 0 0 HCT-8 HCT-116 CO-tissue 1 CO-tissue 2 H460 A549 LC-tissue 1 LC-tissue 2

Colon Lung Colon Lung

Figure 4 The microRNA expression profile for colon cancers. (A) DEMs for colon cancers. (B-F) Experimental validation of the key DEMs. The qPCR analysis of miR-7 (B), miR-200b (C), miR-200a (D), miR-429 (E) and miR-200c (F) expression level in colon cancer cells, colon cancer tissue samples (CO-tissue), lung cancer cells and lung cancer tissue samples (LC-tissue). The data represent mean ± SEM (n=3). *, P<0.05; **, P<0.01; ***, P<0.001. DEMs, differentially expressed genes; qPCR, quantitative polymerase chain reaction; CO, colorectal; LC, lung cancer; SEM, standard error of measurement.

TADA1 IPO5 LMO3 UGT1A10 ZFR SCD5 HECTD1 MAGED4 GSG1 UGT1A8 EPHA2 STAT4 STX2 RALGPS2 EPHA7 PPM1E SCHIP1 RANBP6 WTAP PHYHIPL RNF145 ZCCHC24 UGT1A3 ZNHIT3 EDEM1 EPN1 UGT1A1 UGT1A4 ELMOD1 FUS UGT1A9 TGFB2 P2RY1 ACOT7 KLF12 miR-141/200a UGT1A6 RWDD4 CCDC6 YAF2 UGT1A5 CDK8 FAM188A RFX7 IQCJ-SCHIP1 PGRMC2 TTR CTNND2 TFRC CAMK2D DMWD ANP32E SLC18A2 UGT1A7 C20orf24 DR1 C1orf21 IGSF6 KLF4 42801 SOBP HMG20A FNDC4 LPP YY1 DEK RAB11FIP4 BRMS1L LPAR3 SLC20A1 PRMT2 KIAA0247 LRRC59 NOTCH3 CNOT8 CDC42EP3 LUC7L2 TMEM170B KSR1 C7orf55-LUC7L2 BRI3BP IDE RBMS1 MAGED4B NME1 UBXN2B MEGF9 PAX6 ZNF385D EIF4E RNF141 SLC35C1 LHX1 NDUFA4 BLOC1S4 RGS7BP MSANTD4 PLP2 SP1 C5orf22 MAFG miR-7 SLC25A15 C1orf226 ATG4A NREP VDAC1 POLE4 OST4 LRRC8B NXT2 CACNB4 TRMT13 RYK PIGH TGIF2-C20orf24 ZEB1 IRS2 EIF4EBP2 ELAVL2 PRKACB YWHAG RSBN1 SMARCD1 PFN2 GJC1 FBXL14 RAF1 CNPPD1 IDS FAM131B SETD8 SNCA CNN3 VPS26A HERPUD2 TMOD3 PMAIP1 NOG PAN2 LEMD3 GRP MSN CACNG7 LIMD1 STX5 CNEP1R1 NTF3 ACTC1 CHAMP1 IARS APOO GAL3ST3 CDR1as RECK DZIP1 BNC2 CKAP4 CFL2 AP1S2 CALN1 NR5A2 EPS8 PPIF ARID4A USP27X MCFD2 GTF2E1 C6orf120 SMIM5 CCDC177 DNAJB5 MMD INTS8 KDELC1 miR-200b/c PAPD5 CRTAP RAP1B SESN1 FAM8A1 PTHLH PRDM16 NOVA1 PSAT1 TCEB1 SNAP25 HIPK3 LPAR1 IMMP2L PTPN21 WAPAL SERPINI1LBR PAIP2 XKR8 KIAA0101 MARCKS TMEFF2 CCNJ CRKL FLI1 LHFP B3GNT1 SLC14A1OSTM1 FEZ2 GPM6A LCA5 RBFOX3 CEP41 RANBP10 RP6-24A23.6 GIT2 ASF1A HMGB3 ZFAND6 ERRFI1 PPP4R2 RASSF8 ZFPM2 CDK17 RAP2C ZNF532 VASH2 YPEL2 SLC35F4 SEC23A ZNF131 LIN7BC16orf52

Figure 5 The network of microRNA and its predicted target genes. A network consisted of miR-7, miR-200b/c, miR-200a/141 and their target genes. The selected DEMs were imported to TargetScan (Release 7.1, http://www.targetscan.org/) which can search the target genes based on the conserved sites matching the seed regions of miRNAs. Cumulative weighted context++ score ≤−0.4 were set as the threshold. The miRNA-target mRNA network was constructed by Cytoscape. DEMs, differentially expressed genes.

>0.7 and P value <0.01 were applied to select gene-miRNA Discussion relationship for further analysis. Finally, the network for a total of 104 gene probes and 5 miRNAs was constructed Despite advance in medical and surgical therapy, the using Cytoscape (Figure 6). The data provide a useful colon cancer is the third common diseases in the world, resource for studying and predicting the relationship of moreover, in developed countries, colon cancer has a higher genes and miRNAs for colon cancer. incidence than developing countries (20). In developed

224156_x_at 219255_x_at 224361_s_at 235924_at 203729_at 229842_at 232361_s_at 204751_x_at 232164_s_a213285_at t 205455_at

201650_at 219850_s_a229377_at t 203780_at 203953_s_at 227239_at 202826_at 231941_s_at 225710_at 201428_at 226961_at 226185_at 226403_a236279_at t 218966_at 224650_at 235141_at 200600_at 202005_a232202_at t 37117_at 232165_at 225792_at 239148_a203954_x_at t 205709_s_at 224189_x_at 210058_at 227163_at miR-200b 219513_s_at 219395_at 229372_at 202525_at miR-200c 201839_s_at 210827_s_at 1553132_a_at 219429_at miR-200a 203453_at 210715_s_at 206683_at 216905_s_at 201130_s_at 218644_at

202597_at 205980_s_at 222932_at miR-141 225793_at 219127_at

1552477_a_at 225671_at 225822_at 203713_s_at 226622_at 1554246_at 225045_at S100A14 202790_at 225354_s_at 218677_at 219388_at 218960_at 90265_at

218186_at 230323_s_at 222746_s_at 206043_s_at 229223_at 216641_s_at 235148_at 235955_at 210059_s_at 201426_s_at 203287_at 219411_at 218792_s_at 212263_at 212262_at

226546_at

KLK6 miR-7 204733_at 212526_at

1555829_at 212265_at

235845_at 228912_at ANO1 219404_at 218804_at

205506_at 229777_at 218704_at 229215_at 223761_at 230772_at 219756_s_at 1555383_a_at

Figure 6 Correlation network of gene probes and microRNA. The correlation between gene expression and microRNA expression was calculated across the NCI-60 cell lines. Totals of 104 gene probes and 5 microRNAs were selected with a significant correlation displayed with connecting edges.

© Translational Cancer Research. All rights reserved. tcr.amegroups.com Transl Cancer Res 2018;7(1):17-29 26 Ma et al. Identification of biomarkers for colon cancer countries, more than 65% of diseases are colon cancers (21). As a unique position among the ACSL gene family, The characteristics of colon cancer are invasive and spread ACSL5 at chromosome 10q25.1–q25.2, mainly located to other tissues of the human body. The lethality of lethal on mitochondria and regulated apoptosis of cells (36). malignancy globally is due to the difficulties in detecting While, human ACSL5 exert activation usually through colon cancer at an early state. Therefore, it is essential certain apoptosis pathway (37).As a factor associated with and beneficial to figure out the etiological factors and metastatic, epithelial splicing regulatory protein 1 (ESRP1), mechanisms of colon cancer to improve prevention and is an RNA-binding proteins (RBPs) and splicing factor (38). survival rate (22). Recently, microarray technology has been ESRP1 have ability to bind the 5’UTR of a wide range of an effective tool for revealing the expression of general cancer-related genes and alter their translation to exhibit it genetic alteration in different physiological and pathological functions, as a tumor expression ESRP1 inhibit epithelial to status, which enables the discovery of new targets for mesenchymal transition (EMT) in various cancers (39-42). diagnosis, therapeutic, and prognosis of malignant cancers. However, in the present study, in cancer cells over- In this study, a total of 560 gene probes, including 285 expressing of ESRP1 cloud enhance their metastatic significantly up-regulated genes and 265 significantly down- potential (43). Moreover, metastatic progression of regulated genes were identified used NCI-60 microarray carcinoma cells is a positive association with the level of dataset. These differential expressed genes mainly were CD44v6 isoform, a target of ESRP1 (44). And ESRP1 could enriched in cell proliferation, migration, invasion, apoptosis target growth factor receptor including FGFR1/2 pathway, and survival rate. Among those genes, seven genes were AKT signaling and Snail activation to promote cancer selected as potential gene biomarker candidates, including progression (38). KLK6, S100A14, ANO1, LYZ, ESRP1, ACSLC5 and miRNA, a type of small non-coding RNA molecule FOXQ1. Anoctamin-1 (ANO1) as transmembrane protein which consists of 18–25 nucleotides. It can regulate target 16A (TMEM16A) was found in a number of cancers, gene expression to affect cell biological process such as such as gastrointestinal stromal tumor (GIST), BR cancer cell apoptosis, cell proliferation, cell differentiation, and (23,24). Moreover, ANO1 plays an important role in cell cell metastatic (45). Increasing evidence demonstrated proliferation, tumorigenesis and progression (25). Recent that some miRNA can affect the pathogenesis of various studies shown that in various cell types of ANO1 could cancers, including colon cancer. Therefore, miRNA may activate calcium-activated chloride channels (CaCCs), be biomarker for tumor diagnosis and treatment (46). In molecular and electrophysiological studies indicated that the present study, we identified 27 DEMs for colon cancer ANO1 plays a critical role in metastatic tumor (26,27). cells. Especially, five up-regulated miRNAs (miR-7, miR- Kallikrein-related peptidase (KLK) is a member of the 200a, miR-200b, miR-200c, and miR-141) were identified. human kallikrein gene family, in length, KLK6 encodes of MiRNA-7 plays a pivotal roles on tumors growth and 224 amino acid with trypsin-like activity (28). In addition, metastatic by affecting the region of focal adhesion kinase the KLK6 gene was validated to be a secreted protein which (FAK) mRNA, which inhibit the level of FAK protein (47). shares significant homologies with other kallikreins and the In cancer cells, miRNA-7 inhibits the expression of FAK, enzyme (29). As a new potential biomarker, the expression FAK is mainly located at the cell cytoplasm and promotes of KLK6 was significantly increased in patients with cancer the secretion of MMPs, and therefore, miRNA-7 may in an late-stage, accumulating evidence demonstrates that be targeting the level of MMPs to exert its function (48). KLK6 may play a role in the development and progression MiRNA-200 family consisting of five members (miRNA- of cancer (30). S100A14 is one protein of S1004 proteins. 200a, miRNA-200b, miRNA-200c, miRNA-141 and It has been reported that play a role in cancer cells invasion miRNA-429) which participating in the progression of and metastasis (31). S100A14 protein exert its function EMT (49). As a member of miRNA-200, miRNA-429 which regulates tumor invasion by modulating the level expression in colon cancer was significantly up-regulated of matrix metalloproteinase MMP-1 and MMP-9 (32). compared with LC, as reported, miRNA-429 have the In addition, S100A14 could regulate the expression of ability to inhibit loss of E-cadherin undergoing EMT (50). MMP-2 and p-53 dependent pathway to affect tumor cells Similar results that, miRNA-200a and miRNA-200b were invasion (33). As a member of the long-chain acyl-CoA up-regulated in colon cancer cells and tissue, moreover, synthetase (ACSL) gene family, ACSL5 is overexpression miRNA-200a and miRNA-200b showed higher expression on tumor versus other members of ACSL family (34,35). in cancer tissue than normal tissue which were identified as

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Cite this article as: Ma Q, Song J, Chen Z, Li L, Wang B, He N. Identification of potential gene and microRNA biomarkers for colon cancer by an integrated bioinformatical approach. Transl Cancer Res 2018;7(1):17-29. doi: 10.21037/tcr.2017.12.09