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Identification of Gene and Microrna Signatures for Oral Cancer Developed from Oral Leukoplakia

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Identification of Gene and Microrna Signatures for Oral Cancer Developed from Oral Leukoplakia Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 841956, 10 pages http://dx.doi.org/10.1155/2015/841956 Research Article Identification of Gene and MicroRNA Signatures for Oral Cancer Developed from Oral Leukoplakia Guanghui Zhu,1 Yuan He,2 Shaofang Yang,2 Beimin Chen,3 Min Zhou,2 and Xin-Jian Xu1 1 Department of Mathematics, Shanghai University, Shanghai 200444, China 2Laboratory of Oral Biomedical Science and Translational Medicine, School of Stomatology, Tongji University, Middle Yanchang Road 399, Shanghai 200072, China 3Shanghai Tenth People’s Hospital, Middle Yanchang Road 30, Shanghai 200072, China Correspondence should be addressed to Xin-Jian Xu; [email protected] Received 26 July 2014; Accepted 14 October 2014 Academic Editor: Hao-Teng Chang Copyright © 2015 Guanghui Zhu 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. In clinic, oral leukoplakia (OLK) may develop into oral cancer. However, the mechanism underlying this transformation is still unclear. In this work, we present a new pipeline to identify oral cancer related genes and microRNAs (miRNAs) by integrating both gene and miRNA expression profiles. In particular, we find some network modules as well as their miRNA regulators that play important roles in the development of OLK to oral cancer. Among these network modules, 91.67% of genes and 37.5% of miRNAs have been previously reported to be related to oral cancer in literature. The promising results demonstrate the effectiveness and efficiency of our proposed approach. 1. Introduction interaction (PPI) network and gene expression profiles, we detected the network modules that are associated with oral Oral leukoplakia (OLK) is one of the most common malig- cancer development. In particular, we identified microRNAs nant disorders of the oral mucosa. There are about 2% to (miRNAs) that regulate the oral cancer associated modules. 3% of OLK cases that develop into oral cancers annually [1]. Both the genes from our identified network modules and Therefore, the early diagnosis of the risk of OLKs developing those miRNA regulators are found to be indeed related to into oral cancers can help prevent the disease process with the development of oral cancers, indicating the important timely and effective intervention. Unfortunately, it is still roles of these modules and their miRNA regulators in the unclear how the OLKs develop into oral cancer until now. pathogenesis of oral cancer. Recently, with the emergency of microarray technology that can monitor thousands of genes simultaneously, gene biomarkers are being detected for oral cancers. For example, 2. Materials and Methods Saintigny et al. [2] defined a 29-transcripts signature while Kondoh et al. [3] defined another 11-genes signature that Figure 1 depicts our proposed pipeline to identify networks can help separate oral cancers developed from OLKs from modulesaswellastheirmiRNAregulatorsthatplayimpor- normal OLKs. Despite the good discrimination capacity of tant roles in the development of oral cancer. the transcript signature, few of the genes in the signature have functional relationships which make it difficult to understand 2.1. Gene Expression. Two independent datasets (GSE33299, the malignant transformation of oral leukoplakia. GSE26549) were downloaded from the NCBI Gene Expres- In this work, we developed a novel pipeline to detect genes sion Omnibus (GEO) [4]. The GSE33299 [5]datasetconsists that play important roles in the development of oral cancer of miRNA expression profiles measured in three different from a systematic perspective. Based on the protein-protein tissues, including normal mucousal tissue (5 samples), OLK 2 BioMed Research International Gene expression profiles miRNA expression profiles (GSE26549) (GSE33299) (35 OPL patients with oral (5 malignant transformed oral cancer development, 51 OPL leukoplakia tissues, 20 patients without oral cancer Human PPI network containing 35387 development) oral leukoplakia tissues) PPIs and 9077 proteins Module detection Differential analysis ··· ··· 1444 differentially expressed 439 ··· ··· differentially expressed n=3 n=4 n=10 n=11 genes miRNAs 706 modules were detected Significantly oral cancer related modules with permutation test 13 oral cancer associated miRNA-module interaction network Known miRNA-gene regulation relationships modules 21 hsa-miR-423-3p hsa-miR- hsa-miR-323-3p SFRP1 PCBP2 PABPC1 WNT4 FZD6 hsa-miR-491-3p PCBP1 HNRNPD hsa-miR-525-5p HIRIP3 HIST2H2BE RAP1A PRKACA GNG4 CSNK2A1 hsa-miR-549 HIRA BRAF RAF1 GNB2 SHC1 NCL TOP1 PTPN2 STAT1 CBL PTPN11 DPM2 SRC TP53 JAK2 hsa-miR-499-5p DPM1 PDGFRB STAT3 SNX4 STAT5A DPM3 EGFR HES1 SNX1 STAT5B SIRT1 INSR POU2F1NR3C1 HEY2 ACVR1B SNX2 PDGFRA NCOR2 RARA ACVR2B LEPR Gene SNX6 LRP1 SKIL Gene shared by different modules SNX17 hsa-miR-205 miRNA Figure 1: Schematic illustration of the pipeline to construct oral cancer associated network and detect key genes. BioMed Research International 3 tissue (20 samples) and malignant transformed OLK tis- MAOCs. In the detail, we first defined the scaled connectivity sue (5). The gene expression data were preprocessed with for the th gene as follows: background subtraction and normalization using the global lowess regression model, and the missing expression values = , (2) in the data were generated with Impute package in R [6]. max The GSE26549 [2] dataset contains gene expression profiles where the is the degree of gene , max denotes the measured in 35 OLK patients who developed oral cancer maximum degree of the genes in the MAOCs. Subsequently, in follow-up time and 51 OLK patients that develop to the genes that have larger than 0.9 were regarded as hub oral cancers. All the expression data were normalized with genes hereinafter. quantile normalization and the robust multi-array average Except for the hub genes, there are some genes that link (RMA) approach. Specifically, the expression value of the distinctmodules,whichwerealsoassumedtobeimportant gene associated with multiple probes was calculated as the since they bridge the signal flows between distinct modules. average expression value of all related probes. In particular, we grouped these genes into two categories: (1) genes located in one module and interact with genes from 2.2. Identification of Differentially Expressed Genes and miR- other modules; (2) genes outside of MAOCs but interact with NAs. The miRNAs that are differentially expressed between genes from other modules. Especially, for each of the above OLKs and malignant transformed OLKs were detected by identified genes, the connecting score CS for the th gene was student’s -test with -value cutoff of 0.01. As a result, 439 defined as the number of MAOCs that this gene links to. differentially expressed miRNAs (DEmiRs) were detected. Similarly, 1444 differentially expressed genes (DEGs) were 2.5. Regulations between miRNAs and Genes. Target genes detected by comparing their expression in OLK patients with of miRNAs were predicted through utilizing tools including or without oral cancer consequence with the help of student’s PicTar [9], miRanda [10], MicroT [11]andTargetScan[12]. -test (-value cutoff of 0.01). Target genes of each miRNA were kept only if they were predicted by at least two tools. Target genes of each miRNA 2.3. Identification of Modules Associated with the Development were extended with experimentally determined miRNA tar- of Oral Cancer. The human PPIs consist of 35387 PPIs among get genes deposited in Tarbase [13]. A set of 23336 miRNA- 9077 proteins (self-interactions excluded) were collected gene interactions involving 548 miRNAs and 6797 genes was from the HPRD database [7]. The network modules were obtained. detected from the PPI network by utilizing CFinder [8]with default parameters. As a result, there are in total 706 modules 2.6.IdentificationofmiRNAsRegulatingMAOCs. Based on that were identified. For each module, we defined a score miRNA-gene regulations, given one miRNA, a module will be to measure its relevance to the development of oral cancer as regarded to be regulated by this miRNA if its target genes are follows: significantly enriched in this module, where the enrichment analysis was conducted with Fisher exact test with -value ∑ cutoffof0.01.Asaresult,foreachDEmiR,weidentifiedthe = =1 , (1) modules that can be regulated by this miRNA. where denotes the number of genes in the module, 3. Results denotes the -score of gene obtained with student’s - 3.1. Network Modules Associated with Oral Cancer. In the 13 test analysis on the gene expression data between OLKs MAOCs we identified, there are in total 54 genes involved, with or without oral cancer. Moreover, to control the false among which 38.89% are DEGs (more details see Section 2). discovery rate, the -value for each module was defined as Figure 2 shows the PPI network consists of proteins involved the probability that this module was observed by chance. In in the 13 modules, where the interactions between proteins particular, the same number of genes as that in the module were obtained from the HPRD database, where distinct was randomly picked up and the score for this gene set was modules were marked with different colors while those genes calculated as described in (1). This procedure was repeated for occurring in more than two modules were marked in red. 10000 times, and the frequency
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