NRP1 is targeted by miR-130a and miR-130b, and is associated with multidrug resistance in epithelial ovarian cancer based on integrated gene network analysis
- Authors:
- Published online on: November 11, 2015 https://doi.org/10.3892/mmr.2015.4556
- Pages: 188-196
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Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Ovarian cancer is one of the leading contributors to mortality rates in women worldwide, with a five-year survival rate of 30–45% (1–3). There were ~21,880 new cases diagnosed and 13,850 cases of associated mortality reported in the United States in 2010 (4). Epithelial ovarian cancer (EOC) is the most common histological type, comprising 80–90% of all ovarian cancer cases (5). Chemotherapy is an important treatment for EOC, and patients with advanced EOC have high initial response rates to chemotherapy (≥80%); however, 70–80% of patients eventually relapse, with a progression-free survival rate of 18 months (6,7). Although chemotherapy treatment regimens have improved in previous decades, the efficacy of chemotherapy for EOC has improved simultaneously. Multidrug resistance (MDR) is the predominant reason for chemotherapy failure and poor prognosis in patients with EOC (8).
The role of microRNAs (miRNAs; miRs), and their target genes associated with MDR, in EOC have been investigated in previous years (9). As a cluster of small non-coding molecular RNAs, miRNAs are important in cell differentiation, proliferation, apoptosis, organism growth and the development of human disease (10–12). Mature miRNA primarily targets the 3′-untranslated region (3′-UTR) of an mRNA strand with a complementary sequence, and then induces mRNA degradation or inhibits mRNA translation at the post-transcriptional level (13–15). The dysfunction of miRNAs is associated with the development and metastasis of human cancer (16,17).
A single miRNA is able to target multiple mRNAs and, similarly, a single mRNA can be targeted by multiple miRNAs, forming multiple complex gene expression regulatory networks (18). However, the mechanisms underlying MDR in EOC remain to be fully elucidated. As integrated network analysis has been applied in investigations of EOC as a promising technique (19–23), it may provide important information in investigating the molecular mechanisms underlying MDR in EOC. In the present study four published microarray datasets (GSE41499, GSE33482, GSE15372 and GSE28739) and 11 miRNAs (miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b* and miR-200c) were downloaded from public databases in order to perform a comprehensive bioinformatics analysis through gene expression analysis, signaling pathway analysis, literature co-occurrence and miRNA-mRNA interaction networks. The aim of the present study was to identify any potential genes, and obtain their bioinformatics information, highly associated with MDR in EOC.
Materials and methods
Microarray datasets/miRNAs/miRNA target genes
The four microarray datasets, GSE41499, GSE33482, GSE15372 and GSE28739, were downloaded from the Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo/) (28). All four microarray datasets satisfied the following four criteria: i) Data contained information regarding EOC genome-wide RNA expression; ii) data provided a comparison of EOC samples between chemotherapy resistance and chemotherapy sensitivity; iii) a minimum of three samples were included in each group; iv) each sample provided detailed information on chemotherapy resistance or sensitivity in EOC (Table I).
A total of 11 miRNAs (miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b* and miR-200c) were investigated in PubMed (http://www.ncbi.nlm.nih.gov/pubmed/; Table II), which were those reported to be highly associated with MDR in EOC (29–39). The above-mentioned 11 miRNAs were entered into TargetScan (http://genes.mit.edu/tscan/targetscan2003.html) and PicTar (http://pictar.mdc-berlin.de/) (40,41), respectively, to determine the relative miRNA target genes.
Gene expression analysis
The Benjamini-Hochberg (BH) (42) method was used to analyze gene expression, which was performed in GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) (43), a web tool allowing users to perform R statistical analysis without command line expertise. An adjusted P-value of P<0.05 was used as the screening criterion for statistically significantly expressed genes. The fold change (FC) method (44) was also used to estimate gene expression. When logFC<0, the expression of the genes was downregulated, whereas the expression of the genes was upregulated when logFC>0.
Pathway enrichment analysis
Genetic pathway enrichment analysis was performed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (http://david.abcc.ncifcrf.gov/) (45,46). Another web server, DIANA-miRPath (http://www.microrna.gr/miRPathv2) (47) was also used, as it is specifically designed for miRNA-targeted pathway analysis based on interaction levels. Fisher's exact probability method was used to determine the significant difference of pathway enrichment analysis, with P<0.05 indicating significance.
Co-occurrence analysis
Text mining methods from the literature and disease levels were combined to screen for MDR-associated genes in EOC, which were performed in the COREMINE (http://www.coremine.com/medical/#search) and IPAD (http://bioinfo.hsc.unt.edu/ipad/) (48) databases, respectively. The corresponding genes and the exact keywords 'drug resistance', 'drug resistance, multiple' and 'drug resistance, neoplasm' were input in COREMINE for co-occurrence analysis, and the disease information between differentially expressed genes and EOC were mined in the IPAD database. CytoScape2.6.1 software (49) was used to construct a graph of the association between genes and MDR.
Integrated gene network analysis
Integrated gene network analysis, based on miRNAs and their target genes, was performed using Pajek software (50). The topological characteristics of the integrated gene network comprised degree centralization (DC), betweenness centralization (BC), closeness centralization (CC) and clustering coefficient (CC′), which were calculated using the Pajek software. Degree of node indicates the number of adjacent nodes or connected edges each node has. The higher the number of neighbors (nodes and edges) a node has, the more importance it has in the network. Therefore, the node is also called a hub node (51). Correspondingly, the gene in the position of the hub node was termed the hub gene. The binding sites of the miRNA-target interactions were finally analyzed in StarBase (http://star-base.sysu.edu.cn/) (52), which was designed for deciphering miRNA-target interactions, including miRNA-mRNA interaction networks from large-scale CLIP-Seq data.
Results
Gene expression and miRNA target genes
Using the BH method in GEO2R, a total of 5,003 significantly expressed genes were obtained from GSE41499, 3,372 from GSE33482, 2,029 from GSE15372 and 267 from GSE28739. Among these, 2,505 genes were upregulated and 2,498 were downregulated in GSE41499, 1,487 genes were upregulated and 1,885 genes were downregulated in GSE33482, 798 genes were upregulated and 1,231 genes were downregulated in GSE15372, and 180 genes were upregulated and 87 genes were downregulated in GSE28739, respectively.
The present study also obtained 47,077 target genes using TargetScan and 1,675 target genes using PicTar, based on the previously mentioned 11 miRNAs (miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b* and miR-200c).
Pathway enrichment analysis
Genetic pathway enrichment analysis were performed in the KEGG database using the DAVID tool, based on upregulated genes and downregulated genes from the four microarray datasets (GSE41499, GSE33482, GSE15372 and GSE28739). A total of 11 upregulated signaling pathways were enriched in the KEGG database, including the mitogen-activated protein kinase (MAPK) signaling pathway, ubiquitin-mediated proteolysis, axon guidance, focal adhesion, neurotrophin signaling pathway, pathways in cancer, renal cell carcinoma, citrate cycle, terpenoid backbone biosynthesis, mismatch repair and Huntington's disease. In addition, seven downregulated signaling pathways were identified, including glycerolipid metabolism, pentose phosphate pathway, fructose and mannose metabolism, glutathione metabolism, proteasome, p53 signaling pathway and lysosome. The corresponding upregulated and downregulated genes are shown in Tables III and IV, respectively.
Co-occurrence analysis
The involvement of 11 significantly expressed genes in cancer drug resistance determined in the COREMINE database, on the basis of literature co-occurrence (Fig. 1), whereas eight genes associated with MDR in ovarian cancer were identified in the IPAD database. Among these, five genes, including aconitase 1 (ACO1), brain-derived neurotrophic factor (BDNF), chemokine (C-X-C motif) receptor 4 (CXCR4), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and neuropilin 1 (NRP1) were upregulated, and three genes [FAS, cyclin-dependent kinase inhibitor (CDKN)2C) and S-phase kinase-associated protein 2 (SKP2)] were downregulated. In addition, three important signaling pathways were identified in the IPAD database, including the MAPK signaling pathway, the P53 signaling pathway and axon guidance (Table V).
Integrated gene network analysis
The above-mentioned eight genes (ACO1, BDNF, CXCR4, HMGCR, NRP1, CDKN2C, FAS and SKP2) interacted with the 47,077 target genes from TargetScan and 1,675 target genes from PicTar. The corresponding text file was subsequently converted into a format file (.net), to enable it to be recognized by the Pajek software. Following these steps: File>Network>Read and Draw>Net work>Layout>Circular>using Permutation commands, the integrated gene network based on miRNAs and their target genes (data from TargetScan) was constructed using Pajek software. In the integrated gene network, the upregulated gene, NRP1, was found to represent the most important hub gene (Fig. 2). Only NRP1, targeted by miR-130a and miR-130b, was identified in PicTar.
The topological characteristics of the integrated gene network, including DC, BC, CC and CC′ were 1.50, 0, 0.44 and 0, respectively. The BC and CC′ values of each node were 0, which indicated that no clustering phenomenon existed in the whole or local network. The calculated results of DC and CC are shown in Table VI. The topological analysis also demonstrated that the hub gene, NRP1, exhibited a higher DC and CC, reflecting the orthocenter of the integrated gene network.
The binding sites between miR-130a and NRP1, and miR-130b and NRP1 were identified in StarBase. The two binding sites were found to be located on chromosome 10: 33466864-33466870, determined by CLIP-Seq datasets in the deepView genome browser (Fig. 3). Axon guidance (Fig. 4) was identified based on miRNA signaling pathway analysis in DIANA-miRPath, which involved the hub NRP1 gene.
Discussion
MDR is the predominant form of tumor resistance to chemotherapy, and was first reported by Biedler and Riehm in 1970 (53). MDR in EOC may involve complex interactions among genes and other molecules, including miRNAs, proteins or transcription factors, in numerous biological processes. In organisms, these molecular interactions occur between genes, between miRNAs and genes, and between miRNAs, forming complex networks to regulate gene expression. Following the construction of networks for miRNA target genes and transcription factor target genes in ovarian cancer, a previous study identified miR-16, cyclin E1, CDKN1A and E2F transcription factor 1 as hub genes, all of which may be potential biomarkers for ovarian cancer (54). However, there has been little focus on MDR in EOC on the basis of integrated gene network analysis of miRNAs and their target genes. In the present study, a comprehensive bioinformatics analysis was performed through gene expression, pathway enrichment, co-occurrence, and interaction networks, based on published microarray datasets and miRNAs from public databases. This was performed in order to predict the potential molecular mechanism underlying MDR in EOC. The results of the present study suggested that NRP1 was the hub gene of the network, targeted by miR-130a and miR-130b. Therefore, axon guidance involving NRP1 may be a novel signaling pathway for MDR in EOC.
NRP family members have been an area of intense investigation in the field of cancer research over the last 10 years (55,56). NRP receptors are expressed in several types of tumor and endothelial cell, and interact with various soluble molecules, including vascular endothelial growth factor (VEGF), integrin, c-Met and transforming growth factor receptors to modulate the progression of cancer (57). NRP1 encodes one of two NRPs, which contain specific protein domains, allowing NRP1 to be involved in various signaling pathways, which control cell migration (58). NRPs bind several ligands and various types of co-receptors, including VEGF. VEGF has the ability to promote cancer stemness and renewal by directly affecting cancer stem cells via NRP1 in an autocrine loop, and deletion of NRP1 in normal cells prevents tumor initiation (59). It has been reported that immunoreactivity to NRP1 is observed in the vessels of normal tissue samples adjacent to cancer tissue samples, as well as in 98–100% of carcinoma, and the inhibition of NRP1 signaling results in defective angiogenesis and recapitulated the effects of anti-VEGF treatment (60,61). It was also confirmed that knockdown of NRP1 in regulatory T cells may delay or eliminate oncogeny in mouse models of several types of human cancer (62). Disorders of the expression of NRP1 is widely observed in human cancer, including breast cancer, colorectal cancer and chronic lymphocytic leukemia (63–65). NRP1 in plasmacytoid dendritic cells and T regulatory cells is also reported to be a promising therapeutic target for the treatment of cancer (66).
Previously, the expression of NRP1 was found to be upregulated in EOC, and high expression levels of NRP1 enhanced proliferation and were associated with ovarian malignancy, making NRP1 a potential drug targeting candidate for the treatment of EOC (67). These results were concordant with those of the present study. However, the association between high expression levels of NRP1 and MDR in EOC remains to be elucidated and requires further experimental verification in the future.
The results of the present study also suggested that axon guidance was involved in the alteration of the expression of NRP1. The axon guidance signaling pathway represents a pivotal stage in the development of the neuronal network. Axons are guided along specific signaling pathways by various attractive and repulsive guidance molecules, including netrins, slits, ephrins and semaphorins, a number of which have been implicated in human cancers (68–70). However, the role of axon guidance in MDR in EOC remains to be fully elucidated. The present study hypothesized that axon guidance is important in the formation of MDR in EOC, as it acted as one of multiple developmental events in the MAPK signaling pathway (Fig. 4) (71). The latter mediates cisplatin-induced apoptosis and triggers DNA damage and drug resistance in EOC (72,73). Further investigations are required in order to investigate these hypotheses.
Bioinformatics analysis based on genes and miRNAs has been used for investigations on chemotherapeutic response and prognosis in EOC (74–76). MDR in EOC is often accompanied by alterations in gene expression levels and dysfunction of miRNAs. Alterations in gene expression and dysfunction of miRNAs often affects signaling pathways involved in cell proliferation, adhesion, migration, invasion, apoptosis, drug resistance and survival (77–79). Gene expression analysis is one of the basic methods of bioinformatics analysis, and is regularly used to identify dysregulated genes serving as molecular markers for EOC. As high-throughput gene expression data are available in the GEO, four microarray datasets (GSE41499, GSE33482, GSE15372 and GSE28739) were downloaded for gene expression analyses in the present study, which were performed using an R-based web application called GEO2R with the BH method. In addition, 11 miRNAs were mined from previous literature, all of which were revealed to be associated with MDR in EOC. The miRNA target genes in the present study were mined from TargetScan and PicTar. TargetScan and PicTar represent the first and second generation of miRNA target prediction algorithms, respectively, and have high positive predictive values (40,41). The similarity between the two target prediction algorithms is that the seed sequence of miRNA is complementary with the 3′-UTR of mRNA, characterized by a thermodynamic dimer of miRNA target gene (40,41). Furthermore, PicTar was developed based on the design of the first generation of prediction algorithms, breaking the limitation of cross-species conservation. The above characteristic form the basic of integrated network analysis.
COREMINE is a web tool used for literature mining, which performs automated analysis of titles and abstracts by extracting experimental and theoretical biomedical data to create a gene to gene co-citation network (44). IPAD is a web-based database and tool used for mining gene function based on the enrichment analysis of multiple genomic or proteomic data/sources. The present study used a unique approach with that combined literature co-occurrence and disease enrichment. Pathway enrichment combined with network analysis was a further novel approach used in the present study. This was performed to identify the potential genes involved in drug resistance from putative pathways and manually drawn networks. All theoretical conclusions of from the present study require experimental validation and clinical cohorts in the future.
In conclusion, based on a comprehensive bioinformatics analysis using gene expression analysis, signaling pathway analysis, literature co-occurrence and miRNA-mRNA interaction networks, the present study demonstrated that NRP1 is targeted by miR-130a and miR-130b, and may contribute to MDR in EOC. The binding sites of the miRNAs were found to be located on chromosome 10: 33466864-33466870, and all located in axon guidance, a potential pathway associated with MDR in EOC. To the best of our knowledge, the present study is the first to report a gene function of NRP1 associated with MDR in EOC. The findings provide important information for further experimental investigations on the MDR-associated functions of NRP1 in EOC.
Acknowledgments
The present study was supported by a grant from the Natural Science Foundation of Guangxi, China (grant no. 2011GXNSFA018190).
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