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MiRNAs and Genes in CIN

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Identification of miRNAs and genes in cervical intraepithelial neoplasia using bioinformatic analysis

Running title: miRNAs and genes in CIN


  1. Total 21 feature miRNAs and 361 feature mRNAs were identified.
  2. PBX1 and LAMC2 play important roles in the CIN.
  3. MiR-338-5p, miR-193a-5p and miR-216b were hub nodes in miRNA–mRNA regulatory network.


Aim: The objective of this study was to predict potential target genes and key miRNAs to the pathogenesis of cervical intraepithelial neoplasia (CIN) by bioinformatics analyses.

Methods: By using the GSE51993 microarray data from Gene Expression Omnibus (GEO) database, the feature miRNAs and genes were selected from CIN III and normal samples, followed by the miRNA-mRNA regulatory network. Transcription factors (TF) and cancer genes were analysed for feature genes.

Results: Total 21 miRNAs and 361 mRNAs were gained. The miRNA-mRNA regulatory network results showed that miR-338-5p, miR-193a-5p and miR-216b were hub nodes. PBX1 and LAMC2 were cancer-promoting genes and PBX1 also was a TF.

Conclusions: PBX1 and LAMC2 may be candidate genes to target the pathogenesis of CIN. MiR-338 and miR-216 may be the key miRNAs in CIN development.

Key words: Cervical intraepithelial neoplasia, bioinformatics, miRNA-mRNA regulatory network


Cervical cancer, the malignant neoplasm of the cervix uteri, is the second most common cancer among women worldwide [1]. Cervical intraepithelial neoplasia (CIN) is considered a precursor of cervical cancer. Development of cervical cancer goes through different premalignant stages, from low-grade CIN I through high-grade CIN (CIN II/ III) to cervical cancer [2]. It has been suggested that these lesions have the capacity to progress from hyperplasia to cervix, to preinvasive carcinoma, and ultimately to invasive carcinoma [3]. There is general agreement that either ablation or excision of CIN- II, III reduces the incidence and mortality caused by invasive cervical cancer in women with these lesions [4]. However, it is difficult to detect cervical cancer at an early stage.

CIN is a multi factorial process influenced by various factors, such as human papillomavirus (HPV) infection [5], smoking [6] and human immunodeficiency virus [7]. Previous studies indicate that microRNA-218 (miR-218) levels in patients with high-risk CIN were lower than in those with low-risk CIN. So down-regulation of miR-218 may be involved in the pathogenesis of cervical cancer [8]. Tumor suppressor genes such as p16 and retinoblastoma proteins play a role in the neoplastic changes of CIN [9]. Vascular endothelial growth factor (VEGF) expression is associated with progression of CIN [10]. Using microarray gene expression data and bioinformatic analyses, Prashant et al., suggested that transcription factor family E2F plays an important role in cervical carcinogenesis [11]. However, the target genes to the pathogenesis of CIN or cervical cancer were still not clearly demonstrated.

In this study, we aimed to predict potential target genes and key miRNAs to thepathogenesis of CIN. Normal and CIN III samples were used to identify the feature genes and miRNAs involved in CIN. We constructed miRNA–mRNA regulatory networks. Furthermore, we used the DAVID (the Database for Annotation, Visualization and Integrated Discovery) to identify over-represented GO (Gene Ontology) categories in biological processes and significant pathways in this progression.

Material and methods

Microarray data

The data of GSE51993 were downloaded from NCBI GEO (Gene Expression Omnibus) (http://www.ncbi.nlm.nih.gov/geo/) database which were based on the platform of GPL8179 Illumina Human v2 microRNA expression beadchip and GPL10558 Illumina HumanHT-12 V4.0 expression beadchip. The data was reported the genome-wide expression profiles of both miRNAs and mRNAs from 24 cervical samples with consecutive stages of normal (7 samples), CIN I (mild dysplasia, 9 samples) and CIN III (severe dysplasia and carcinoma in situ, 8 samples) [12].

Total 30 samples were available for further analysis, including 8 CIN III samples (GSM1256881 - GSM1256888) and 7 normal cervix samples (GSM1256898 - GSM1256904) from platform of GPL8179, 8 CIN III samples (GSM1256905 - GSM1256912) and 7 normal cervix samples (GSM1256922 - GSM1256928) from platform of GPL10558.

The data were normalized with robust multi-array average (RMA) algorithm [13] and subjected to logarithmic transformation.

Feature miRNAs and genes selection

Probe set identifiers were mapped to miRNA and gene symbols based on the SOFT formatted family file provided by corresponding databases, respectively. Nonspecific probes were filtered. When multiple probe sets were mapped to the same miRNA or gene, the average expression value of all probes was used to represent the gene expression level.

These samples in platform were divided into two groups: normal group (7 samples) and CIN group (8 samples). The dimensions of this dataset are very large, sofeature selection was performed. At first, we used interquartile range (IQR) to filter miRNA or genes based on gene expression levels distribution [14]. All miRNA or genes whose variability is less than 1/5 overall IQR are eliminated. Then, we performed Factor Analysis (ANOVA), and filter feature selection based on random forest [15].

TaLasso online analysis[MF1]

The TaLasso web site (http://talasso.cnb.csic.es/) is an easy tool are needed to get the interactive scoring results provided by TaLasso algorithm [16]. TaLasso is applied to two datasets with matched miRNA and mRNA expression data. Expression data must be tab delimited and have the sample names in the first row and ensemble gene IDs of miRBase mature miRNA names in the first column. TaLasso uses as initial putative targets the union of several sequence databases (miRWalkrelease, miRGen, miRBase, miRanda, TarBase and miRecords) [17].

We converted names of mRNA and miRNA which were screening from expression data into ensemble names. And then, we deleted without ensemble name data and input mRNA and miRNA expression profile data into TaLasso. The putative target genes the unions of TarBase, miRecords and miRWalk were stored.

Functional enrichment analysis

DAVID (the Database for Annotation, Visualization and Integrated Discovery) [18] is a gene functional enrichment analysis tool for investigators to understand biological meaning. Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis were conducted with DAVID. The hypergeometric distribution arithmetic[19] was used to identify over-represented GO categories or KEGG pathways.[MF2] The p-value less than 0.05 was used as the cut-off criterion for the gene enrichment analysis.

Construction of the miRNA-mRNA regulatory network

The integrated miRNA–mRNA regulatory network was constructed by using Cytoscape software [20], which is a software for visualizing complex networks and integrating these networks with any type of attribute data.

Transcription factor (TF) and cancer genes analysis

Target genes associated with transcription factors (TFs) were selected from miRNA–mRNA regulatory network. The cancer-promoting genes were extracted from Tumor Associated Genes (TAG) database, and tumor suppressor genes were extracted from TAG database and TS database.


Feature miRNAs and genes selection

Total 25 feature miRNAs were selected, including hsa-miR-338-5p, hsa-miR-193a-5p, hsa-miR-216b, hsa-miR-204, hsa-miR-21*, hsa-miR-887, hsa-miR-323-3p, hsa-miR-887, hsa-miR-1294 and so on.

Total 1143 feature mRNAs were selected, including MYO5C, SALL4, PBX1, LAMC2, FOS, CHD1L, CCL28, NNT, NROB1 and so on.

TaLasso online analysis

To putative target miRNAs and genes, 21 miRNAs and 361 mRNAs were gained. Total 596 unique miRNA-target gene pairs were used to construct a subsequent regulatory network.

Functional enrichment analysis

GO enrichment analysis was carried out for all target genes. The result showed that many immune response related functions and biological processes were significantly enriched in target genes, regardless of GO terms categories (CC: cellular component; and BP: biological process) (Table 1). The most significant terms in each of these two these GO categories were: extracellular region in CC (p = 0.004191) and embryonic skeletal system development in BP (p = 0.004742).

We used the DAVID to identify the significant KEGG pathways related with the DEGs. The p-value less than 0.05 was chosen as the cut-off criteria. No significant KEGG pathway term was found in this study.

Construction of the miRNA-mRNA regulatory network

A miRNA-mRNA regulatory network was drawn in Figure 1. The degree of each miRNA in the network was then calculated and the top six ones were[MF3] hsa-miR-338-5p (42), hsa-miR-193a-5p (38), hsa-miR-216b (37), hsa-miR-887 (35), hsa-miR-204 (35) and hsa-miR-21* (35). These miRNAs might be of great importance in the whole network.

Transcription factor (TF) and cancer genes analysis

In this study, 15 target genes were transcription factors such as TAF1B, SIX1, RORC, PPARG, PBX1, MAF, HOXD9, HOXB8, HOXB4, HOXB3, HNF4G, GTF2E2, FOXA3, CLOCK and BARX2. We detected that five target genes (SALL4, PBX1, LAMC2, FOS and CHD1L) were cancer-promoting genes, 14 target genes (UCHL1, SLC39A1, SCGB3A1, RAP1GAP, PTPN6, MSMB,LTF, HINT1, DUSP22, DMBT1, DEFB1, CDKN1C, CADM3 and BAI2) were tumor suppressor genes, and six target genes (RAD54B, MFGE8, MAF, LAMP3, EMP1 and AFF4) were uncertain to the development of cancer.[MF4]


CIN is the leading cause of death among gynecological malignancies and represents the second-leading cause of cancer-related deaths in women worldwide [21]. Although, some genes have been reported in the progression of CIN or cervical cancer there is lack of a detailed molecular pathogenesis mechanism. In this study, we identified the feature miRNAs and mRNAs[MF5] between normal cervix samples and CIN III samples using bioinformatics analysis, total 25 feature miRNAs and 1143 feature mRNA were identified. TF analysis result showed 15 transcription factors are involved in regulation of the CIN. In these factors, PBX1 and LAMC2 are known to act as tumor suppressors for the cervical cancer. From the result of miRNA–mRNA regulatory network construction in CIN, we could find that many miRNAs have been linked. The miR-338-5p, miR-193a-5p and miR-216b are hub nodes in this regulatory network.

PBX1 (pre-B-cell leukemia transcription factor 1) encodes a nuclear protein that belongs to the PBX homeobox family of transcriptional factors. PBX was a cofactor for HOX-class homeobox proteins [22]. Previous studies have shown that HOX and PBX genes are involved in oncogenic processes, such as chromatin binding, cell cycle control, proliferation, apoptosis, angiogenesis and cell–cell communications [23-26]. Richard et al., reported that disrupting the interaction between HOX proteins and their co-factor PBX induces apoptosis in SK-OV3 cells and retards tumour growth in vivo [22]. In this study, PBX1 is a feature gene and cancer-promoting gene, suggesting this gene is possibly involved in oncogenic processes in the CIN. Besides, the miRNA-target genes regulatory network result shows that PBX1 regulates by miR-193a-5p in CIN and miR-193a-5p is the second hub node in our network[MF6]. Chen et al., reported that miR-193b (miR-193b is part of the miR-193 family, together with miR-193a-5p) represses cell proliferation and regulates cyclin D1 (CCND1) expression in melanoma [27]. In addition, miR-193 regulates cell growth through the transforming growth factor-β(TGF-β) pathway by regulating Smad3 in glioma [28]. So we suggest that miR-193a-5p may control cell cycle in CIN by regulating PBX1.

LAMC2 (laminin subunit gamma-2) belongs to the laminin family which is an epithelial basement membrane protein. It has been involved in a wide variety of biological processes including cell adhesion, differentiation, migration and tumor invasion [29-31]. In our results, LAMC2 is a feature gene in CIN III compared to normal. Immunohistochemical analysis revealed that LAMC2 protein is highly expressed in carcinomas of the cervix [32] and LAMC2 is a marker to predict the risk of progression of CIN lesions [33].

MicroRNA (miRNA) is an epigenetic factor that regulates cell proliferation, tumor cell growth, cancer formation, and metastasis by regulating tumor suppressor genes or oncogenes [34]. Other studies supported the tumor suppressive activity of miR-338 through PTEN-AKT signaling in gastric cancer cells [35], as well as its role as suppressor of the Smoothened-independent signaling pathways [36]. MiR-216b suppresses tumor growth and invasion by targeting KRAS in nasopharyngel carcinoma [37]. Kim et al., showed that miR-216b promote cellular senescence through the p53–p21Cip1/WAF1pathway in colon cancer HCT116 cells [38]. Thus, those suggest that miRNAs are positively correlated with cancer formation, and metastasis [34], and alterations of these might sustain CIN development.

In conclusion, we have analysed the feature miRNAs and genes of CIN III using bioinformatics analysis and found that PBX1 and LAMC2 may be candidate genes to target the pathogenesis of CIN. MiR-338 and miR-216 may be the key miRNAs in CIN development. They may be used to detect cervical cancer at an early stage. However, further experiments are still needed to confirm our result.



[MF3]参考Screening of Hub Genes and Pathways in Colorectal

Cancer with Microarray Technology




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