BioMed Research International

BioMed Research International / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 6341967 | 13 pages |

Predicting MicroRNA Target Genes and Identifying Hub Genes in IIA Stage Colon Cancer Patients Using Bioinformatics Analysis

Academic Editor: Giandomenico Roviello
Received04 Oct 2018
Accepted26 Dec 2018
Published07 Feb 2019


Background. Colon cancer is a heterogeneous disease, differing in clinical symptoms, epigenetics, and prognosis for each individual patient. Identifying the core genes is important for early diagnoses and it provides a more precise method for treating colon cancer. Materials and Methods. In this study, we wanted to pinpoint these core genes so we obtained GSE101502 microRNA profiles from the GEO database, which resulted in 17 differential expressed microRNAs that were identified by GEO2R analysis. Then, 875 upregulated and 2920 downregulated target genes were predicted by FunRich. GO and KEGG pathway were used to do enrich analysis. Results. GO analysis indicated that upregulated genes were significantly enriched in the regulation of cell communication and signaling and in nervous system development, while the downregulated genes were significantly enriched in nervous system development and regulation of transcription from the RNA polymerase II promoter. KEGG pathway analysis suggested that the upregulated genes were enriched in axon guidance, MAPK signaling pathway, and endocytosis, while the downregulated genes existed in pathways in cancer, focal adhesion, and PI3K-Akt signaling pathway. The top four molecules including 82 hub genes were identified from the PPI network and involved in endocytosis, spliceosome, TGF-beta signaling pathway, and lysosome. Finally, NUDT21, GNB1, CLINT1, and COL1A2 core gene were selected due to their correlation with the prognosis of IIA stage colon cancer. Conclusion. this study suggested that NUDT21, GNB1, CLINT1, and COL1A2 might be the core genes for colon cancer that play an important role in the development and prognosis of IIA stage colon cancer.

1. Introduction

Colon cancer is the second most commonly diagnosed cancer and the fourth leading cause of cancer death worldwide. It has been estimated that there were 1,360,600 new cases and 693,900 deaths of colon and rectum cancer worldwide in 2012 [1]. The American Cancer Society estimated that the incidence of colon cancer (71%) is higher than rectum cancer (29%) [2]. Colon cancer is a heterogeneous disease, differing in clinical symptoms, gene mutation or alteration, epigenetics, prognosis, and the response to therapy [3]. It is reported that multiple genes and pathways play a role in the occurrence and development of colon cancer [4]. Moreover, colon cancer is a global burden due to the rising healthcare costs to manage the disease.

MicroRNA (miRNA) is a small endogenous, noncoding RNA molecule, which is composed of approximately 21-25 nucleotides. These small miRNAs usually target one or more mRNA, regulating gene expression through translation level inhibition or breaking target mRNAs [5]. miRNAs characterize an innovative epigenetic mechanism that controls gene expression in several pathological conditions within the cancer tissues [6], and the dysfunction of miRNA is associated with different cancers. For example, Ruan et al. [7] reported that miR-1181 and miR-4314 were associated with ovarian cancer through downregulated FOXP1 and GRWD1/IP6K1/NEGR1 whereas Zhang et al. [8] indicated that the tumor suppressive role of miR-149 targeted the AKT-mTOR pathway in human hepatocellular carcinoma. miR-149-5p inhibited epithelial-to-mesenchymal transition (EMT) of cells via targeting FOXM1 in non–small cell lung cancer [9]. miR-203a-suppressed cell proliferation in human gastric cancer via targeting E2F transcription factor 3 has been described by Yang et al. [10]. Moreover, Liu et al. [11] suggested that Jun/miR-22/HuR regulatory axis may play a role in colorectal cancer progression. It is reported that no reliable biomarker profile has been identified in order to discriminate cancerous from normal tissue [12]. Many miRNA expression profiling experiments on colon cancer carcinogenesis have been published during the last several years using microarray, RNA-seq, DNA-seq, and ChIP-seq technology which have exposed hundreds of differentially expressed miRNA or genes involved in biological processes, molecular functions, or different pathways [1315]. Therefore, how to predict genes using miRNA and identify those target genes is vital to understand the molecular mechanism, develop early diagnostics, and precisely treat colon cancer.

Gene Expression Omnibus (GEO) is an open database that provides high quantity miRNA expression data [16]. With the emerging development of high-throughput next generation sequencing in the biological sciences, the identification of core genes and the extraction of useful information from large set of gene data are essential. Therefore, we used bioinformatics analysis to solve this problem. One problem we ran into though was that miRNA targets are difficult to characterize as each miRNA has multiple gene targets so the accurate identification of miRNA and miRNA interaction remains a challenge. In this condition, several tools have been developed for miRNA target prediction with one of them being comprehensive bioinformatics analysis, which we used to analyze the expression of differential miRNA and find the core genes that exist in the development and progression of colon cancer.

In the present study, we will examine related miRNA datasets of human colon cancer from the GEO database. Overall, miRNA expression profiles of cancer tissues in patients with colon cancer were compared with those patients with normal colon tissue to identify the differential expressed miRNA. We used FunRich software to predict the target genes from the differential expressed miRNA, and the STRING (Retrieval of Interacting Genes) and Cytoscape software were used to analyze the target genes and select hub genes. Furthermore, the DVIAD online tool was used to perform enrichment analysis, and the GEPIA was used to investigate the overall survival and gene expression level of hub genes. In the end, we figured out which core genes were closely related to colon cancer, which might help researchers to examine molecular mechanisms involved in the disease prognosis, thus providing information on the precise gene therapy for colon cancer research.

2. Materials and Methods

2.1. Database and MicroRNA Selection

GEO (Gene Expression Omnibus, is a public genomics database, including gene array, RNA-seq, DNA-seq, and ChIP-seq based data [16]. “Colon cancer” AND “microRNA” AND “Homo sapiens” keywords were used to search related gene expression profiles by GEO datasets. The GSE101502 profile included three IIA stage colon cancer tissues and three normal colon mucosa tissues.

2.2. Identifying Differentially Expressed MicroRNA

GEO2R ( is an online statistics tool that allows user to compare different groups of samples to identify differential microRNA across experimental conditions. We performed a T test to identify differential microRNA. ≥ 2 cutoff and P value < 0.05 were considered to have a statistically significant difference whereas logFC ≥ 2 was upregulated microRNA and logFC ≤ -2 was downregulated [16].

2.3. Predicting Target Genes

FunRich (functional enrichment) is an analysis tool used for functional enrichment and protein-protein interaction network analysis for genes or proteins. The microRNA enrichment function in FunRich could be used to perform miRNA enrichment analysis, to predict targets of microRNAs, or to find microRNAs through given target genes. Functional analysis of differentially expressed microRNA target genes was conducted to predict target genes with FunRich [17].

2.4. GO and KEGG Pathway Analysis of DEGs

GO (Gene Ontology) analysis is a common advantage method for annotating genes and classifying characteristic biological attributes for high-throughput genome and transcriptome data. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database used in conducting searches regarding genomes, biological pathways, diseases, drugs, and chemical substances. DAVID (Database for Annotation, Visualization and Integrated Discovery, is an online bioinformatics tool that is utilized to provide the functional understanding of large lists of genes. P < 0.05 was set as the cutoff criterion. We conducted key biological processes (BP), molecular functions (MF), cellular components (CC), and pathways among those DEGs by DAVID [18, 19].

2.5. PPI Network and Modules Analysis

STRING (the Retrieval of Interacting Genes, is web tool created to evaluate PPI (protein-protein interaction) networks information. To detect the potential relationship among those DEGs, we used Cytoscape software and a confidence score of ≥ 0.4 was set as the cutoff criterion. MCODE (Molecular Complex Detection) app in Cytoscape was utilized to display modules of PPI network with node score cutoff = 0.2, k-core = 2, max. depth from seed = 100, and degree cutoff = 2. Then, the top four molecules were mapped into STRING [20, 21].

2.6. Comparison of the Hub Genes Expression Level

The GEPIA ( is an interactive online tool for analyzing the RNA-seq expression data of 9,736 tumor samples and 8,587 normal samples from the TCGA (the Cancer Genome Atlas dataset, found by NCI and NHGRI, multidimensional maps of important genomic changes in 33 types of cancer) and the GTEx projects (Genotype-Tissue Expression projects, launched by NIH, is a tissue bank and resource for biological research), with a standard processing pipeline. It offers customizable functions such as tumor and normal tissue gene differential expression analysis, and we can determine the expression of hub genes in colon cancer tissues and normal colon mucosa tissues. Survival analysis is then performed to show the high expression and low expression hub genes relationship of colon cancer and overall survival. P<0.05 was considered as significantly different. The boxplot was conducted to visualize the association between cancer and normal tissue [22].

3. Results

3.1. MicroRNA Data

The gene expression profiles for GSE101502, “microRNA expression profiling in human colon cancer”, were obtained from GEO datasets ( GSE101502, which was based on the GPL21439 platform (miRCURY LNA microRNA Array, 7th generation hsa, mmu, and rno [miRBase 21; probe ID version]), was submitted by Huang et al. on Jul 18th, 2017. The GSE101502 dataset contained three male patients’ tissues comprised of six samples including three IIA stage colon cancer tissues and three normal colon mucosa tissues. Table 1 showed the characteristics of tissues’ information from GSE101502.

GroupAccessionPatient No.Disease stateStageStatus after 1 yearsAgeSexTissue

Cancer groupGSM2705118Patient 1Primary colon cancerTNM:IIAalive62maleCancer tissue
GSM2705119Patient 2Primary colon cancerTNM:IIAalive62maleCancer tissue
GSM2705120Patient 3Primary colon cancerTNM:IIAalive64maleCancer tissue
Normal groupGSM2705121Patient 4Primary colon cancerTNM:IIAalive62maleAdjacent normal mucosa
GSM2705122Patient 5Primary colon cancerTNM:IIAalive62maleAdjacent normal mucosa
GSM2705123Patient 6Primary colon cancerTNM:IIAalive64maleAdjacent normal mucosa

3.2. Identification of Differentially Expressed MicroRNA

The six samples were divided into two groups (cancer and normal tissue group), and the differentially expressed miRNA analysis was conducted by GEO2R ( P values <0.05, were considered as differentially expressed microRNA. LogFC > 2 was upregulated microRNA, LogFC < -2 was down-regulated.  Table 2 showed the identification of differentially expressed miRNA in the two groups.

miRNA Nameadj.P.ValP.ValuetBlogFC


3.3. Prediction of Target Genes

miRNA enrichment was used to predict potential target genes from differentially expressed miRNA. The up- and downregulated microRNA were inputted into the FunRich software tool, respectively. There were 875 up- and 2920 downregulated target genes found.

3.4. GO Function and KEGG Pathway Enrichment Analysis

All target genes were imported into the online analysis tool, DAVID, to identify potential GO categories and KEGG pathways. GO analysis results revealed that upregulated target genes were expressively enriched in biological processes (BP), including regulation of cell communication, regulation of signaling, and nervous system development; in molecular function (MF) including receptor signaling protein activity, transcription factor activity, RNA polymerase II core promoter proximal region sequence-specific binding, and enzyme binding; and in cell component (CC) including cell junction, cell leading edge, and adherens junction (Table 3). The downregulated target genes were expressively enriched in BP, including nervous system development, regulation of transcription from RNA polymerase II promoter, and positive regulation of RNA metabolic process; in MF including RNA polymerase II transcription factor activity, sequence-specific DNA binding, regulatory region nucleic acid binding, and regulatory region DNA binding; in CC, including nucleoplasm, neuron projection, and neuron part (Table 3). KEGG pathway analysis showed that the upregulated target genes were enriched in axon guidance, MAPK signaling pathway, endocytosis, proteoglycans in cancer, and the FoxO signaling pathway, while the downregulated target genes were enriched in pathways in cancer, focal adhesion, PI3K-Akt signaling pathway, small cell lung cancer, and signaling pathways regulating pluripotency of stem cells. Table 4 shows the most significantly enriched pathways of the upregulated target genes and downregulated target genes were performed by KEGG analysis.

regulationCategoryTermCountP-ValueFold EnrichmentFDR

upGOTERM_BP_FATGO:0010646~regulation of cell communication23424.920132.36E-121.5267447784.63E-09
upGOTERM_BP_FATGO:0023051~regulation of signaling23625.133124.03E-121.5147158037.91E-09
upGOTERM_BP_FATGO:0007399~nervous system development18019.169338.89E-121.634444961.75E-08
upGOTERM_BP_FATGO:0010604~positive regulation of macromolecule metabolic process22023.429182.72E-111.5178260655.34E-08
upGOTERM_BP_FATGO:0007167~enzyme linked receptor protein signaling pathway9810.436632.73E-112.0241152175.37E-08
upGOTERM_MF_FATGO:0005057~receptor signaling protein activity272.8753992.69E-083.5804760194.39E-05
upGOTERM_MF_FATGO:0000982~transcription factor activity, RNA polymerase II core promoter proximal region sequence-specific binding414.3663471.00E-062.330151060.001631
upGOTERM_MF_FATGO:0019899~enzyme binding13614.483491.81E-061.4819136820.002953
upGOTERM_MF_FATGO:0008092~cytoskeletal protein binding757.987223.96E-061.7281651390.006455
upGOTERM_MF_FATGO:0019904~protein domain specific binding596.283284.69E-061.8731733930.00764
upGOTERM_CC_FATGO:0030054~cell junction12313.099042.20E-091.7116734033.35E-06
upGOTERM_CC_FATGO:0031252~cell leading edge444.6858362.05E-072.3785067073.12E-04
upGOTERM_CC_FATGO:0005912~adherens junction697.3482433.11E-071.9079618094.74E-04
upGOTERM_CC_FATGO:0070161~anchoring junction697.3482437.57E-071.8623424690.001153
downGOTERM_BP_FATGO:0007399~nervous system development54618.86013.19E-361.6193876.50E-33
downGOTERM_BP_FATGO:0006357~regulation of transcription from RNA polymerase II promoter46816.16584.34E-321.641458.83E-29
downGOTERM_BP_FATGO:0051254~positive regulation of RNA metabolic process37512.953377.66E-291.7056611.56E-25
downGOTERM_BP_FATGO:0006366~transcription from RNA polymerase II promoter45315.647671.98E-281.6019344.03E-25
downGOTERM_BP_FATGO:1903508~positive regulation of nucleic acid-templated transcription36012.435232.90E-281.7175465.91E-25
downGOTERM_MF_FATGO:0000981~RNA polymerase II transcription factor activity, sequence-specific DNA binding1886.4939553.54E-171.7992496.12E-14
downGOTERM_MF_FATGO:0001067~regulatory region nucleic acid binding2257.7720217.28E-161.6633861.34E-12
downGOTERM_MF_FATGO:0000975~regulatory region DNA binding2247.7374781.35E-151.6579282.31E-12
downGOTERM_MF_FATGO:0044212~transcription regulatory region DNA binding2237.7029361.78E-151.6563313.08E-12
downGOTERM_MF_FATGO:0043565~sequence-specific DNA binding2679.2227983.59E-151.5663146.15E-12
downGOTERM_CC_FATGO:0043005~neuron projection2338.0483595.31E-121.5227838.54E-09
downGOTERM_CC_FATGO:0097458~neuron part29710.259078.60E-111.4066491.38E-07
downGOTERM_CC_FATGO:0098644~complex of collagen trimers180.6217629.04E-104.9408761.45E-06

RegulatedCategoryTermCount%P-ValueFold EnrichmentFDR

UpKEGG_PATHWAYhsa04360:Axon guidance212.247.30E-063.1738850.009462
UpKEGG_PATHWAYhsa04010:MAPK signaling pathway313.301.94E-052.3334420.025073
UpKEGG_PATHWAYhsa05205:Proteoglycans in cancer262.773.53E-052.4952780.045696
UpKEGG_PATHWAYhsa04068:FoxO signaling pathway202.135.61E-052.8648420.072721
DownKEGG_PATHWAYhsa05200:Pathways in cancer1083.731.58E-101.8102300262.10E-07
DownKEGG_PATHWAYhsa04510:Focal adhesion662.281.44E-092.1104704431.90E-06
DownKEGG_PATHWAYhsa04151:PI3K-Akt signaling pathway913.144.76E-081.7375001736.31E-05
DownKEGG_PATHWAYhsa05222:Small cell lung cancer321.119.87E-072.4798968210.001306893
DownKEGG_PATHWAYhsa04550:Signaling pathways regulating pluripotency of stem cells441.522.04E-062.0702710060.002694908

3.5. Module Screening and Hub Gene Selecting from the Protein-Protein Interaction (PPI) Network

All target genes were imported into the STRING database to conduct the PPI network. A combined score of > 0.4 of the nodes was considered as significance (Figure 1). Then, the results of the PPI network were exported as.txt and imported to Cytoscape software which was analyzed using plug-ins MCODE. Finally, the top four significant modules were selected and considered as hub genes. The 82 hub genes are illustrated in Figures 2(a), 2(c), 2(e), and 2(g). The functional annotations of those genes were analyzed by DAVID. Enrichment analysis indicated that the genes in modules 1 through 4 were mainly associated with endocytosis, spliceosome, TGF-beta signaling pathway, and lysosome (Figures 2(b), 2(d), 2(f), and 2(h)).

3.6. Survival Plots and Expression Level of Hub Genes

We used survival analysis by GEPIA (Gene Expression Profiling Interactive Analysis) to detect the overall survival of 82 hub genes between the high and low expression groups. It was found that high expressions of NUDT21 (HR= 0.57, P = 0.023) (Figure 1(a)), GNB1 (HR=0.028, P=0.026) (Figure 1(c)), and CLINT1 (HR=0.6, P=0.043) (Figure 1(d)) were associated with better overall survival for colon cancer patients. However, a high expression of COL1A2 (HR 1.8, P = 0.017) (Figure 1(b)) was associated with worse overall survival for colon cancer patients (Figure 3), and there was no statistical significance in the other 78 hub genes. Taken together, NUDT21, GNB1, CLINT1, and COL1A2 were considered as core genes with a close relationship to colon cancer. Then, we used GEPIA analysis to explore the core genes’ expression level between colon cancer and normal tissue (Figures 4(a), 4(b), 4(c), and 4(d)).

4. Discussion

Pathogenesis of colon cancer is association with gene mutation, epigenetics, and the CpG island methylator phenotype [23]. In order to diagnose this disease early to precisely and effectively treat colon cancer, understanding the molecular mechanism is imperative. Microarray and high-throughput next generation sequencing have been widely utilized in order to predict the potential therapeutic targets gene of colon cancer as both techniques could provide expression levels for thousands of genes. miRNA regulates the progression of the tumor by regulating these target genes, and some miRNAs have been identified as being involved in several types of cancer [24, 25]. Therefore, it is of great significance to study the expression profile of miRNAs and predict the target genes in colon cancer. In this study, we extracted the data from GSE101502 and identified two upregulated and 15 downregulated differential expressed microRNAs between colon cancer tissue and adjacent normal mucosa tissue using bioinformatics analysis [26, 27]. And we found that NUDT21, GNB1, CLINT1, and COL1A2 might be the potential core genes that play an important role in the development and prognosis in IIA stage colon cancer.

In this study, the GO analysis showed that these potentially upregulated genes were mainly enriched in the regulation of cell communication, receptor signaling protein activity, and cell junction. Potential downregulated genes were involved in nervous system development, RNA polymerase II transcription factor activity, sequence-specific DNA binding, and nucleoplasm. Pinto et al. [28] indicated that there is a complicated cell communication in response to ionizing radiation revealed by primary human macrophage-cancer cell culture. Kim et al. [29] reported that IFITM1 expression was positively correlated with galectin-3 via receptor signaling protein activity in human colon cancer cells. The cell junctions might lead to cancer due to the differences in cell junctions for colorectal cancer [30]. Moreover, nervous system development also plays a key role in colorectal cancer metastasis [31, 32]. RNA polymerase II transcription factor contains sequence-specific DNA binding, transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins [33, 34]. Between the nucleoplasm and cytoplasm called perinuclear, the signal transmission becomes abnormal by the perinucleus in malignant cell transformation [35]. All those studies indicated that the molecular functions of those up- and downregulated genes are related to colon cancer.

Moreover, the KEGG pathways for upregulated genes were enriched in axon guidance, MAPK signaling pathway, endocytosis, proteoglycans cans in cancer, and FoxO signaling pathway. Downregulated genes were involved in the pathways in cancer, focal adhesion, PI3K-Akt signaling pathway, small cell lung cancer, and regulation of signaling pathways in the pluripotency of stem cells. The axon guidance indicates that netrin 1 and Slits are causally involved in human cancer [36]. The ERK MAPK (extracellular-signal-regulated kinases) is one of the subfamilies of MAPK (mitogen-activated protein kinases), and it has been found that overexpression and activation of ERK MAPK play a role in the progression of colorectal cancer [37]. Through PIP2 mediated vinculin activation, PIPKIγ might positively regulate focal adhesion dynamics and colon cancer cell invasion [38]. The PI3K/AKT pathway plays an important role in the prognostic and predictive values in colorectal cancer [39]. Evidence suggests that endocytosis, proteoglycans in cancer, FoxO signaling pathway, and regulation of signaling pathways in the pluripotency of stem cells are all associated with colorectal cancer [4042].

Finally, NUDT21, COL1A2, GNB1, and CLINT1 closely related to the overall survival of colon cancer were selected as core genes. COL1A2 is collagen type I alpha 2 chain, the fibrillary collagen detected in most connective tissues. This observation suggests that patients with a high expression of COL1A2 have a worse prognosis. Pekow et al. indicated that downregulating miR-4728-3p reduces ulcerative colitis associated colon cancers, and miR-4728-3p is a regulator of COL1A2 [43]. NUDT21 is Nudix hydrolase 21, belonging to the Nudix family of hydrolases. GNB1 is G protein subunit beta 1. Wazir et al. researched on 136 human breast cancer tissues and 31 normal tissues, undertook reverse transcription and quantitative polymerase chain reaction, and suggested that GNB1 plays an important character in the mTOR-related antiapoptosis pathway and might potentially be targeted in breast cancer [44]. CLINT1 is Clathrin interactor 1. Ajiro et al. [45] indicated that SRSF3 regulates a lot of genes including CLINT1 affecting gene expression to keep cell homeostasis. Moreover, further deeply investigated molecular mechanism of NUDT21, COL1A2, GNB1, CLINT1, and colon cancer is necessary; it is also the limitation of this study.

5. Conclusion

In conclusion, this study showed that NUDT21, GNB1, CLINT1, and COL1A2 might be the potential core genes that play an important role in the development and prognosis in IIA stage colon cancer. After discovering this, we have come to the conclusion that a series of experiments and further deeply investigated molecular mechanism of those four core genes should be designed to confirm the results of this study.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

All the authors have no conflicts of interest.

Authors’ Contributions

Zhiyong Dong and Wei Lin contributed equally to this work.


We would like to thank Jun Bai (Feinberg School of Medicine, Northwestern University) for the help in reviewing and editing this paper.


  1. L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, and J. Lortet-Tieulent, “Global cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015. View at: Publisher Site | Google Scholar
  2. R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer Statistics, 2017,” CA: A Cancer Journal for Clinicians, vol. 67, no. 1, pp. 7–30, 2017. View at: Publisher Site | Google Scholar
  3. M. C. Merlano, C. Granetto, E. Fea, V. Ricci, and O. Garrone, “Heterogeneity of colon cancer: from bench to bedside,” ESMO Open, vol. 2, no. 3, Article ID e000218, 2017. View at: Publisher Site | Google Scholar
  4. L.-A. MacFarlane and P. R. Murphy, “MicroRNA: biogenesis, function and role in cancer,” Current Genomics, vol. 11, no. 7, pp. 537–561, 2010. View at: Publisher Site | Google Scholar
  5. T. A. Farazi, S. A. Juranek, and T. Tuschl, “The growing catalog of small RNAs and their association with distinct Argonaute/Piwi family members,” Development, vol. 135, no. 7, pp. 1201–1214, 2008. View at: Publisher Site | Google Scholar
  6. A. Nakamura, T. Osonoi, and Y. Terauchi, “Relationship between urinary sodium excretion and pioglitazone-induced edema,” Journal of Diabetes Investigation, vol. 1, no. 5, pp. 208–211, 2010. View at: Publisher Site | Google Scholar
  7. L. Ruan, Y. Xie, F. Liu, and X. Chen, “Serum miR-1181 and miR-4314 associated with ovarian cancer: MiRNA microarray data analysis for a pilot study,” European Journal of Obstetrics & Gynecology and Reproductive Biology, vol. 222, pp. 31–38, 2018. View at: Publisher Site | Google Scholar
  8. Y. Zhang, X. Guo, L. Xiong et al., “Comprehensive analysis of microRNA-regulated protein interaction network reveals the tumor suppressive role of microRNA-149 in human hepatocellular carcinoma via targeting AKT-mTOR pathway,” Molecular Cancer, vol. 13, no. 1, article 253, 2014. View at: Google Scholar
  9. Y. Ke, W. Zhao, J. Xiong, and R. Cao, “miR-149 Inhibits Non-Small-Cell Lung Cancer Cells EMT by Targeting FOXM1,” Biochemistry Research International, vol. 2013, Article ID 506731, 8 pages, 2013. View at: Publisher Site | Google Scholar
  10. H. Yang, L. Wang, X. Tang, and W. Bai, “miR-203a suppresses cell proliferation by targeting E2F transcription factor 3 in human gastric cancer,” Oncology Letters, vol. 14, no. 6, pp. 7687–7690, 2017. View at: Publisher Site | Google Scholar
  11. Y. Liu, X. Chen, R. Cheng et al., “The Jun/miR-22/HuR regulatory axis contributes to tumourigenesis in colorectal cancer,” Molecular Cancer, vol. 17, no. 1, 2018. View at: Publisher Site | Google Scholar
  12. J. Lascorz, K. Hemminki, and A. Försti, “Systematic enrichment analysis of gene expression profiling studies identifies consensus pathways implicated in colorectal cancer development,” Journal of Carcinogenesis, vol. 10, no. 1, p. 7, 2011. View at: Publisher Site | Google Scholar
  13. M. Rokavec, D. Horst, and H. Hermeking, “Cellular model of colon cancer progression reveals signatures of mRNAs, miRNA, lncRNAs, and epigenetic modifications associated with metastasis,” Cancer Research, vol. 77, no. 8, pp. 1854–1867, 2017. View at: Publisher Site | Google Scholar
  14. Y. Lu, X. Zhao, Q. Liu et al., “lncRNA MIR100HG-derived miR-100 and miR-125b mediate cetuximab resistance via Wnt/β-catenin signaling,” Nature Medicine, vol. 23, no. 11, pp. 1331–1341, 2017. View at: Publisher Site | Google Scholar
  15. E. Li, P. Ji, N. Ouyang et al., “Differential expression of miRNAs in colon cancer between African and Caucasian Americans: Implications for cancer racial health disparities,” International Journal of Oncology, vol. 45, no. 2, pp. 587–594, 2014. View at: Publisher Site | Google Scholar
  16. T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive for functional genomics data sets—update,” Nucleic Acids Research, vol. 41, no. 1, pp. D991–D995, 2013. View at: Publisher Site | Google Scholar
  17. M. Pathan, S. Keerthikumar, D. Chisanga et al., “A novel community driven software for functional enrichment analysis of extracellular vesicles data,” Journal of Extracellular Vesicles (JEV), vol. 6, no. 1, Article ID 1321455, 2017. View at: Publisher Site | Google Scholar
  18. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at: Publisher Site | Google Scholar
  19. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009. View at: Publisher Site | Google Scholar
  20. D. Szklarczyk, A. Franceschini, S. Wyder et al., “STRING v10: protein-protein interaction networks, integrated over the tree of life,” Nucleic Acids Research, vol. 43, pp. D447–D452, 2015. View at: Publisher Site | Google Scholar
  21. P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software Environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. View at: Publisher Site | Google Scholar
  22. Z. Tang, C. Li, B. Kang, G. Gao, C. Li, and Z. Zhang, “GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses,” Nucleic Acids Research, vol. 45, no. 1, pp. W98–W102, 2017. View at: Publisher Site | Google Scholar
  23. M. Toyota, N. Ahuja, M. Ohe-Toyota, J. G. Herman, S. B. Baylin, and J. J. Issa, “CpG island methylator phenotype in colorectal cancer,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 96, no. 15, pp. 8681–8686, 1999. View at: Publisher Site | Google Scholar
  24. A. Lujambio and S. W. Lowe, “The microcosmos of cancer,” Nature, vol. 482, no. 7385, pp. 347–355, 2012. View at: Publisher Site | Google Scholar
  25. Y. W. Kong, D. Ferland-McCollough, T. J. Jackson, and M. Bushell, “microRNAs in cancer management,” The Lancet Oncology, vol. 13, no. 6, pp. e249–e258, 2012. View at: Publisher Site | Google Scholar
  26. R. Apweiler, A. Bairoch, C. H. Wu et al., “UniProt: the universal protein knowledgebase,” Nucleic Acids Research, vol. 32, pp. D115–D119, 2004. View at: Publisher Site | Google Scholar
  27. M. Pathan, S. Keerthikumar, C.-S. Ang et al., “FunRich: An open access standalone functional enrichment and interaction network analysis tool,” Proteomics, vol. 15, no. 15, pp. 2597–2601, 2015. View at: Publisher Site | Google Scholar
  28. A. T. Pinto, M. L. Pinto, S. Velho et al., “Intricate Macrophage-Colorectal Cancer Cell Communication in Response to Radiation,” PLoS ONE, vol. 11, no. 8, Article ID e0160891, 2016. View at: Publisher Site | Google Scholar
  29. K. Kim, Y. Kwon, C. Cho et al., “Galectin-3-independent Down-regulation of GABABR1 due to Treatment with Korean Herbal Extract HAD-B Reduces Proliferation of Human Colon Cancer Cells,” Journal of Pharmacopuncture, vol. 15, no. 3, pp. 19–30, 2012. View at: Publisher Site | Google Scholar
  30. M. Shirdel, Differences in Cell Junctions of Colorectal Cancer with a Pushing or Infiltrative Growth Pattern: The Role of Gap Junction Protein beta 3 (Dissertation), 2013,
  31. S. Li, Y. Sun, and D. Gao, “Role of the nervous system in cancer metastasis,” Oncology Letters, vol. 5, no. 4, pp. 1101–1111, 2013. View at: Publisher Site | Google Scholar
  32. S. Mondaca, V. Hornig, P. Munoz-Schuffenegger, F. Acevedo, M. Garrido, and B. Nervi, “Central nervous system metastasis secondary to colorectal cancer: A retrospective cohort study of 20 cases,” Ecancermedicalscience, vol. 10, no. 705, 2016. View at: Google Scholar
  33. C. S. Parker and J. Topol, “A Drosophila RNA polymerase II transcription factor contains a promoter-region-specific DNA-binding activity,” Cell, vol. 36, no. 2, pp. 357–369, 1984. View at: Publisher Site | Google Scholar
  34. P. J. Mitchell and R. Tjian, “Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins,” Science, vol. 245, no. 4916, pp. 371–378, 1989. View at: Publisher Site | Google Scholar
  35. T. E. Shaiken and A. R. Opekun, “Dissecting the cell to nucleus, perinucleus and cytosol,” Scientific Reports, vol. 4, article 4923, 2014. View at: Google Scholar
  36. P. Mehlen, C. Delloye-Bourgeois, and A. Chédotal, “Novel roles for Slits and netrins: Axon guidance cues as anticancer targets?” Nature Reviews Cancer, vol. 11, no. 3, pp. 188–197, 2011. View at: Publisher Site | Google Scholar
  37. J. Y. Fang and B. C. Richardson, “The MAPK signalling pathways and colorectal cancer,” The Lancet Oncology, vol. 6, no. 5, pp. 322–327, 2005. View at: Publisher Site | Google Scholar
  38. Z. Wu, X. Li, M. Sunkara, H. Spearman, A. J. Morris, and C. Huang, “Pipkiγ regulates focal adhesion dynamics and colon cancer cell invasion,” PLoS ONE, vol. 6, no. 9, Article ID e24775, 2011. View at: Google Scholar
  39. S. A. Danielsen, P. W. Eide, A. Nesbakken, T. Guren, E. Leithe, and R. A. Lothe, “Portrait of the PI3K/AKT pathway in colorectal cancer,” Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, vol. 1855, no. 1, pp. 104–121, 2015. View at: Publisher Site | Google Scholar
  40. I. Mellman and Y. Yarden, “Endocytosis and cancer,” Cold Spring Harbor Perspectives in Biology, vol. 5, no. 12, Article ID a016949, 2013. View at: Publisher Site | Google Scholar
  41. S. F. De Mattos, P. Villalonga, J. Clardy, and E. W.-F. Lam, “FOXO3a mediates the cytotoxic effects of cisplatin in colon cancer cells,” Molecular Cancer Therapeutics, vol. 7, no. 10, pp. 3237–3246, 2008. View at: Publisher Site | Google Scholar
  42. D. Dutta, “Signaling pathways dictating pluripotency in embryonic stem cells,” The International Journal of Developmental Biology, vol. 57, no. 9-10, pp. 667–675, 2013. View at: Publisher Site | Google Scholar
  43. J. Pekow, A. L. Hutchison, K. Meckel et al., “MIR-4728-3p Functions as a Tumor Suppressor in Ulcerative Colitis-associated Colorectal Neoplasia Through Regulation of Focal Adhesion Signaling,” Inflammatory Bowel Diseases, vol. 23, no. 8, pp. 1328–1337, 2017. View at: Publisher Site | Google Scholar
  44. U. Wazir, W. G. Jiang, A. K. Sharma, and K. Mokbel, “Guanine nucleotide binding protein β 1: A novel transduction protein with a possible role in human Breast cancer,” Cancer Genomics & Proteomics, vol. 10, no. 2, pp. 69–74, 2013. View at: Google Scholar
  45. M. Ajiro, R. Jia, Y. Yang, J. Zhu, and Z.-M. Zheng, “A genome landscape of SRSF3-regulated splicing events and gene expression in human osteosarcoma U2OS cells,” Nucleic Acids Research, vol. 44, no. 4, pp. 1854–1870, 2015. View at: Publisher Site | Google Scholar

Copyright © 2019 Zhiyong Dong et al. This 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.

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