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Identification of crucial miRNAs and genes in esophageal squamous cell carcinoma by miRNA-mRNA integrated analysis

Zhong, Xiaowu PhDa,b,c; Huang, Guangcheng MDa; Ma, Qiang MDa; Liao, Hebin MDb; Liu, Chang MDc; Pu, Wenjie MDc; Xu, Lei MDb; Cai, Yan PhDa; Guo, Xiaolan PhDa,b,c,*

Section Editor(s): Kumar., Surinder

doi: 10.1097/MD.0000000000016269
Research Article: Observational Study
Open

Esophageal squamous cell carcinoma (ESCC) is a malignancy that severely threatens human health and carries a high incidence rate and a low 5-year survival rate. MicroRNAs (miRNAs) are commonly accepted as a key regulatory function in human cancer, but the potential regulatory mechanisms of miRNA-mRNA related to ESCC remain poorly understood.

The GSE55857, GSE43732, and GSE6188 miRNA microarray datasets and the gene expression microarray datasets GSE70409, GSE29001, and GSE20347 were downloaded from Gene Expression Omnibus databases. The differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using GEO2R. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). A protein–protein interaction (PPI) network and functional modules were established using the STRING database and were visualized by Cytoscape. Kaplan-Meier analysis was constructed based on The Cancer Genome Atlas (TCGA) database.

In total, 26 DEMs and 280 DEGs that consisted of 96 upregulated and 184 downregulated genes were screened out. A functional enrichment analysis showed that the DEGs were mainly enriched in the ECM-receptor interaction and cytochrome P450 metabolic pathways. In addition, MMP9, PCNA, TOP2A, MMP1, AURKA, MCM2, IVL, CYP2E1, SPRR3, FOS, FLG, TGM1, and CYP2C9 were considered to be hub genes owing to high degrees in the PPI network. MiR-183-5p was with the highest connectivity target genes in hub genes. FOS was predicted to be a common target gene of the significant DEMs. Hsa-miR-9-3p, hsa-miR-34c-3p and FOS were related to patient prognosis and higher expression of the transcripts were associated with a poor OS in patients with ESCC.

Our study revealed the miRNA-mediated hub genes regulatory network as a model for predicting the molecular mechanism of ESCC. This may provide novel insights for unraveling the pathogenesis of ESCC.

aDepartment of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College

bTranslational Medicine Research Center

cDepartment of Laboratory Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.

Correspondence: Xiaolan Guo, Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China (e-mail: alan5200@hotmail.com).

Abbreviations: AURKA = aurora kinase A, BP = biological process, CC = cellular component, CYP2C9 = cytochrome P450 2C9, CYP2E1 = cytochrome P450 2E1, DAVID = database for annotation, visualization and integrated discovery, DEGs = differentially expressed genes, DEMs = differentially expressed miRNAs, EC = esophageal carcinoma, EMT = epithelial-mesenchymal transition, ESCC = esophageal squamous cell carcinoma, FDR = false discovery rate, FLG = filaggrin, GEO = gene expression omnibus, GO = gene ontology, IVL = involucrin, KEGG = Kyoto Encyclopedia of Genes and Genomes, MCM2 = minichromosome maintenance protein 2, MF = molecular function, MMPs = matrix metalloproteinases, OS = overall survival, PCNA = proliferating cell nuclear antigen, PDCD4 = programmed Cell Death 4, PDK1 = phosphoinositide dependent kinase 1, PPI = protein–protein interactions, SPRR3 = small proline-rich repeat protein 3, STRING = search tool for the retrieval of interacting genes/proteins, TGM1 = Transglutaminase-1, TOP2A = topoisomerase IIα.

XZ, GH, and HL contributed equally to this work.

This study was a secondary retrospective study. No ethical approval was required.

The authors acknowledge financial support from the Applied Basic Research Program of Sichuan Province (Grant No. 2017JY0151), and Science and Technology Support Program of Nanchong (Grant Nos. NSMC20170436, NCKL201703, and 18SXHZ0514).

The authors have no conflicts of interest to disclose.

Received January 9, 2019

Received in revised form May 14, 2019

Accepted June 8, 2019

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

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1 Introduction

Esophageal carcinoma (EC) is the ninth most common cancer worldwide, with 572,034 new cases and 508,585 mortalities reported in 2018.[1] The incidence and mortality of EC are rapidly growing worldwide in developing countries. Esophageal squamous cell carcinoma (ESCC) is the predominant histological type of EC, which is more prevalent in the developing world, with very high incidence areas found in East Asia, mainly in China.[2,3] Epidemiological evidence suggests a different risk factor profile including cigarette smoking, alcohol consumption, highly salted meats, and so on.[4,5] If earlier detection and aggressive medical and surgical treatment of EC could be achieved, then the five-year survival rates would exceed 80%.[6,7] Unfortunately, because effective early diagnosis for EC remains elusive, EC often presents in an insidious and nonspecific manner, and the one-year survival rate remains lower than 15%.[8–10] Therefore, the identification of a molecular mechanism and more efficient diagnostic methods for the early stages of EC can help to develop treatments for patients with ESCC.

During recent years, developments in molecular biology have provided gene expressions profiles for studying the pathogenesis of ESCC. Many scholars have carried out in-depth research on the pathogenesis of ESCC at the gene level, through which variety of significant genes were found. For instance, it was found that Programmed Cell Death 4 (PDCD4) is downregulated in ESCC.[11] Phosphoinositide dependent kinase 1 (PDK1) is reported to be upregulated in ESCC, which is associated with a poor prognosis.[12] In addition, microRNAs (miRNAs) are small noncoding RNA molecules of 20 to 25 nucleotides in length that participate in posttranscriptional regulation of gene expression. It has previously been demonstrated that miRNAs play an important role in tumor initiation and progression.[13] In ESCC, miR-375 is frequently downregulated in cancer cells and functions as a tumor suppressor.[14] MiR-183 might play an oncogenic role in suppressing apoptosis and promoting proliferation in ESCC by regulating the PDCD4 expression.[15] It is known that miR-21 acts as an oncogenic miRNA in several types of cancer. The overexpression of miR-21 is related to advanced clinical stage, lymph node metastasis and a poor prognosis in ESCC.[16,17] However, little is known regarding an integrated analysis of the miRNA-mRNA regulatory network in ESCC. Therefore, further study is imperative.

In this study, we analyzed differentially expressed miRNAs (DEMs) and genes (DEGs) between ESCC tumor tissues and normal tissue samples using bioinformatics methods. We performed functional and pathway enrichment analysis of DEGs, and established the miRNA-mRNA and protein–protein interactions (PPI) network to reveal regulatory mechanisms in ESCC. Here, we aimed to explore the main pathways and processes that are associated with ESCC and provide cancer biomarkers for diagnosis and therapy.

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2 Materials and methods

2.1 Microarray data collection

The GSE55857, GSE43732, and GSE6188 miRNA expression profile data and three gene expression profiles (GSE70409, GSE29001, and GSE20347) were acquired from Gene Expression Omnibus (GEO: http://www.ncbi.nlm.nih.gov/geo/).[18] For these datasets, only normal ESCC and ESCC tissue samples were selected for further analysis. Every included dataset contained more than 10 normal ESCC tissues and ESCC samples. Basic information on the miRNA and mRNA expression microarrays of ESCC is shown in Table 1.

Table 1

Table 1

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2.2 Identification of DEMs and DEGs

The differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were obtained from 2 groups of samples (ESCC vs normal ESCC tissues) in each GEO database. The raw data of miRNA and gene expression profiles were preprocessed by a way of GEO2R analysis (www.ncbi.nlm.nih.gov/geo/geo2r/). The adjusted P value < .01 and |logFC| > 2 were set as the cut-off criteria of the DEM and DEG analyses to identify a statistically significant difference.

The miRWalk 2.0 database (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/index.html) is a comprehensive atlas that supplies a large collection of predicted and experimentally verified miRNA-target interactions.[19] We submitted the significant DEMs to miRwalk 2.0 and selected the data of their target mRNAs, which were based on experimental literature reports. Then, we extracted by intersecting the target mRNAs of the significant DEMs and the overlapping genes of the DEGs from the three GEO datasets. Finally, these genes were defined as significant DEGs (Fig. 1).

Figure 1

Figure 1

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2.3 Gene ontology and KEGG pathway analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david-d.ncifcrf.gov/) is an online bioinformatics resource that provides functional and pathway enrichment analysis.[20] Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs was performed by with the DAVID online tool. A false discovery rate (FDR) <0.05 and gene count >2 were used as the cut-off criteria.

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2.4 Construction of regulatory network

Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, https://string-db.org/) is an online platform that is used to predict functional protein association networks.[21] To construct protein–protein interactions (PPI) of DEGs, we submitted the DEGs to STRING, and only validated interactions that had a combined score >0.40, which was considered to be significant and the PPI was visualized.

Furthermore, the target genes of the significant DEMs were predicted by miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/)[22] and TargetScan (http://www.targetscan.org/).[23] When the miRNAs shared a common target mRNA with the hub genes of DEGs, they might exist in a similar regulatory pathway. Finally, the miRNA-mRNA regulatory network depicted interactions between miRNAs and their potential targets in ESCC were visualized by using Cytoscape 3.7.0.[24]

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2.5 Survival analysis

A Kaplan–Meier analysis was performed using OncoLnc (http://www.oncolnc.org/),[25] in which boxplots were employed to visualize the expression level of all prognosis-related genes between ESCC and normal tissues, that was based on data from the TCGA database (https://cancergenome.nih.gov/). Data from 144 ESCC patients were found in the TCGA database. Among them, 117 patients were male, and 27 were female. Among the 144 available clinical outcome events, 85 patients were living and 59 were deceased when the follow-up period ended. The P value < .05 was considered to be statistically significant.

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3 Results

3.1 Identification of DEMs and DEGs

A total of 186, 173, and 75 DEMs were identified from the GSE55857, GSE43732, and GSE6188 datasets, respectively. As shown in Figure 2A, 26 DEMs were screened out in all 3 datasets, so that they could be identified as significant DEMs, which were hsa-miR-18b-5p, hsa-miR-503-5p, hsa-miR-9-3p, hsa-miR-133a-3p, hsa-miR-34b-5p, hsa-miR-34c-5p, hsa-miR-339-3p, hsa-miR-375, hsa-miR-133b, hsa-miR-203a, hsa-miR-150-5p, hsa-miR-493-3p, hsa-miR-18a-3p, hsa-miR-130b-3p, hsa-miR-431-5p, hsa-miR-7-5p, hsa-miR-382-5p, hsa-miR-182-3p, hsa-miR-424-5p, hsa-miR-146b-5p, hsa-miR-196a-3p, hsa-miR-183-5p, hsa-miR-1-3p, hsa-miR-196b-3p, and hsa-miR-497-5p.

Figure 2

Figure 2

Compared with the normal samples, a total of 496 upregulated DEGs and 417 downregulated DEGs were identified in the ESCC samples (Fig. 2B and C). We obtained 18,501 target genes of the 26 significant DEMs from the miRWalk 2.0 database. The intersection number of these target genes and the upregulated DEGs that appeared in the three datasets was 96, while the intersection number of these candidate genes and the downregulated DEGs which appeared in three datasets was 184. Therefore, the 96 upregulated and 184 downregulated genes were identified as the final sets of significant DEGs.

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3.2 Functional annotation analysis

GO ontology contains three terms: molecular function (MF), cellular component (CC) and biological process (BP). In MF ontology, the most significant GO terms for upregulated significant DEGs were the extracellular matrix structural constituent, platelet-derived growth factor binding and metalloendopeptidase activity. Whereas the terms for the downregulated significant DEGs, the terms were iron ion binding and monooxygenase activity. In CC ontology, the extracellular region, the proteinaceous extracellular matrix, the extracellular matrix, the collagen trimer, the extracellular space and the endoplasmic reticulum lumen were significantly enriched GO terms for upregulated genes, while the extracellular exosome, the cornified envelope and the extracellular space were significantly enriched GO terms for downregulated genes. In BP ontology, the upregulated genes were mainly enriched in collagen catabolic process, extracellular matrix organization, skeletal system development, extracellular matrix disassembly and collagen fibril organization. The downregulated genes were mainly enriched in keratinocyte differentiation, keratinization, peptide cross-linking, epithelial cell differentiation and epidermis development.

Moreover, 4 KEGG pathways were overrepresented in the upregulated genes, including ECM-receptor interaction, amoebiasis, focal adhesion and protein digestion and absorption, and only 1 pathway (drug metabolism-cytochrome P450) was identified in the downregulated genes. The detailed results are presented in Table 2.

Table 2

Table 2

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3.3 PPI network

The PPI network of DEGs was established on the basis of STRING. The 103 nodes and 316 edges in total constituted the PPI network of significantly upregulated DEGs (Fig. 3). The network that significantly downregulated DEGs was composed of 188 nodes and 138 edges (Fig. 4).

Figure 3

Figure 3

Figure 4

Figure 4

The edge information plays an important role in the identification of hub genes in a PPI network. We used the parameter“degree” to calculate the edge counts of every single gene in a PPI network. The top 5% degree genes are shown in Table 3, which were assessed as hub genes. Thirteen genes in the PPI network were selected as hub genes in ESCC. These hub genes might play crucial roles in ESCC.

Table 3

Table 3

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3.4 miRNA–mRNA interaction network

To further understand the regulatory relationship between 26 significant DEMs and hub genes, the miRNA-mRNA regulation network was established and is shown in Figure 5. There were no hub genes that could be the target genes of hsa-miR-339-3p, hsa-miR-375 or hsa-miR-196b-3p. MiR-183-5p was with the highest connectivity target genes, targeting 7 hub genes (upregulated: MMP9, TOP2A, AURKA, and MCM2; downregulated: IVL, FOS, and CYP2C9). Five hub genes could be target genes of hsa-miR-9-3p and hsa-miR-130b-3p. Moreover, several genes were predicted to be common targets of different miRNAs. For instance, FOS was predicted as a common target of hsa-miR-133a-3p, hsa-miR-34b-5p, hsa-miR-34c-5p, hsa-miR-133b, hsa-miR-150-5p, hsa-miR-130b-3p, hsa-miR-431-5p, hsa-miR-382-5p, hsa-miR-424-5p, hsa-miR-196a-3p, hsa-miR-183-5p, and hsa-miR-497-5p.

Figure 5

Figure 5

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3.5 Stage and survival analysis of miRNA/mRNA in ESCC

We investigated the miRNAs expression level of 26 significant DEMs in the TCGA dataset, and found that 23 miRNAs were consistent with the results from GEO databases. However, the expression levels of hsa-miR-203a, hsa-miR-182-3p, and hsa-miR-1-3p were not significantly different between the ESCC samples and normal ESCC tissues. Using the TCGA ESCA dataset, we assessed the relationship of miRNAs with the clinical TNM stages. Detailed results of the significant DEMs are presented in Table 4.

Table 4

Table 4

Based on the TCGA survival data, survival analysis using the Kaplan–Meier method and the log-rank test was conducted. The results indicated that hsa-miR-9-3p, hsa-miR-34c-3p and FOS were related to overall survival (OS) in ESCC patients (Fig. 6). Higher expression of the transcript was associated with the poor OS in patients with ESCC.

Figure 6

Figure 6

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4 Discussion

ESCC is one of the most lethal cancers and is a public health issue of great concern worldwide.[26] Despite many advances in diagnosis and treatment, there is still no effective treatment for ESCC, and survival remains very poor.[27] Thus, the molecular mechanisms that are involved in the progress of ESCC have not been clarified. Therefore, it is crucial to study the mechanism and identify the molecular targets for diagnosis and treatment. In the present study, we identified 26 significant DEMs, 96 upregulated DEGs, and 184 downregulated DEGs. The results of the functional enrichment analysis indicated that significant DEGs were related to the ECM-receptor interaction and cytochrome P450 pathways in ESCC. Thirteen genes in the PPI network were selected as hub genes in ESCC. Importantly, hsa-miR-9-3p, hsa-miR-34c-3p and FOS were validated and were found to be correlated with tumor stages and survival, which meant they could not only regulate cellular process but could also be of valuable in clinical practice.

As was suggested by the functional enrichment analysis of significant DEGs, we found that the DEGs were mainly enriched in biological processes including, the ECM-receptor interaction and the cytochrome P450 metabolic pathways, and so on. ECM-receptor interaction was a chief contributor of cancer progression.[28] Cytochrome P450 levels showed significant differences between ESCC patients and healthy subjects and may contribute to the development of ESCC.[29] The genetic polymorphism RsaI/PstI in cytochrome P450 2E1 (CYP2E1) was a risk factor for EC. It demonstrated that CYP2E1 was a genetic determinant in the development of ESCC.[30] CYP2C9 expression was relevant for high Ki-67 labeling indices in EC. Moreover, inhibition of cytochrome P450 2C9 (CYP2C9) could affect the tumor cell proliferation in early EC development.[31]

A growing body of evidence suggests that miRNAs have significant roles in human tumorigenesis, tumor progression and metastasis.[32] MiRNAs are unique in their ability to regulate mRNA, which is more than 60% of protein-coding genes.[33] The aberrant expression of miRNAs in ESCC causes destruction to the miRNA-regulated mRNA networks and can function as tumor suppressors or oncogenes.[34] Therefore, the identification of the miRNA-mRNA regulatory network is important for further research concerning ESCC. Compared with normal samples, a microarray analysis identified 26 significant DEMs in our results. MiR-18a/b,[35,36] miR-503,[37] miR-9,[38] miR-133a/b,[39,40] miR-34b/c,[41,42] miR-375,[43] miR-203a,[44] miR-150,[45] miR-130b,[46] miR-7,[47] miR-382,[48] miR-424,[49] miR-146b,[50] miR-196a,[51] miR-183,[15,52], and miR-1[53] were reported in ESCC studies, and mostly involved in ESCC pathogenesis. MiR-18a overexpression was positively correlated with the stage that promoted the expression Cyclin D1 regulating PTEN-PI3K-AKT-mTOR signaling axis in ESCC cells. MiR-150 plays an oncogenic role in ESCC. MiR-150 promots ESCC cell migration and invasion by directly targeting ZEB1, SPOCK1 and Gli1.[45,54,55] MiR-133a, miR-133b and miR-375 are known diagnostic and prognostic markers that are associated with tumor suppressor miRNAs. MiR-133a, miR-133b and miR-375 are frequently downregulated in ESCC and are closely related to advanced clinical stage, tumor metastasis and poor prognosis. MiR-133a can suppress the migration and invasion of ESCC cells by targeting Sox4 and the EMT process. MiR-133b can regulate metastases of ESCC by affecting the MAPK/ERK and PI3K/AKT signaling pathways by targeting EGFR. MiR-375 is involved in the development and progression of ESCC by repressing metadherin expression.[39,40,43] In this study, higher expression levels of hsa-miR-9-3p and hsa-miR-34c-3p were found to be associated with poor OS in patients with ESCC. It was reported that miR-9 induces epithelial-mesenchymal transition (EMT) in ESCC, which is a key event in tumor metastasis. High plasma miR-9 concentrations are significantly correlated with poor tumor differentiation.[38,56] MiR-34b and miR-34c are located on intron 1 and exon 2 of the common primary transcript. MiR-34b and miR-34c expression levels were significantly higher in ESCC in the corresponding normal samples. Inhibiting the expression of miR-34b or miR-34c in ESCC cells may suppress cell growth in vitro. However, it may inactivate the p53-miR-34 pathway. Taken together, these significant DEMs provide potential biomarkers and molecular mechanisms for the diagnosis and therapy of ESCC.

Global alterations of miRNA and mRNA expression are involved in the regulatory mechanisms of the development and progression of ESCC.[57] Through PPI analyses, we found that the 13 mRNAs that were assessed as hub genes might play crucial roles in ESCC. Matrix metalloproteinases (MMPs) are strongly expressed in the cytoplasm of cancer cells, especially in the invasive margin, and are weakly expressed in stromal cells. MMP9 expression is positively associated with poor tumor cell differentiation.[58] MMP1 promotes tumor growth and metastasis when the PI3K/AKT pathway is inhibited by LY294002.[59] Proliferating Cell Nuclear Antigen (PCNA) expression can serve as an independent prognostic factor of EC.[60] The c-FOS which belongs to FOS protein family that is involved in the transcriptional regulation of ID1, responds to etoposide in ESCC cells.[61] Topoisomerase IIα (TOP2A) is associated with active cell proliferation of mammalian cells, and have reported expression levels and prognostic value in ESCC patients.[62] AURKA can directly interacts with β-catenin enhances tumor cell invasion and metastatic in vitro and in vivo, and upregulates MMP-2 expression through activating AKT/NF-κB pathway in ESCC cells.[63,64] Minichromosome maintenance protein 2 (MCM2) expression is useful marker than Ki-67 in predictingtumor aggressiveness and prognostic value in ESCC.[65] Involucrin (IVL) IVL and filaggrin (FLG) are major components of the cornified envelope and are considered to be appropriate markers for terminal differentiation.[66,67] Small proline-rich repeat protein 3 (SPRR3) was downregulated and involved in radiosensitivity in ESCC patients.[68] Transglutaminase-1 (TGM1) can suppress the Wnt signaling pathway and involve in development of gastric cancer.[69] By constructing a miRNA-mRNA network, miR-183 was shown to have the highest connectivity to target genes and to target, MMP9, TOP2A, AURKA, MCM2, IVL, FOS, and CYP2C9. MiR-183 might play an oncogenic role by suppressing apoptosis and promoting proliferation in ESCC by regulating PDCD4 expression.[15,52] Five hub genes could be the target genes of hsa-miR-9-3p and hsa-miR-130b-3p. Moreover, the FOS gene was predicted to be a common target of different miRNAs. FOS and miR-34c had higher expression levels, which were found to be associated with poor OS in patients with ESCC. Interestingly, FOS was a target gene of hsa-miR-34c-3p determined by both miRTarBase and TargetScan. It revealed that miR-34c might be involved in the regulation of FOS. Further experimental studies are required to test our results. We identified that the hub genes that miRNAs may interact with might play important roles in ESCC. In addition, the identification of the miRNA-mRNA regulation network provided new insights into ESCC oncogenesis.

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5 Conclusion

In this study, 26 DEMs and 280 DEGs that consisted of 96 upregulated and 184 downregulated genes were identified. Hsa-miR-9-3p, hsa-miR-34c-3p and FOS were found to be related to OS in ESCC patients. The higher expression levels of the transcript were associated with poor OS in patients with ESCC. The present study provides potential biomarkers and molecular mechanisms for the diagnosis and therapy of ESCC and provides valuable clues for further research.

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Author contributions

Data curation: Guangcheng Huang.

Formal analysis: Guangcheng Huang, Wenjie Pu.

Funding acquisition: Xiaowu Zhong.

Methodology: Hebin Liao, Chang Liu.

Project administration: Xiaowu Zhong, Xiaolan Guo.

Resources: Lei Xu.

Software: Qiang Ma, Chang Liu, Yan Cai.

Supervision: Yan Cai, Xiaolan Guo.

Writing – original draft: Xiaowu Zhong.

Writing – review & editing: Xiaolan Guo.

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References

[1]. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424.
[2]. Shibata A, Matsuda T, Ajiki W, et al. Trend in incidence of adenocarcinoma of the esophagus in japan, 1993–2001. Jpn J Clin Oncol 2008;38:464–8.
[3]. Lin Y, Totsuka Y, Shan B, et al. Esophageal cancer in high-risk areas of China: Research progress and challenges. Ann Epidemiol 2017;27:215–21.
[4]. Domper Arnal MJ, Ferrández Arenas Á, Lanas Arbeloa Á. Esophageal cancer: risk factors, screening and endoscopic treatment in Western and Eastern countries. World J Gastroenterol 2015;21:7933–43.
[5]. Andreou A, Pesthy S, Struecker B, et al. Incidence and risk factors of symptomatic hiatal hernia following resection for gastric and esophageal cancer. Anticancer Res 2017;37:7031–6.
[6]. Lv J, Cao XF, Zhu B, et al. Effect of neoadjuvant chemoradiotherapy on prognosis and surgery for esophageal carcinoma. World J Gastroenterol 2009;15:4962–8.
[7]. Ho HJ, Chen HS, Hung WH, et al. Survival impact of total resected lymph nodes in esophageal cancer patients with and without neoadjuvant chemoradiation. Ann Surg Oncol 2018;25:3820–32.
[8]. Portale G, Hagen JA, Peters JH, et al. Modern 5-year survival of resectable esophageal adenocarcinoma: single institution experience with 263 patients. J Am Coll Surg 2006;202:588–96.
[9]. Chen JW, Xie JD, Ling YH, et al. The prognostic effect of perineural invasion in esophageal squamous cell carcinoma. BMC Cancer 2014;14:313.
[10]. Saddoughi SA, Reinersman JM, Zhukov YO, et al. Survival after surgical resection of stage iv esophageal cancer. Ann Thorac Surg 2017;103:261–6.
[11]. Wu Y, Hu L, Liang Y, et al. Up-regulation of lncRNA CASC9 promotes esophageal squamous cell carcinoma growth by negatively regulating PDCD4 expression through EZH2. Mol Cancer 2017;16:150.
[12]. Yang Z, Wu Z, Liu T, et al. Upregulation of PDK1 associates with poor prognosis in esophageal squamous cell carcinoma with facilitating tumorigenicity in vitro. Med Oncol 2014;31:337.
[13]. Tie J, Fan D. Big roles of microRNAs in tumorigenesis and tumor development. Histol Histopathol 2011;26:1353–61.
[14]. Kinoshita T, Hanazawa T, Nohata N, et al. The functional significance of microRNA-375 in human squamous cell carcinoma: aberrant expression and effects on cancer pathways. J Hum Genet 2012;57:556–63.
[15]. Yang M, Liu R, Li X, et al. miRNA-183 suppresses apoptosis and promotes proliferation in esophageal cancer by targeting PDCD4. Mol Cells 2014;37:873–80.
[16]. Akagi I, Miyashita M, Ishibashi O, et al. Relationship between altered expression levels of MIR21, MIR143, MIR145, and MIR205 and clinicopathologic features of esophageal squamous cell carcinoma. Dis Esophagus 2011;24:523–30.
[17]. Hezova R, Kovarikova A, Srovnal J, et al. Diagnostic and prognostic potential of miR-21, miR-29c, miR-148 and miR-203 in adenocarcinoma and squamous cell carcinoma of esophagus. Diagn Pathol 2015;10:42.
[18]. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 2013;41:D991–5.
[19]. Dweep H, Sticht C, Pandey P, et al. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 2011;44:839–47.
[20]. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc 2009;4:44–57.
[21]. Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017;45(D1):D362–8.
[22]. Chou CH, Shrestha S, Yang CD, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018;46(D1):D296–302.
[23]. Agarwal V, Bell GW, Nam JW, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015;4:e05005.
[24]. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504.
[25]. Anaya J. OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs. PeerJ Preprints 2016;4:e2574v1.
[26]. Talukdar FR, di Pietro M, Secrier M, et al. Molecular landscape of esophageal cancer: implications for early detection and personalized therapy. Ann N Y Acad Sci 2018;1434:342–59.
[27]. Zhang Y. Epidemiology of esophageal cancer. World J Gastroenterol 2013;19:5598–606.
[28]. Liu D. LYN, a key gene from bioinformatics analysis, contributes to development and progression of esophageal adenocarcinoma. Med Sci Monit Basic Res 2015;21:253–61.
[29]. Bergheim I, Wolfgarten E, Bollschweiler E, et al. ytochrome P450 levels are altered in patients with esophageal squamous-cell carcinoma. World J Gastroenterol 2007;13:997–1002.
[30]. Leng WD, Zeng XT, Chen YJ, et al. Cytochrome P450 2E1 RsaI/PstI polymorphism and risk of esophageal cancer: a meta-analysis of 17 case-control studies. Exp Ther Med 2012;4:938–48.
[31]. Schmelzle M, Dizdar L, Matthaei H, et al. Esophageal cancer proliferation is mediated by cytochrome P450 2C9 (CYP2C9). Prostaglandins Other Lipid Mediat 2011;94:25–33.
[32]. Farazi TA, Spitzer JI, Morozov P, et al. miRNAs in human cancer. J Pathol 2011;223:102–15.
[33]. Friedman RC, Farh KK, Burge CB, et al. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 2009;19:92–105.
[34]. Mei LL, Qiu YT, Zhang B, et al. MicroRNAs in esophageal squamous cell carcinoma: potential biomarkers and therapeutic targets. Cancer Biomark 2017;19:1–9.
[35]. Zhang W, Lei C, Fan J, et al. miR-18a promotes cell proliferation of esophageal squamous cell carcinoma cells by increasing cylin D1 via regulating PTEN-PI3K-AKT-mTOR signaling axis. Biochem Biophys Res Commun 2016;477:144–9.
[36]. Su H, Jin X, Zhang X, et al. Identification of microRNAs involved in the radioresistance of esophageal cancer cells. Cell Biol Int 2014;38:318–25.
[37]. Jiang L, Zhao Z, Zheng L, et al. Downregulation of miR-503 Promotes ESCC Cell Proliferation, Migration, and Invasion by Targeting Cyclin D1. Genom Proteom Bioinform 2017;15:208–17.
[38]. Song Y, Li J, Zhu Y, et al. MicroRNA-9 promotes tumor metastasis via repressing E-cadherin in esophageal squamous cell carcinoma. Oncotarget 2014;5:11669–80.
[39]. Li S, Qin X, Li Y, et al. MiR-133a suppresses the migration and invasion of esophageal cancer cells by targeting the EMT regulator SOX4. Am J Transl Res 2015;7:1390–403.
[40]. Zeng W, Zhu JF, Liu JY, et al. miR-133b inhibits cell proliferation, migration and invasion of esophageal squamous cell carcinoma by targeting EGFR. Biomed Pharmacother 2018;111:476–84.
[41]. Harata K, Ishiguro H, Kuwabara Y, et al. MicroRNA-34b has an oncogenic role in esophageal squamous cell carcinoma. Oncol Lett 2010;1:685–9.
[42]. Sreedharan L, Mayne GC, Watson DI, et al. MicroRNA profile in neosquamous esophageal mucosa following ablation of Barrett's esophagus. World J Gastroenterol 2017;23:5508–18.
[43]. Hu C, Lv L, Peng J, et al. MicroRNA-375 suppresses esophageal cancer cell growth and invasion by repressing metadherin expression. Oncol Lett 2017;13:4769–75.
[44]. Liu Y, Dong Z, Liang J, et al. Methylation-mediated repression of potential tumor suppressor miR-203a and miR-203b contributes to esophageal squamous cell carcinoma development. Tumour Biol 2016;37:5621–32.
[45]. Yokobori T, Suzuki S, Tanaka N, et al. MiR-150 is associated with poor prognosis in esophageal squamous cell carcinoma via targeting the EMT inducer ZEB1. Cancer Sci 2013;104:48–54.
[46]. Yu T, Cao R, Li S, et al. MiR-130b plays an oncogenic role by repressing PTEN expression in esophageal squamous cell carcinoma cells. BMC Cancer 2015;15:29.
[47]. Dong W, Li B, Wang J, et al. Diagnostic and predictive significance of serum microRNA-7 in esophageal squamous cell carcinoma. Oncol Rep 2016;35:1449–56.
[48]. Qi B, Lu JG, Yao WJ, et al. Downregulation of microRNA-382 is associated with poor outcome of esophageal squamous cell carcinoma. World J Gastroenterol 2015;21:6884–91.
[49]. Wang F, Wang J, Yang X, et al. MiR-424-5p participates in esophageal squamous cell carcinoma invasion and metastasis via SMAD7 pathway mediated EMT. Diagn Pathol 2016;11:88.
[50]. Li J, Shan F, Xiong G, et al. Transcriptional regulation of miR-146b by C/EBP ( LAP2 in esophageal cancer cells. Biochem Biophys Res Commun 2014;446:267–71.
[51]. Ma Y, Wang B, Guo Y, et al. Inhibition of miR-196a affects esophageal cancer cell growth in vitro. Biomed Pharmacother 2016;84:22–7.
[52]. Ren LH, Chen WX, Li S, et al. MicroRNA-183 promotes proliferation and invasion in oesophageal squamous cell carcinoma by targeting programmed cell death 4. Br J Cancer 2014;111:2003–13.
[53]. Liu W, Li M, Chen X, et al. MicroRNA-1 suppresses proliferation, migration and invasion by targeting Notch2 in esophageal squamous cell carcinoma. Sci Rep 2018;8:5183.
[54]. Osako Y, Seki N, Koshizuka K, et al. Regulation of SPOCK1 by dual strands of pre-miR-150 inhibit cancer cell migration and invasion in esophageal squamous cell carcinoma. J Hum Genet 2017;62:935–44.
[55]. Sun J, Wang D, Li X, et al. Targeting of miR-150 on Gli1 gene to inhibit proliferation and cell cycle of esophageal carcinoma EC9706. Cancer Biomark 2017;21:203–10.
[56]. Cui Y, Xue Y, Dong S, et al. Plasma microRNA-9 as a diagnostic and prognostic biomarker in patients with esophageal squamous cell carcinoma. J Int Med Res 2017;45:1310–7.
[57]. Liu M, Wang C, Gao L, et al. A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer. J Cancer 2018;9:3986–90.
[58]. Gu ZD, Li JY, Li M, et al. Matrix metalloproteinases expression correlates with survival in patients with esophageal squamous cell carcinoma. Am J Gastroenterol 2005;100:1835–43.
[59]. Liu M, Hu Y, Zhang MF, et al. MMP1 promotes tumor growth and metastasis in esophageal squamous cell carcinoma. Cancer Lett 2016;377:97–104.
[60]. Zhu SC, Li R, Wang YX, et al. Impact of simultaneous assay, the PCNA, cyclinD1, and DNA content with specimens before and after preoperative radiotherapy on prognosis of esophageal cancer-possible incorporation into clinical TNM staging system. World J Gastroenterol 2005;11:3823–9.
[61]. Zhao Y, Luo A, Li S, et al. Inhibitor of Differentiation/DNA Binding 1 (ID1) Inhibits Etoposide-induced Apoptosis in a c-Jun/c-Fos-dependent Manner. J Biol Chem 2016;291:6831–42.
[62]. Hanagiri T, Ono K, Kuwata T, et al. Evaluation of topoisomerase I/topoisomerase II alpha status in esophageal cancer. J UOEH 2011;3:205–16.
[63]. Jin S, Wang X, Tong T, et al. Aurora-A enhances malignant development of esophageal squamous cell carcinoma (ESCC) by phosphorylating (-catenin. Mol Oncol 2015;9:249–59.
[64]. Wang X, Li X, Li C, et al. Aurora-A modulates MMP-2 expression via AKT/NF-(B pathway in esophageal squamous cell carcinoma cells. Acta Biochim Biophys Sin 2016;48:520–7.
[65]. Kato H, Miyazaki T, Fukai Y, et al. A new proliferation marker, minichromosome maintenance protein 2, is associated with tumor aggressiveness in esophageal squamous cell carcinoma. J Surg Oncol 2003;84:24–30.
[66]. Rice RH, Green H. Presence in human epidermal cells of a soluble protein precursor of the cross-linked envelope: activation of the cross-linking by calcium ions. Cell 1979;18:681–94.
[67]. Sandilands A, Sutherland C, Irvine AD, et al. Filaggrin in the frontline: role in skin barrier function and disease. J Cell Sci 2009;122:1285–94.
[68]. Luo A, Chen H, Ding F, et al. Small proline-rich repeat protein 3 enhances the sensitivity of esophageal cancer cells in response to DNA damage-induced apoptosis. Mol Oncol 2013;7:955–67.
[69]. Huang H, Chen Z, Ni X. Tissue transglutaminase-1 promotes stemness and chemoresistance in gastric cancer cells by regulating Wnt/(-catenin signaling. Exp Biol Med (Maywood) 2017;242:194–202.
Keywords:

bioinformatics; esophageal squamous cell carcinoma; miRNA; regulatory network

Copyright © 2019 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.