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.
    Full-text · Article · Jun 2015
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    • "Additionally, because the patient samples analyzed in these studies567were obtained from a variety of sites in the human oral cavity, including the tongue, palate, lower/upper gingiva, floor of mouth, buccal mucosa, and sinus, the results may not reflect the gene expression profiles at specific sites. These studies567used cDNA microarrays that have disadvantages when compared with RNA-seq technology. Microarrays have higher background noise, limited specificity and dynamic range for quantifying gene expression levels, and lack of the ability to distinguish different isoforms[8]. "
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    ABSTRACT: We compared the changes in global gene expression between an early stage (the termination of the carcinogen treatment and prior to the appearance of frank tumors) and a late stage (frank squamous cell carcinoma (SCC)) of tongue carcinogenesis induced by the carcinogen 4-nitroquinoline 1-oxide (4-NQO) in a mouse model of human oral cavity and esophageal squamous cell carcinoma. Gene ontology and pathway analyses show that increases in "cell cycle progression" and "degradation of basement membrane and ECM pathways" are early events during SCC carcinogenesis and that changes in these pathways are even greater in the actual tumors. Myc, NFκB complex (NFKB1/RELA), and FOS transcription networks are the major transcriptional networks induced in early stage tongue carcinogenesis. Decreases in metabolism pathways, such as in "tricarboxylic acid cycle" and "oxidative phosphorylation", occurred only in the squamous cell carcinomas and not in the early stages of carcinogenesis. We detected increases in ALDH1A3, PTGS2, and KRT1 transcripts in both the early and late stages of carcinogenesis. The identification of the transcripts and pathways that change at an early stage of carcinogenesis provides potentially useful information for early diagnosis and for prevention strategies for human tongue squamous cell carcinomas.
    Preview · Article · Jun 2015 · Oncotarget
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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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    ABSTRACT: While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the Class Association Rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.
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