Gene expression signatures that can discriminate oral leukoplakia subtypes and squamous cell carcinoma

Department of Biochemistry II, National Defense Medical College, 3-2 Namiki, Tokorozawa-shi 359-8513, Japan.
Oral Oncology (Impact Factor: 3.61). 06/2007; 43(5):455-62. DOI: 10.1016/j.oraloncology.2006.04.012
Source: PubMed


The purpose of this study is to generate a classifier for oral squamous cell carcinoma (OSCC) and leukoplakias (LPs), and evaluate its diagnostic potential. In order to identify marker gene candidates, differential gene expression between LPs and OSCCs were examined by cDNA microarray. The expression of 118 marker gene candidates was further evaluated by quantitative reverse transcription-PCR (QRT-PCR) analyses of 27 OSCC and 19 LP tissues. We identified 12 up-regulated and 15 down-regulated marker genes in OSCCs compared to LPs. Using Fisher's linear discriminant analysis (LDA), we demonstrated that 11-gene predictors among this novel marker set could best distinguish OSCCs from LPs (>97% accuracy), whereas a further seven of these gene predictors could be utilized to distinguish higher grade (higher than moderate) from lower grade (lower than mild) dysplasias (>95% accuracy). These predictor gene sets provide multigene classifiers for the diagnosis of pre-cancerous to cancerous transition of oral malignancy.

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Available from: Masanobu Shindoh,
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    • "Recently, with the emergency of microarray technology that can monitor thousands of genes simultaneously, gene biomarkers are being detected for oral cancers. For example, Saintigny et al. [2] defined a 29-transcripts signature while Kondoh et al. [3] defined another 11-genes signature that can help separate oral cancers developed from OLKs from normal OLKs. Despite the good discrimination capacity of the transcript signature, few of the genes in the signature have functional relationships which make it difficult to understand the malignant transformation of oral leukoplakia. "
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    ABSTRACT: 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.
    06/2015; 2015:1-10. DOI:10.1155/2015/841956
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    • "Before proceeding to LDA, the feature selection step was conducted to reduce the number of genes, because classical LDA requires the total scatter matrix to be nonsingular, while the matrix can be singular when the sample size (149) does not exceed the number of features (genes) (more than 30,000) [27], and tends to overfit and become less interpretable in the presence of many irrelevant and/or redundant features [28]. Based on the previous reports on microarray data analysis [29] [30], we selected only the genes that were up-regulated (fc > 2 and p < 0.05) or down-regulated (fc < 0.5 and p < 0.05) in the groups with increased or decreased liver weight when compared to the not-increased or not-decreased groups, respectively. "
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    Toxicology Reports 11/2014; 1. DOI:10.1016/j.toxrep.2014.10.014
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    • "It has been found to control IFN-b stimulated genes which regulate a group of immune response related genes.57 Dysregulation of USP18 was found in OPMLs in two microarray studies.13,15 Another study found that USP18 was significantly over-expressed in OSCC compared to normal controls.25 "
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    ABSTRACT: Early and accurate diagnosis of oral potentially malignant lesions (OPML) is of critical importance in preventing malignant transformation. Although histopathological interpretation of the degree of epithelial dysplasia is considered the gold standard for diagnosis, this method is subjective and lacks sensitivity. Therefore, many attempts have been made to identify objective molecular biomarkers to improve diagnosis. Microarray technology has the advantage of screening the expression of the whole genome making it one of the best tools for searching for novel biomarkers. However, microarray studies of OPMLs are limited, and no review has been published to highlight and compare their findings. In this paper, we systematically review all studies that have incorporated microarray analyses in the investigation of gene profile alterations in OPMLs and suggest a set of commonly dysregulated genes across multiple gene expression profile studies. This list of common genes may help focus selection of markers for further analysis regarding their importance in the diagnosis and prognosis of OPMLs.
    Clinical Medicine Insights: Oncology 10/2013; 7:279-290. DOI:10.4137/CMO.S12950
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